System and method for analyzing power quality events in an electrical system

Abstract

A method and system for analyzing power quality events in an electrical system, the method comprising: processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices to generate or update a plurality of dynamic tolerance curves, wherein each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a corresponding metering point in a plurality of metering points in the electrical system; and selectively aggregating power quality data from the plurality of dynamic tolerance curves; analyzing power quality events in the electrical system based on the selectively aggregated power quality data; and adjusting or controlling one or more parameters, processes, conditions, or loads associated with the electrical system in response to the analyzed power quality events.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a divisional application of an invention patent application with an application date of July 8, 2019, application number 201910609586.4, and invention name “System and method for analyzing power quality events in electrical systems”.

[0003] This application claims the benefit of and priority to U.S. Provisional Application No. 62 / 694,791 filed on July 6, 2018, U.S. Provisional Application No. 62 / 770,730 filed on November 21, 2018, U.S. Provisional Application No. 62 / 770,732 filed on November 21, 2018, U.S. Provisional Application No. 62 / 770,737 filed on November 21, 2018, U.S. Provisional Application No. 62 / 770,741 filed on November 21, 2018, and U.S. Provisional Application No. 62 / 785,424 filed on December 27, 2018, the entire contents of which are incorporated herein by reference. Technical Field

[0004] The present disclosure relates generally to power quality issues and, more particularly, to systems and methods for analyzing power quality issues or events in an electrical system. Background Art

[0005] It is well known that power quality issues are one of the most significant and costly impacts on electrical systems (sometimes referred to as “grids”). According to the Leonardo Power Quality Initiative, poor power quality is estimated to cost the European economy up to €150 billion per year. 1 In addition, according to the Electric Power Research Institute (EPRI), the U.S. economy suffers losses of $119 billion to $188 billion per year. 2 Perhaps the most important statistic is that EPRI found that 80% of power quality disturbances are generated within a single facility. An exemplary economic model summarizes the total costs associated with power quality events as follows:

[0006] Total loss = production loss + restart loss + product / material loss + equipment loss + third-party costs + other miscellaneous costs 3

[0007] Other miscellaneous costs associated with power quality issues may include intangible losses, such as damaged reputation with customers and suppliers, or more direct losses, such as depreciation of credit ratings and stock prices. Summary of the invention

[0008] Systems and methods related to analyzing power quality issues or events in an electrical system are described herein. For example, the electrical system may be associated with at least one load, process, building, facility, vessel, aircraft, or other type of structure. In one aspect of the present disclosure, a method for analyzing power quality events in an electrical system includes processing electrical measurement data derived from or from energy-related signals captured by multiple metering devices (e.g., intelligent electronic devices (IEDs)) in the electrical system to generate or update multiple dynamic tolerance curves. In some embodiments, each of the multiple dynamic tolerance curves characterizes a response characteristic of the electrical system at a corresponding metering point in a plurality of metering points in the electrical system. The method also includes selectively aggregating power quality data from the multiple dynamic tolerance curves to analyze power quality events in the electrical system.

[0009] In some embodiments, the method may be implemented using one or more of a plurality of metering devices. In addition, in some embodiments, the method may be implemented away from a plurality of metering devices, for example, in a gateway, a cloud-based system, on-site software, a remote server, etc. (which may be alternatively referred to herein as a "head-end" system). In some embodiments, a plurality of metering devices may be coupled to measure an electrical signal, receive electrical measurement data from the electrical signal at an input, and be configured to generate at least one or more outputs. The output may be used to analyze power quality events in an electrical system. Examples of a plurality of metering devices may include a smart meter, a power quality meter, and / or another metering device (or devices). For example, a plurality of metering devices may include a circuit breaker, a relay, a power quality correction device, an uninterruptible power supply (UPS), a filter, and / or a variable speed drive (VSD). In addition, in some embodiments, a plurality of metering devices may include at least one virtual meter.

[0010] In an embodiment, the above method is generally applicable to non-periodic power quality problems or events, such as transients, short-term RMS changes (e.g., sags, swells, momentary interruptions, temporary interruptions, etc.), and some long-term RMS changes (e.g., possibly up to about 1-5 minutes).

[0011] Examples of electrical measurement data that may be captured by the plurality of metering devices may include at least one of continuously measured voltage and current signals and parameters and characteristics derived therefrom. Electrical parameters and events may be derived, for example, from analyzing energy-related signals (e.g., active power, reactive power, apparent power, harmonic distortion, phase imbalance, frequency, voltage / current transients, voltage sags, voltage swells, etc.). More specifically, the plurality of metering devices may assess the magnitude, duration, load impact, impact recovery time, non-productive renewable energy consumed, CO2 emitted from renewable energy, costs associated with the event, etc. of a power quality event.

[0012] It should be understood that there are types of power quality events and that there are certain characteristics of these types of power quality events, for example, as further described in the table from IEEE Standard 1159-2009 (known technology) provided below. Voltage sag is an example type of power quality event. For example, significant characteristics of a voltage sag event are the magnitude of the voltage sag and its duration. As used herein, examples of power quality events can include voltage events that affect phase, neutral, and / or ground conductors and / or paths.

[0013] In some embodiments, the above method and other methods (and systems) described below may include one or more of the following features, alone or in combination with other features. In some embodiments, the energy-related signals captured by the plurality of metering devices include at least one of the following: voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage. In embodiments, for example, the energy-related signals may include (or utilize) substantially any electrical parameter derived from the voltage and current signals (including the voltage and current themselves).

[0014] In some embodiments, processing electrical measurement data derived from or captured by a plurality of metering devices in an electrical system to generate or update a plurality of dynamic tolerance curves includes: processing the electrical measurement data derived from or captured by the plurality of metering devices at a first initial time to generate a dynamic tolerance curve for each respective metering point in the plurality of metering points in the electrical system based on a response characteristic of the electrical system at each respective metering point at the first initial time. Processing the electrical measurement data derived from or captured by the plurality of metering devices at subsequent times after the first initial time to optimize the dynamic tolerance curve for each respective metering point based on the response characteristic of the electrical system at each respective metering point at the subsequent time may continue.

[0015] In some embodiments, degradation or improvement in the sensitivity or resilience of the electrical system to power quality events at each respective metering point may be identified and reported. In one aspect, the degradation or improvement is identified based on the identified change in the dynamic tolerance curve for each respective metering point. Further, in one aspect, the identified degradation or improvement is reported by generating and / or initiating an alert indicating the identified degradation or improvement in the sensitivity or resilience of the electrical system to power quality events, and communicating the alert via at least one of a report, text, email, audible, and screen / display interface. In some embodiments, the alert provides actionable recommendations for responding to the identified degradation or improvement in the sensitivity or resilience of the electrical system to power quality events.

[0016] In some embodiments, selectively aggregating power quality data from a plurality of dynamic tolerance curves to analyze power quality events includes: selectively aggregating power quality data from a plurality of dynamic tolerance curves to generate at least one aggregated dynamic tolerance curve, and analyzing at least one aggregated dynamic tolerance curve to account for power quality events in an electrical system. In one aspect, the power quality data is selectively aggregated based on the location of a plurality of metering points in the electrical system. Additionally, in one aspect, the power quality data is selectively aggregated based on a criticality or sensitivity of the plurality of metering points to power quality events.

[0017] In some embodiments, selectively aggregating power quality data from a plurality of dynamic tolerance curves to analyze power quality events includes determining whether there are any differences between the selectively aggregated power quality data, and requesting additional information from a system user to reconcile the differences. The differences may include, for example, inconsistent naming conventions in the selectively aggregated data.

[0018] In some embodiments, selectively aggregating power quality data from at least one dynamic tolerance curve to account for power quality events includes: determining a relative criticality score for each of the power quality events to a process or application associated with an electrical system. In one aspect, the relative criticality score is based on the impact of the power quality event on the process or application. In one aspect, the impact of the power quality event is related to a tangible or intangible cost to the process or application regarding the power quality event. Further, in one aspect, the impact of the power quality event is related to a relative impact on a load in the electrical system. In some embodiments, the determined relative criticality scores can be used to prioritize responses to the power quality events.

[0019] In some embodiments, based on the extracted information about the power quality event, the power quality event in each of the plurality of dynamic tolerance curves may be marked with relevant and characterizing information. Examples of relevant and characterizing information may include at least one of the following: severity (magnitude), duration, power quality type (e.g., sag, swell, interruption, oscillation transient, pulse transient, etc.), time of occurrence, process(es) involved, location, affected equipment, relative or absolute impact, recovery time, event period or event type, etc. In addition, examples of relevant and characterizing information may include at least one of the following: information about activities occurring before, during, or after the power quality event, such as measured load changes associated with the event, or specific loads or devices turned on or off before, during, or after the event. In some embodiments, information about the power quality event may be extracted from a portion of electrical measurement data acquired before the start time of the power quality event, and from a portion of electrical measurement data acquired after the end of the power quality event. In addition, in some embodiments, information about the power quality event may be extracted from a portion of electrical measurement data acquired during the power quality event.

[0020] In some embodiments, processing electrical measurement data derived from or captured by a plurality of metering devices in an electrical system to generate or update a plurality of dynamic tolerance curves includes: receiving electrical measurement data derived from or captured by a plurality of metering devices in the electrical system during a first period corresponding to a learning period. Based on the electrical measurement data captured during the first period, one or more upper alarm thresholds and one or more lower alarm thresholds may be generated for each of the plurality of dynamic tolerance curves. Electrical measurement data derived from or captured by a plurality of metering devices during a second period corresponding to a normal operation period may be received, and based on the electrical measurement data received during the second period, it may be determined whether at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds needs to be updated. In response to determining that at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds needs to be updated, at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds may be updated.

[0021] In some embodiments, one or more upper alarm thresholds include a threshold value above a nominal voltage or an expected voltage at a point of installation of a corresponding one of a plurality of metering devices in an electrical system. In one aspect, a threshold value above the nominal voltage indicates a transient, a swell, or an overvoltage. In some embodiments, one or more lower alarm thresholds include a threshold value below a nominal voltage or an expected voltage at a point of installation of a corresponding one of a plurality of metering devices in an electrical system. In one aspect, a threshold value below the nominal voltage indicates a sag, an interruption, a notch, or an undervoltage. In some embodiments, each of the one or more upper alarm thresholds and each of the one or more lower alarm thresholds have an associated magnitude and duration.

[0022] In some embodiments, each of a plurality of metering devices in the electrical system is associated with a corresponding one of a plurality of metering points. In addition, in some embodiments, at least one of the plurality of metering devices comprises a virtual meter. In some embodiments, the plurality of dynamic tolerance curves may be selectively displayed in at least one of: a graphical user interface (GUI) of a control system for controlling one or more parameters associated with the electrical system, and a GUI of at least one of the plurality of metering devices.

[0023] In some embodiments, a dynamic tolerance curve according to the present disclosure can alert a user to any (or substantially any) degradation or improvement in the sensitivity or resilience of the electrical system (e.g., if the impact is defined by the time to return to normal). This can be done both on a new calculated optimal model and on a more traditional key performance indicator (KPI), such as the evolution of the load drop percentage.

[0024] Typically, the sensitivity or resilience of an electrical system does not remain at exactly the same level over time. Degradation may occur, but it may also improve due to, for example, normal lifetime operation, changes in equipment, maintenance operations, impact time, etc. Alerts indicating degradation or improvement may be provided in existing real-time systems such as power or industrial / manufacturing SCADA systems, building management systems, power monitoring systems. As another example, a specific report may be published and sent via email or provided online on a mobile application.

[0025] In these cases, actionable recommendations related to specific meters can be sent to users, partners, or service teams to provide them with optional or mandatory actions.

[0026] In another aspect of the present disclosure, a system for analyzing power quality events in an electrical system includes at least one input coupled to at least a plurality of metering devices in the electrical system, and at least one output coupled to at least a plurality of loads monitored by the plurality of metering devices. The system for managing power quality events also includes a processor coupled to receive electrical measurement data derived from or from energy-related signals captured by the plurality of metering devices from at least one system input. The processor may be configured to process the electrical measurement data to generate or update at least one of a plurality of dynamic tolerance curves. In some embodiments, at least one of the plurality of dynamic tolerance curves characterizes and / or depicts a response characteristic of the electrical system at at least a corresponding metering point of a plurality of metering points in the electrical system. The processor may also be configured to selectively aggregate power quality data from the plurality of dynamic tolerance curves, and analyze power quality events in the electrical system based on the selectively aggregated power quality data. The processor may also be configured to adjust at least one parameter associated with one or more of the plurality of loads in response to the analyzed power quality event. In some embodiments, the at least one parameter is adjusted in response to a control signal generated at at least one system output and provided to one or more of the plurality of loads.

[0027] In some embodiments, the energy-related signals captured by the plurality of metering devices include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage.

[0028] In some embodiments, the metering devices (e.g., IEDs) and loads of the systems and methods described above and below are installed, located, or derived from different corresponding locations (i.e., multiple locations) or metering points in the electrical system. For example, a particular IED (e.g., a second IED) may be upstream of another IED (e.g., a third IED) in the electrical system, and downstream of yet another IED (e.g., the first IED) in the electrical system.

[0029] As used herein, the terms "upstream" and "downstream" are used to refer to electrical locations within an electrical system. More specifically, the electrical locations "upstream" and "downstream" are relative to the electrical location of the IED that collects data and provides the information. For example, in an electrical system including multiple IEDs, one or more IEDs may be positioned (or installed) at an electrical location upstream relative to one or more other IEDs in the electrical system, and one or more IEDs may be positioned (or installed) at an electrical location downstream relative to one or more further IEDs in the electrical system. A first IED or load on an electrical circuit that is upstream of a second IED or load may, for example, be located closer electrically to an input or source (e.g., a utility feed) of the electrical system than the second IED or load. Conversely, a first IED or load on an electrical circuit that is downstream of a second IED or load may be located closer electrically to an end or terminal of the electrical system than another IED.

[0030] In an embodiment, a first IED or load electrically connected in parallel (e.g., on an electrical circuit) with a second IED or load may be considered to be "electrically" upstream of the second IED or load, and vice versa. In an embodiment, (multiple) algorithms for determining the direction (i.e., upstream or downstream) of a power quality event are located (or stored) in an IED, cloud, field software, gateway, etc. As an example, an IED may record voltage and current phase information of an electrical event (e.g., by sampling corresponding signals) and communicate the information to a cloud-based system. The cloud-based system may then analyze the voltage and current phase information (e.g., instantaneous, root-mean-square (rms), waveform, and / or other electrical characteristics) to determine whether the source of the voltage event is electrically upstream or downstream of where the IED is electrically coupled to the electrical system (or network).

[0031] As used herein, a load loss (sometimes also referred to as a "loss of load") refers to the unintended, unplanned, and / or unintentional removal of one or more loads from an electrical system. In the present application, a voltage disturbance or event and the subsequent loss of load may be the result of one or more external influences (e.g., faults, etc.) on the electrical system or the normal or abnormal operation of loads, protection devices, mitigation devices, and / or other equipment intentionally connected to the electrical system. Load losses may be indicated by measured parameters such as voltage, current, power, energy, harmonic distortion, imbalance, etc., or they may be indicated by discrete (digital) and / or analog input-output (I / O) signals originating from equipment directly and / or indirectly connected to the electrical system. For example, circuit breakers typically provide an output indication (e.g., open / closed, off / on, etc.) of their current position to communicate their operating status.

[0032] In some embodiments, electrical measurement data from energy-related signals captured by a plurality of metering devices may be processed on one or more of the plurality of metering devices, or in field software, a cloud-based application, a gateway, or the like, to characterize a power quality event in an electrical system. Further, in some embodiments, the electrical measurement data may be processed on a system for quantifying power quality events in an electrical system, such as a control system associated with the electrical system. For example, the control system may be used to control one or more parameters associated with the electrical system. In an embodiment, identifying a power quality event may include identifying: (a) a type of power quality event, (b) a magnitude of an abnormal power quality event, (c) a duration of the power quality event, and / or (d) a location of the power quality event in the electrical system. In an embodiment, the power quality event type may include one of a voltage sag, a voltage swell, a voltage interruption, and a voltage transient. Further, in an embodiment, the location of the power quality event may be derived from voltage and current signals measured by an IED and associated with an abnormal voltage condition.

[0033] As described above, a voltage event is an example type of power quality event. For example, a power quality event may include at least one of a voltage sag, a voltage swell, and a voltage transient. For example, according to IEEE Standard 1159-2009, a voltage sag is a drop in the root mean square voltage or current at power frequency to between 0.1 and 0.9 per unit (pu) for a duration of 0.5 cycle to 1 minute. Typical values ​​are 0.1 to 0.9 pu. In addition, according to IEEE Standard 1159-2009, a voltage swell is an increase in the root mean square voltage or current at power frequency for a duration of 0.5 cycle to 1 minute. Below is a table from IEEE Standard 1159-2009 (known technology) that defines various categories and characteristics of electromagnetic phenomena in power systems.

[0034]

[0035]

[0036] It should be understood that the above table is the way in which a standards body (IEEE in this case) defines / characterizes power quality events. It should be understood that there are other standards that define power quality categories / events, such as the International Electrotechnical Commission (IEC), the American National Standards Institute (ANSI), etc., which may have different descriptions or power quality event types, characteristics, and terms. In an embodiment, the power quality event can be a customized power quality event (e.g., defined by a user).

[0037] In some embodiments, electrical measurement data processed to identify power quality events may be captured continuously or semi-continuously by a plurality of metering devices, and a tolerance curve may be dynamically updated in response to power quality events detected (or identified) from the electrical measurement data. For example, a tolerance curve may be initially generated in response to a power quality event identified from electrical measurement data captured at a first time, and may be updated or modified in response to (e.g., including or incorporating) a power quality event identified from electrical measurement data captured at a second time. When an event is captured, the tolerance curve (also sometimes referred to herein as a "dynamic tolerance curve") may be continuously (e.g., dynamically) updated based on the unique response of the electrical system.

[0038] In some embodiments, the tolerance curve may be displayed in a GUI of at least one IED, or in a GUI of a control system for monitoring or controlling one or more parameters associated with an electrical system. In embodiments, the control system may be a meter, an IED, field / head-end software (i.e., a software system), a cloud-based control system, a gateway, a system in which data is routed via Ethernet or some other communication system, and the like. For example, in response to a determined impact (or severity) of a power quality event exceeding a range or threshold, a warning may be displayed in a GUI of the IED, monitoring system, or control system. In some embodiments, the range is a predetermined range, such as a user-configured range. Further, in some embodiments, the range is automatic, such as using thresholds based on standards. Further, in some embodiments, the range is "learned," for example, by starting at a nominal voltage and deriving thresholds when non-impact events occur in the natural course of grid operation.

[0039] The GUI can be configured to display factors that contribute to the power quality event. Furthermore, the GUI can be configured to indicate the location of the power quality event in the electrical system. Furthermore, the GUI can be configured to indicate how a load (or another specific system or equipment in the electrical system) will respond to the power quality event. It should be understood that any amount of information can be displayed in the GUI. As part of the present invention, any electrical parameter, effect on a parameter, I / O state input, I / O output, process impact, recovery time, impact time, affected phases, potential discrete loads affected under a single IED, etc. can be displayed in the GUI. For example, as will be discussed further below, Fig. 20 A simple example combining the percentage of load affected and an indication of the recovery time is shown.

[0040] In an embodiment, the tolerance curve displayed in the GUI does not have a fixed scale, but can (and needs to) automatically scale, for example, to capture or display multiple power quality events. According to aspects of the present disclosure, the beauty of having a dynamic tolerance curve is that it is not constrained to a static curve (e.g., with a fixed scale). For example, briefly referring to Figure 2 (discussed further below), although the y-axis is Figure 2, but it can also be shown as an absolute nominal value (e.g., 120V, 208V, 240V, 277V, 480V, 2400V, 4160V, 7.2kV, 12.47kV, etc.). In this case, automatic scaling is required because different voltage ranges require different scaling of the y-axis. In addition, the x-axis can be scaled in different units (e.g., cycles, seconds, etc.) and / or can have a variable maximum endpoint (e.g., 10 seconds, 1 minute, 5 minutes, 600 cycles, 3600 cycles, 18000 cycles, etc.). In other words, in some embodiments, there is no reason for the GUI to display more than it has to.

[0041] In embodiments, an object of the invention claimed herein is to build customized tolerance curves for discrete locations within a customer's electrical system (e.g., at a given IED) based on the perceived impact on downstream loads. Further, in embodiments, an object of the invention claimed herein is to quantify the time taken to recover from a power quality event. In short, aspects of the invention claimed herein are intended to describe the impact of power quality events, which allows customers to understand their operating parameters and constraints accordingly.

[0042] As used herein, an IED is a computing electronic device optimized to perform a specific function or set of functions. As described above, examples of IEDs include smart meters, power quality meters, and other metering devices. IEDs may also be embedded in variable speed drives (VSDs), uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical devices. IEDs may be used to perform monitoring and control functions in a variety of facilities. These facilities may include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing co-location centers, data centers, distribution networks, and the like. For example, in the case where the IED is an electric power monitoring device, it may be coupled to (or installed in) a distribution system and configured to sense and store data as electrical parameters (e.g., voltage, current, waveform distortion, power, etc.) representing the operating characteristics of the distribution system. Users can analyze these parameters and characteristics to assess potential performance, reliability, or power quality-related issues. An IED may include at least one controller (which, in some IEDs, may be configured to run one or more applications simultaneously, serially, or both simultaneously and serially), firmware, memory, communication interfaces, and connectors that connect the IED to external systems, devices, and / or components of any voltage level, configuration, and / or type (e.g., AC, DC). At least some aspects of the monitoring and control functionality of the IED may be embodied in a computer program accessible to the IED.

[0043] In some embodiments, the term "IED" as used herein may refer to a hierarchy of IEDs operating in parallel and / or in series. For example, an IED may correspond to a hierarchy of energy meters, power meters, and / or other types of resource meters. The hierarchy may include a tree-based hierarchy, such as a binary tree, a tree with one or more child nodes inherited from each parent node or multiple parent nodes, or a combination thereof, wherein each node represents a specific IED. In some cases, the hierarchy of IEDs may share data or hardware resources and may execute shared software.

[0044] Features presented in this disclosure evaluate specific power quality events to characterize their impact on load, restoration time, and other useful or interesting parameters of the electrical system. The scope may include discrete metered points, network zones, and / or the total aggregated electrical system. Novel ideas demonstrating these concepts are also discussed, enabling energy consumers to identify, analyze, mitigate, and manage their grids more efficiently and economically.

[0045] Of the seven recognized power quality categories defined by IEEE 1159-2009, short-term root mean square (rms) variations are generally the most disruptive and have the greatest pervasive economic impact on energy consumers. Short-term rms variations include voltage dips / sags, swells, momentary interruptions, short interruptions, and temporary outages. One example from an Electric Power Research Institute (EPRI) study estimated that the average industrial customer experiences about 66 voltage dips per year. As the trend toward greater industrial reliance on sag-sensitive equipment increases, the impact of these events has also increased.

[0046] The prevalence of voltage sags and the consequence of a growing installed base of sag-sensitive equipment presents many additional opportunities for electrical solution and service providers. The table below illustrates a few example opportunities:

[0047]

[0048] In one aspect of the present disclosure, a method for analyzing power quality events in an electrical system includes: processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices to generate or update a plurality of dynamic tolerance curves, wherein each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a corresponding metering point in a plurality of metering points in the electrical system; and selectively aggregating power quality data from the plurality of dynamic tolerance curves; analyzing power quality events in the electrical system based on the selectively aggregated power quality data; and adjusting or controlling one or more parameters, processes, conditions, or loads associated with the electrical system in response to the analyzed power quality events.

[0049] In another aspect of the present disclosure, a system for analyzing power quality events in an electrical system includes: at least one input coupled to at least a plurality of metering devices in the electrical system; at least one output coupled to at least a plurality of loads monitored by the plurality of metering devices; and a processor coupled to receive electrical measurement data derived from or from energy-related signals captured by the plurality of metering devices from at least one system input, the processor being configured to: process the electrical measurement data to generate or update a plurality of dynamic tolerance curves, wherein each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a corresponding metering point of a plurality of metering points in the electrical system; selectively aggregate power quality data from the plurality of dynamic tolerance curves; analyze power quality events in the electrical system based on the selectively aggregated power quality data; and adjust or control one or more parameters, processes, conditions, or loads associated with the electrical system in response to the analyzed power quality events. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The foregoing features of the present disclosure as well as the present disclosure itself may be more fully understood through the detailed description of the following drawings, in which:

[0051] Figure 1 A graphical view showing several example power quality categories;

[0052] Figure 1A An example electrical system according to an embodiment of the present disclosure is shown;

[0053] Figure 1B An example intelligent electronic device (IED) that may be used in an electrical system according to an embodiment of the present disclosure is shown;

[0054] Figure 2 An example Information Technology Industry (ITI) curve (sometimes also called a "power acceptability curve") is shown;

[0055] Figure 3 An example baseline voltage tolerance curve is shown, which may be an ITI curve (as shown) or some other unique relationship between voltage amplitude and duration of an event;

[0056] Figure 4 An example voltage sag event on a baseline voltage tolerance curve is shown;

[0057] Figure 5 Shown based on Figure 4 The impact of voltage sag events shown on Figure 3 An example of a baseline voltage tolerance curve of suggested changes;

[0058] Figure 6An example dynamically customized and updated voltage tolerance curve is shown;

[0059] Figure 7 A number of examples of impactful and non-impactful voltage dips, swells and transients on the voltage tolerance curve are shown;

[0060] Figure 8 shows dynamically customized and updated voltage tolerance curves for a large number of impactful and non-impactful events;

[0061] Fig. 9 An example three-dimensional (3-D) tolerance-impact curve with load(s) impact is shown;

[0062] Fig.10 shows an example 3-D tolerance-impact curve, where gradient color shading indicates the severity of load impact(s);

[0063] Fig.11 An example 3-D tolerance-impact curve is shown, where sample events indicate the severity of load impact(s);

[0064] Fig.12 An example 3-D tolerance-impact curve with recovery time is shown;

[0065] Fig.13 An example 3-D tolerance-impact curve is shown, where the gradient color shading indicates the length of the recovery time;

[0066] Fig.14 An example 3-D tolerance-impact curve is shown, where a sample event indicates the length of the recovery time;

[0067] Fig.15 Another example 3-D tolerance-impact curve is shown, where a sample event indicates production loss as an economic impact;

[0068] Fig.16 An example simple electrical grid with a fault is shown;

[0069] Fig.16A Another example power grid having a fault is shown;

[0070] Fig.17 An example customized tolerance curve with a large number of influential and non-influential upstream and downstream events is shown;

[0071] Fig.18 An example customized tolerance curve for upstream events with a large number of influential and non-influential decompositions is shown;

[0072] Fig.19 An example customized tolerance curve for downstream events with a large number of influential and non-influential decompositions is shown;

[0073] Fig. 20 shows an example 3-D tolerance-impact curve with load impact, recovery time, and upstream / downstream event sources indicated for a number of events;

[0074] Fig.21 is a graph showing an example progression of costs for mitigating voltage events;

[0075] Fig. 22 Shows Figure 4 Example customized and updated tolerance curve for voltage sag events shown;

[0076] Fig.23 Shown superimposed on Fig. 22 SEMI F47 curve on the graph shown;

[0077] Fig.24 An example of a ride-through benefit of a sag mitigation device in an electrical system is shown, one example being Schneider Electric's

[0078] Fig.25 Examples of a number of potentially avoidable load impact events utilizing sag mitigation equipment are shown;

[0079] Fig.26 Another example of a number of potentially avoidable load impact events utilizing sag mitigation equipment, and their aggregate recovery times, is shown;

[0080] Fig. 27 An example of the predicted impact of installing voltage event mitigation equipment is shown;

[0081] Fig.28 An example showing the practical impact of installing voltage event mitigation equipment;

[0082] Fig.29 An example of a simple electrical system with multiple IEDs is shown;

[0083] Fig.30 shows example recovery timelines for multiple IED types experiencing a voltage event;

[0084] Fig. 30A An example of virtual metering for identifying the impact of voltage events on unmetered loads is shown;

[0085] Fig. 30B An example electrical system according to an embodiment of the present disclosure is shown;

[0086] Figures 30C-30E An example dynamic tolerance curve according to an embodiment of the present disclosure is shown;

[0087] Figures 30F-30I A further example electrical system according to an embodiment of the present disclosure is shown;

[0088] Fig.31 Shows Fig.29 Example fault on a simple electrical system;

[0089] Fig.32 For example, the position of the step-down transformer is shown Fig.29 Example zones for a simple electrical system;

[0090] Fig.33 Shows Fig.29 Example custom zoning configuration for a simple electrical system;

[0091] Fig.34 An example of a simple voltage tolerance curve (sometimes also called a power acceptability curve) is shown;

[0092] Fig.35 Shown in Fig.34 An example voltage sag event is shown on a simple voltage tolerance curve of

[0093] Fig.36 Shown in Fig.35 Example updated voltage tolerance curve after a voltage sag event shown;

[0094] Fig.37 Shows Fig.36 An example second voltage sag event on the voltage tolerance curve shown;

[0095] Fig.38 Shown in Fig.37 The example updated voltage tolerance curve after the second voltage sag event is shown;

[0096] Fig.39 Shows Fig.38 A third example voltage sag event on the voltage tolerance curve shown;

[0097] Fig.40 Shown in Fig.39 An example voltage tolerance curve after a third voltage sag event is shown;

[0098] Fig.41 is a graph showing load vs. time for example measured(s) affecting voltage events;

[0099] Fig.42 is a graph of load vs. time showing several examples of measured(s) impact voltage events;

[0100] Fig.43are graphs showing measured, typical, and expected load vs. time for example voltage events;

[0101] Fig.44 is a graph showing the effect of percentage load vs. time;

[0102] Fig.45 is a flow chart illustrating an example method for managing power quality events (or disturbances) in an electrical system;

[0103] Fig.46 is a flow chart illustrating an example method for quantifying power quality events (or disturbances) in an electrical system;

[0104] Fig.47 is a flow chart illustrating an example method for extended qualified lead generation for power quality;

[0105] Fig.48 is a flow chart illustrating an example method for generating a dynamic tolerance curve for power quality;

[0106] Fig.49 Illustrative waveforms are shown;

[0107] Fig.50 Another illustrative waveform is shown;

[0108] Fig.51 is a flow chart illustrating an example method for characterizing a power quality event in an electrical system;

[0109] Fig.52 is a flow chart illustrating an example method for characterizing the effects of a power quality event on an electrical system;

[0110] Fig.53 is a flow chart illustrating an example method for reducing recovery time from a power quality event in an electrical system, for example, by tracking response characteristics of the electrical system.

[0111] Fig.54 is a flow chart illustrating an example method for analyzing power quality events in an electrical system;

[0112] Fig.55 is a flow chart illustrating an example method for generating a dynamic tolerance curve;

[0113] Fig.55A is a diagram illustrating an example method for moving from a single meter threshold calculation to a meter group threshold calculation;

[0114] Fig.56 is a flow chart illustrating an example method of marking a criticality score, such as during a learning period;

[0115] Fig.57 is a flow chart illustrating another example method of marking a criticality score, such as during a learning period;

[0116] Figures 57A-57K Several example ways in which load loss may be modeled according to embodiments of the present disclosure are shown;

[0117] Fig.58 is a flow chart illustrating an example method of generating a dynamic tolerance curve, such as after a learning period; and

[0118] Fig.59 is a flow chart illustrating an example method of utilizing and combining dynamic tolerance curves. DETAILED DESCRIPTION

[0119] The features and other details of the concepts, systems and techniques for which protection is sought herein will now be described in greater detail. It should be understood that any specific embodiments described herein are shown by way of illustration and are not limitations of the present disclosure and the concepts described herein. The features of the subject matter described herein may be employed in various embodiments without departing from the scope of the concepts for which protection is sought.

[0120] For convenience, some introductory concepts and terms used in this specification are collected here (adopted from IEEE Standard 1159-2009). For example, several of these concepts and terms are Figure 1 It is worth noting that Figure 1 Not all power quality categories are included, such as waveform distortion, unbalance, voltage fluctuations and power frequency deviations.

[0121] As used herein, the term "non-periodic event" is used to describe electrical events that occur non-periodically, randomly, or without specific temporal regularity. For purposes of this article, both short-term root mean square (rms) changes and transients are considered non-periodic events (i.e., notches are considered harmonic phenomena).

[0122] As used herein, the term "instantaneous interruption" is used to describe a 0-10% deviation from a nominal value with a duration of 1 / 2 cycle to 30 cycles.

[0123] As used herein, the term "momentary interruption" is used to describe a 0-10% deviation from a nominal value over a duration of 30 cycles to 3 seconds.

[0124] As used herein, the term "sag" (of which "voltage sag" is an example) is used to describe a deviation of 10-90% from a nominal value, for example, with a duration of 1 / 2 cycle to 1 minute. Figure 1 shown.

[0125] As used herein, the term "short-term RMS variation" is used to describe deviations from a nominal value with durations ranging from 1 / 2 cycle to 1 minute. Subcategories of short-term RMS variation include momentary interruptions, momentary interruptions, temporary interruptions, dips, and swells.

[0126] As used herein, the term "swell" is used to describe a deviation greater than 110% of the nominal value, for example, within a duration of 1 / 2 cycle to 1 minute, such as Figure 1 shown.

[0127] As used herein, the term "temporary interruption" is used to describe a 0-10% deviation from a nominal value for a duration of 3 seconds to 1 minute.

[0128] As used herein, the term "transient" is used to describe a deviation from a nominal value that lasts less than 1 cycle. Subcategories of transients include impulse (unidirectional polarity) and oscillatory (bidirectional polarity) transients.

[0129] In an embodiment, the degree to which short-term RMS changes affect energy consumer facilities depends primarily on four factors:

[0130] 1. The nature and origin of the incident,

[0131] 2. The sensitivity of the load(s) to the event,

[0132] 3. The impact of the event on the process or activity, and

[0133] 4. Cost sensitivity to the event.

[0134] As a result, each customer system, operation, or load may respond differently to a given electrical disturbance. For example, a voltage sag event may significantly impact one customer's operations, while the same voltage sag may have little or no significant impact on another customer's operations. A voltage sag may also affect one part of a customer's electrical system differently than another part of the same electrical system.

[0135] refer to Figure 1A, an example electrical system according to an embodiment of the present disclosure includes one or more loads (here, loads 111, 112, 113, 114, 115) and one or more intelligent electronic devices (IEDs) (here, IEDs 121, 122, 123, 124) capable of sampling, sensing or monitoring one or more parameters associated with the loads (e.g., power monitoring parameters). In an embodiment, the loads 111, 112, 113, 114, 115 and the IEDs 121, 122, 123, 124 may be installed in one or more buildings or other physical locations, or they may be installed on one or more processes and / or loads within a building. These buildings may correspond to, for example, commercial, industrial, or institutional buildings.

[0136] like Figure 1A As shown, IEDs 121, 122, 123, 124 are each coupled to one or more of loads 111, 112, 113, 114, 115 (in some embodiments, loads 111, 112, 113, 114, 115 may be located "upstream" or "downstream" of the IEDs). Loads 111, 112, 113, 114, 115 may include, for example, machinery or devices associated with a specific application (e.g., an industrial application), multiple applications, and / or (multiple) processes. For example, machinery may include electrical or electronic equipment. The machine may also include control devices and / or auxiliary equipment associated with the equipment.

[0137] In embodiments, the IEDs 121, 122, 123, 124 may monitor and, in some embodiments, analyze parameters (e.g., energy-related parameters) associated with the loads 111, 112, 113, 114, 115 to which they are coupled. In some embodiments, the IEDs 121, 122, 123, 124 may also be embedded in the loads 111, 112, 113, 114, 115. According to various aspects, one or more of the IEDs 121, 122, 123, 124 can be configured to monitor a utility power source, including surge protective devices (SPDs), trip units, active filters, lighting, IT equipment, motors and / or transformers, which are some examples of loads 111, 112, 113, 114, 115, and the IEDs 121, 122, 123, 124 can detect ground faults, voltage sags, voltage swells, momentary interruptions and ringing transients, as well as fan faults, temperatures, arc faults, phase-to-phase faults, shorted windings, blown fuses, and harmonic distortion, which are some example parameters that may be associated with the loads 111, 112, 113, 114, 115. The IEDs 121, 122, 123, 124 may also monitor equipment, such as generators, including input / output (I / O), protection relays, battery chargers, and sensors (eg, water, air, gas, steam, liquid level, accelerometers, flow rate, pressure, etc.).

[0138] According to another aspect, the IEDs 121, 122, 123, 124 may detect overvoltage and undervoltage conditions, as well as other parameters such as temperature, including ambient temperature. According to yet another aspect, the IEDs 121, 122, 123, 124 may provide indications of monitored parameters and detected conditions, which may be used to control the loads 111, 112, 113, 114, 115 and other equipment in the electrical system in which the loads 111, 112, 113, 114 and the IEDs 121, 122, 123, 124 are installed. The IEDs 121, 122, 123, 124 may perform a variety of other monitoring and / or control functions, and the aspects and embodiments disclosed herein are not limited to the IEDs 121, 122, 123, 124 operating according to the above examples.

[0139] It should be understood that the IEDs 121, 122, 123, 124 may take various forms and may each have an associated complexity (or set of functional capabilities and / or features). For example, the IED 121 may correspond to a "basic" IED, the IED 122 may correspond to an "intermediate" IED, and the IED 123 may correspond to an "advanced" IED. In such an embodiment, the intermediate IED 122 may have more functionality (e.g., energy measurement features and / or capabilities) than the basic IED 121, and the advanced IED 123 may have more functionality and / or features than the intermediate IED 122. For example, in an embodiment, IED 121 (e.g., an IED with basic capabilities and / or features) may be able to monitor instantaneous voltage, current energy, demand, power factor, average, maximum, instantaneous power, and / or long-term RMS variation, and IED 123 (e.g., an IED with advanced capabilities) may be able to monitor additional parameters, such as voltage transients, voltage fluctuations, frequency slew rates, harmonic power flows and discrete harmonic components, all at higher sampling rates, etc. It should be understood that this example is for illustrative purposes only, and that likewise in some embodiments, an IED with basic capabilities may be able to monitor one or more of the above energy measurement parameters indicated as being associated with an IED with advanced capabilities. It should also be understood that in some embodiments, IEDs 121, 122, 123, 124 each have independent functionality.

[0140] In the example embodiment shown, the IEDs 121, 122, 123, 124 are communicatively coupled to the central processing unit 140 via a “cloud” 150. In some embodiments, the IEDs 121, 122, 123, 124 may be directly communicatively coupled to the cloud 150, as the IED 121 is in the illustrated embodiment. In other embodiments, the IEDs 121, 122, 123, 124 may be indirectly communicatively coupled to the cloud 150, for example, through an intermediary device such as a cloud connection hub 130 (or gateway), as the IEDs 122, 123, 124 are in the illustrated embodiment. The cloud connection hub 130 (or gateway) may, for example, provide the IEDs 122, 123, 124 with access to the cloud 150 and the central processing unit 140.

[0141] As used herein, the terms "cloud" and "cloud computing" are intended to refer to computing resources that are connected to the Internet or otherwise accessible to IEDs 121, 122, 123, 124 via a communication network, which may be a wired or wireless network, or a combination of both. The computing resources comprising cloud 150 may be concentrated in a single location, distributed in multiple locations, or a combination of both. A cloud computing system may divide computing tasks between multiple racks, blades, processors, cores, controllers, nodes, or other computing units according to a specific cloud system architecture or programming. Similarly, a cloud computing system may store instructions and computing information in a centralized memory or storage device, or may distribute such information between multiple storage devices or memory components. A cloud system may store multiple copies of instructions and computing information in redundant storage units, such as a RAID array.

[0142] The central processing unit 140 may be an example of a cloud computing system or a cloud-connected computing system. In an embodiment, the central processing unit 140 may be a server located in a building where the loads 111, 112, 113, 114, 115 and the IEDs 121, 122, 123, 124 are installed, or may be a cloud-based service located remotely. In some embodiments, the central processing unit 140 may include computing functional components similar to the IEDs 121, 122, 123, 124, but may generally have a greater number and / or more powerful versions of components involved in data processing, such as processors, memories, storage devices, interconnect mechanisms, etc. The central processing unit 140 may be configured to implement various analysis techniques to identify patterns in the measurement data received from the IEDs 121, 122, 123, 124, as discussed further below. The various analysis techniques discussed herein also include the execution of one or more software functions, algorithms, instructions, applications, and parameters stored on one or more memory sources communicatively coupled to the central processing unit 140. In certain embodiments, the terms "function," "algorithm," "instruction," "application," or "parameter" may also refer to a hierarchy of functions, algorithms, instructions, applications, or parameters, respectively, operating in parallel and / or in series. The hierarchy may include a tree-based hierarchy, such as a binary tree, a tree with one or more child nodes inherited from each parent node, or a combination thereof, where each node represents a specific function, algorithm, instruction, application, or parameter.

[0143] In an embodiment, because the central processing unit 140 is connected to the cloud 150, it can access additional cloud-connected devices or databases 160 via the cloud 150. For example, the central processing unit 140 can access the Internet and receive information such as weather data, utility pricing data, or other data that can be used to analyze the measurement data received from the IEDs 121, 122, 123, 124. In an embodiment, the cloud-connected device or database 160 can correspond to a device or database associated with one or more external data sources. In addition, in an embodiment, the cloud-connected device or database 160 can correspond to a user device from which a user can provide user input data. The user can use the user device to view information about the IEDs 121, 122, 123, 124 (e.g., the manufacturer, model, type, etc. of the IED) and data collected by the IEDs 121, 122, 123, 124 (e.g., energy usage statistics). In addition, in an embodiment, the user can use the user device to configure the IEDs 121, 122, 123, 124.

[0144] In an embodiment, by utilizing the cloud connectivity and enhanced computing resources of the central processing unit 140 relative to the IEDs 121, 122, 123, 124, complex analysis can be performed on data retrieved from one or more IEDs 121, 122, 123, 124, as well as the additional data sources discussed above, as appropriate. The analysis can be used to dynamically control one or more parameters, processes, conditions, or equipment (e.g., loads) associated with the electrical system.

[0145] In an embodiment, the parameter, process, condition or equipment is dynamically controlled by a control system associated with the electrical system. In an embodiment, the control system may correspond to or include one or more of the IEDs 121, 122, 123, 124 in the electrical system, a central processing unit 140, and / or other devices inside or outside the electrical system.

[0146] refer to Figure 1B , for example, can be applied to Figure 1A The illustrated example IED 200 of an electrical system includes a controller 210, a memory device 215, storage 225, and an interface 230. IED 200 also includes input-output (I / O) ports 235, sensors 240, communication modules 245, and an interconnect mechanism 220 for communicatively coupling two or more IED components 210-245.

[0147] The memory device 215 may include volatile memory, such as DRAM or SRAM, for example. The memory device 215 may store programs and data collected during operation of the IED 200. For example, when the IED 200 is configured to monitor or measure one or more loads (e.g., Figure 1A In the embodiment shown in FIG. 111 ), the memory device 215 may store the monitored electrical parameters.

[0148] The storage system 225 may include a computer-readable and writable non-volatile recording medium, such as a disk or flash memory, in which signals defining a program to be executed by the controller 210 or information to be processed by the program are stored. The controller 210 may control data transfer between the storage system 225 and the memory device 215 according to known computing and data transfer mechanisms. In an embodiment, electrical parameters monitored or measured by the IED 200 may be stored in the storage system 225.

[0149] I / O port 235 may be used to connect a load (e.g., Figure 1A 111 shown) is coupled to the IED 200, and the sensor 240 can be used to monitor or measure an electrical parameter associated with the load. The I / O port 235 can also be used to couple to external devices of the IED 200, such as sensor devices (e.g., temperature and / or motion sensor devices) and / or user input devices (e.g., local or remote computing devices) (not shown). The I / O port 235 can also be coupled to one or more user input / output mechanisms, such as buttons, displays, acoustic devices, etc., to provide an alert (e.g., display a visual alert, such as text and / or a steady or flashing light, or provide an audio alert, such as a beep or a prolonged sound) and / or allow a user to interact with the IED 200.

[0150] The communication module 245 may be configured to couple the IED 200 to one or more external communication networks or devices. These networks may be private networks within the building where the IED 200 is installed, or public networks such as the Internet. In an embodiment, the communication module 245 may also be configured to couple the IED 200 to a cloud connection hub (e.g., Figure 1A 130) or a cloud-connected central processing unit (e.g., Figure 1A 140) shown.

[0151] The IED controller 210 may include one or more processors configured to perform (multiple) specific functions of the IED 200. The (multiple) processors may be commercial processors, such as Intel's well-known Pentium™, Core™ or Atom™ class processors. Many other processors are also available, including programmable logic controllers. The IED controller 210 may execute an operating system to define a computing platform on which (multiple) applications associated with the IED 200 may run.

[0152] In an embodiment, the electrical parameters monitored or measured by the IED 200 may be received at an input of the controller 210 as IED input data, and the controller 210 may process the measured electrical parameters to generate IED output data or signals at its output. In an embodiment, the IED output data or signals may correspond to the output of the IED 200. For example, the IED output data or signals may be provided at (multiple) I / O ports 235. In an embodiment, the IED output data or signals may be received by a cloud-connected central processing unit, for example, for further processing (e.g., identifying power quality events, as briefly discussed above), and / or received by equipment (e.g., loads) to which the IED is coupled (e.g., for controlling one or more parameters associated with the equipment, as will be further discussed below). In one example, the IED 200 may include an interface 230 for displaying a visualization indicating the IED output data or signals. In an embodiment, the interface 230 may correspond to a graphical user interface (GUI), and the visualization may include a tolerance curve characterizing a tolerance level of the equipment to which the IED 200 is coupled, as will be further described below.

[0153] The components of IED 200 may be coupled together via an interconnect mechanism 220, which may include one or more buses, lines, or other electrical connections. Interconnect mechanism 220 may enable communications (eg, data, instructions, etc.) to be exchanged between system components of IED 200.

[0154] It should be understood that IED 200 is only one of many potential configurations of IEDs according to aspects of the present disclosure. For example, an IED according to an embodiment of the present disclosure may include more (or fewer) components than IED 200. In addition, in an embodiment, one or more components of IED 200 may be combined. For example, in an embodiment, memory 215 and storage device 225 may be combined.

[0155] Now back to Figure 1A , in order to accurately describe non-periodic events, such as electrical systems (such as Figure 1A When a voltage sag occurs in an electrical system (such as an electrical system shown in FIG. 1 ), it is important to measure the voltage signal associated with the event. Two attributes commonly used to characterize voltage sags and transients are the magnitude (deviation from the norm) and duration (length of time) of the event. Both of these parameters help define and therefore mitigate these types of power quality issues. A scatter plot of the magnitude of an event (y-axis) vs. its corresponding duration (x-axis) is often displayed in a single graph referred to as an "amplitude-duration" graph, a "power tolerance curve," or, as referred to herein, a tolerance curve.

[0156] Figure 2A well-known amplitude-duration graph 250 is shown: the Information Technology Industry (ITI) curve (sometimes referred to as the ITIC curve or CBEMA curve) 260. The ITIC curve 260 shows the "AC input voltage envelope that can generally be tolerated (without interruption of function) by most Information Technology Equipment (ITE)" and is "applicable to 120V nominal voltage obtained from 120V, 208Y / 120V, and 120 / 240V 60Hz systems." The "Prohibited Region" in the figure includes any surge or swell that exceeds the upper limit of the envelope. Events occurring in this area may cause damage to the ITE. The "No Damage Region" includes sags or interruptions that are not expected to damage the ITE (i.e., below the lower limit of the envelope). In addition, the "No Interruption in Function Region" describes the area between the blue lines where sags, swells, interruptions, and transients can generally be tolerated by most ITE.

[0157] As is known, the constraints of ITIC curve 260 include:

[0158] 1. It is a static / fixed envelope / curve,

[0159] 2. It is proposed for IT,

[0160] 3. It is suitable for 120V 60Hz electrical system,

[0161] 4. It is a standardized / universal diagram that describes what should be expected as “normal”,

[0162] 5. It does not provide information about the consequences of the event.

[0163] 6. It is a voltage-based graph only and does not take into account any other electrical parameter(s), and

[0164] 7. For multiplication efficiency, it is presented on a semi-log plot.

[0165] It will be appreciated that prior art tolerance curves, such as ITIC / CBEMA curves, SEMI curves, or other manually configured curves, are generally no more than suggestions for a particular application. They do not indicate how a particular system or equipment, device, load, or control device associated with an equipment, device, or load will actually respond to a sag / swell event, what the impact of the event on the electrical system will be, or how and where to economically mitigate these problems. Furthermore, zones (subsystems) within the electrical system are all treated equally, even though most IEDs monitor multiple loads. A good analogy is a road atlas: while the atlas shows the location of the road, it does not indicate the location of road hazards, expected gas mileage, vehicle conditions, buildings, etc. A better approach is needed to improve voltage sag and swell management in electrical systems.

[0166] With the above in mind, the ability to provide customized tolerance curves allows energy consumers (and the systems and methods disclosed herein) to better manage their systems through simplified investment decisions, reduced CAPEX and OPEX costs, identification and characterization of issues and opportunities, improved ride-through, and ultimately higher productivity and profitability.

[0167] A few example factors to consider when leveraging the benefits of providing energy consumers with dynamic tolerance curves include:

[0168] 1. No two customers are exactly alike, and no two metering points are exactly alike. Dynamic tolerance curves are uniquely tailored to the metering data collection points on a specific electrical system.

[0169] 2. As events occur and are captured, dynamic tolerance curves are continuously updated based on the unique response of the electrical system.

[0170] 3. The dynamic tolerance curve can be applied to any type of electrical system / any type of customer; it is not limited to ITE systems.

[0171] 4. The dynamic tolerance curve can also be used for essentially any voltage level; it is not limited to 120 volt systems.

[0172] 5. A dynamic tolerance curve has no fixed scaling; it can (and may need to) scale automatically.

[0173] 6. Dynamic tolerance curves from discrete devices can be automatically aggregated into a single dynamic system tolerance curve.

[0174] In view of the above, there are many new potential features according to the present disclosure that can bring many benefits to energy consumers. In embodiments, the goal of these features is to simplify a typically complex subject matter into actionable opportunities for energy consumers. Example features according to the present disclosure are set forth below for consideration.

[0175] I. Dynamic Tolerance Curve

[0176] This embodiment of the present disclosure includes automatically adjusting the sag / swell tolerance curve based on the load impact measured by the discrete IED. In this embodiment, the "load impact" is determined by evaluating the pre-event load against the post-event load (i.e., the load after the event begins). The difference between the pre-event and post-event loads (i.e., kilowatts, current, energy, etc.) is used to quantify the impact of the event. The measurement of "impact" can be calculated as a percentage value, an absolute value, a normalized value, or other value useful to energy consumers. Further evaluations can include changes in: voltage, current, power factor, total harmonic distortion (THD) level, discrete harmonic component level, total demand distortion (TDD), imbalance, or any other electrical parameter / characteristic that can provide an indication of the type (load or source), magnitude, and location of changes within the electrical system. The data source can be from recorded data, waveform data, direct MODBUS reading, or any other means.

[0177] Figure 3 A typical tolerance curve (e.g., ITIC curve) used as a baseline is shown (also in Figure 2 ). It should be noted that in embodiments, substantially any known uniquely described tolerance curve (e.g., SEMI F47, ANSI, CBEMA, other custom curves) may be used as the baseline tolerance curve, as the intent of this embodiment of the present disclosure is to dynamically customize (i.e., change, update, modify, etc.) the tolerance curve so that it reflects the unique voltage event tolerance characteristics at the installation point of the IED. As more events are captured and quantified by the IED, the accuracy and characterization of the dynamic voltage tolerance curve may be improved at the installation point of the IED. Figure 3 Also shown is a semi-log plot; however, the dynamic tolerance curve may be scaled in any practical format for both analysis and / or viewing purposes.

[0178] Figure 4 An example voltage sag event (50% of nominal, 3 ms duration) on the standard / baseline tolerance curve is shown, resulting in a 20% load loss as determined by the IED. Figure 5The shaded area in FIG. 1 shows the baseline tolerance curve (e.g., Figure 3 ) and the actual tolerance of the downstream metered load(s) due to the specific sensitivity of that location in the electrical system to voltage dips of this degree (magnitude and duration). Figure 6 An example automatically customized and updated tolerance curve constructed from event data points and determined for a point on an electrical system where an IED is installed is shown. In an embodiment, it is assumed that any sag / swell / transient event with more severe characteristics (i.e., deeper voltage sag, larger voltage swell, larger transient, longer duration, etc.) will affect the load at least as severely as the event currently being considered.

[0179] Figure 7 A number of voltage sags / swells / transients on a standard / baseline tolerance curve are shown. Some events are indicated as having an impact and some as having no impact based on one or more parameters that changed at the time of the event. Figure 8 Shown are automatically customized and updated tolerance curves for a large number of impactful and non-impactful voltage sags / swells / transients determined from measurement data obtained from points where the IEDs are installed on the electrical system.

[0180] a. Three-dimensional (3-D) dynamic tolerance curve with load impact (sometimes also called "dynamic tolerance-impact curve")

[0181] A standard tolerance curve (e.g., ITIC curve, SEMI curve, etc.) is described in a two-dimensional graph, with the percentage of nominal voltage on the y-axis and the duration (e.g., cycles, seconds, milliseconds, etc.) on the x-axis, for example, as Figure 7 Although the y-axis is presented in units of a percentage of the nominal voltage, it should be understood that the y-axis units may also be absolute units (e.g., a real value such as voltage), or any other descriptor of substantially the magnitude of the y-axis parameter. Figure 7 The x-axis in is logarithmic, but it should be understood that the x-axis does not have to be logarithmic (e.g., it can also be linear). These 2-D standard tolerance curve graphs only provide a limited description of the characteristics of the event (magnitude and duration); they do not provide information related to the impact of the event on the load(s). Although energy consumers know that the event occurred, they cannot tell whether (and if so, to what extent) the event affected their electrical system (and potentially, their operations).

[0182] Adding a third dimension to the tolerance curve graph allows energy consumers to visually identify a characterization of their system's sag / swell / transient tolerance (at the meter point) as related to magnitude, duration, and a third parameter such as load impact. Likewise, load impact is determined by analyzing changes in load (or other electrical parameters) before and after the event using logged data, waveform data, direct MODBUS read data, other data, or any combination thereof.

[0183] A three-dimensional (3-D) tolerance curve according to an embodiment of the present disclosure may be adapted and / or oriented to any axis, perspective, scale, numerical ascending / descending, alphabetical order, color, size, shape, electrical parameter, event characteristic, etc., to effectively describe one or more events to an energy consumer. For example, Fig. 9 An exemplary orthogonal view of a tolerance-impact curve combining three parameters is shown: 1) percentage of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percentage of load affected on the z-axis. Although in the illustrated embodiment, the y-axis is presented in units of percentage of nominal voltage, it should be understood that the y-axis units may also be absolute units (e.g., real values ​​such as voltage), or any other descriptor of substantially the magnitude of the y-axis parameters. Furthermore, although in the illustrated embodiment the x-axis is logarithmic, it should be understood that the x-axis need not be logarithmic (e.g., it may also be linear). Fig.10 Shows Fig. 9 An exemplary single-point perspective 3-D view of the same tolerance-impact curve shown in FIG, and incorporating the same corresponding parameters for the three axes. It also attempts to integrate color shading to help illustrate the severity of the impact due to an event of a particular magnitude and duration (from least to worst; in the illustrated embodiment, the colors go from light to dark as indicated by the arrows). Fig.11 An exemplary single-point perspective 3-D view of a tolerance-impact curve is attempted to illustrate, which combines magnitude, duration, load impact percentage, shading, and event shape (to provide more event characteristics in a single diagram). Likewise, the load impact can be a relative percentage of the total load before the event (as shown), or it can be a real value (e.g., kilowatts, amps, etc.), an increase or decrease in value, or any other manipulation of these or any other electrical parameters.

[0184] b. Three-dimensional (3-D) dynamic tolerance-recovery time curve

[0185] Building on the previous discussion of load impact, in an embodiment, tolerance-impact curves may also be used to more directly quantify the impact of voltage sag / swell / transient events on the operation of energy consumers. The time to recover from an event may directly affect the total cost of a voltage event.

[0186] For purposes of this disclosure, "recovery time" is defined as the period of time required to return (or approximately return) an electrical system parameter to its original state prior to the event that caused the initial disturbance of the electrical system parameter. In an embodiment, the recovery time and the load impact are independent variables; both are independent of each other. For example, a voltage event may affect a small percentage of the load, but the recovery time may be quite long. Conversely, the time to recover from an extremely impactful event may be relatively short. Just as the impact of an event depends on many factors (some examples of which are set forth in the Invention Summary section of this disclosure), so does the recovery time. Some examples of factors that may significantly affect the duration of the recovery time include: the ability to quickly locate the source of the event (if the source of the event is within the facility), the extent of equipment damage, spare parts availability, personnel availability, redundant systems, protection schemes, etc.

[0187] An example method of calculating the recovery time includes measuring the time that elapsed between the occurrence of the first influential event and the point in time when the load exceeds a predetermined threshold of the pre-event load. For example, a 500 kW pre-event load with a 90% recovery threshold would indicate that recovery occurred at 450 kW. If it takes 26 minutes for the metered load to reach or exceed 450 kW (i.e., 90% of the pre-event load), the recovery time is equal to 26 minutes. The recovery threshold may be determined using a relative percentage of the pre-event load, an absolute value (kW), a restoration of voltage or current levels, an external or manual trigger, a recognized standard value, a subjective configuration, or by some other method using (multiple) electrical or non-electrical parameters.

[0188] Fig.12 An exemplary orthogonal view of a tolerance-recovery time curve is shown that combines three parameters: 1) a percentage of nominal voltage on the y-axis, 2) a duration in cycles and seconds (or alternatively, milliseconds) on the x-axis, and 3) a recovery time or period in days, hours, and / or minutes on the z-axis. Although in the illustrated embodiment, the y-axis is presented in units of a percentage of nominal voltage, it should be understood that the y-axis units may also be absolute units (e.g., real values ​​such as voltage), or substantially any other descriptor of the magnitude of the y-axis parameter. Furthermore, although in the illustrated embodiment the x-axis is logarithmic, it should be understood that the x-axis need not be logarithmic (e.g., it may also be linear). In embodiments, the z-axis (recovery time) may be configured to substantially any fixed scale (or auto-scale), may be listed in ascending or descending order, and may use substantially any known time unit. Fig.13 Shows Fig.12 An exemplary single-point perspective 3-D view of the same tolerance-recovery time curve shown in , and incorporating the same corresponding parameters for the three axes. Fig.13Color shading is also integrated to help illustrate the severity of recovery time due to events of a particular magnitude and duration (from least to worst; in the illustrated embodiment, the colors go from light to dark as indicated by the arrows). Fig.14 An exemplary single-point perspective 3-D view of a tolerance-recovery time curve combining amplitude, duration, recovery time, shading, and event shape (to provide more event characteristics in a single graph) is shown.

[0189] c.3-D Dynamic Tolerance-Economic Impact Curve

[0190] The above 3-D curve can also be used to illustrate the economic impact (e.g., production loss, restart loss, product / material loss, equipment loss, third-party loss, total loss, etc.) as it relates to a voltage event. Obviously, configuration can be time-consuming; however, the relationship between recovery time and any relevant economic factors can be easily shown and understood using a dynamic tolerance-economic impact curve. The cost of downtime (CoD) can be used initially to determine a given economic cost during the recovery period (assuming the CoD value is reasonable). Some studies have shown that every minute of downtime in the automotive industry costs energy consumers more than $22K. In contrast, similar studies have shown that energy consumers in the healthcare industry lose more than $10K per minute of downtime. Over time, energy consumers (and the systems and methods disclosed herein) can quantify their typical recovery time costs (whether linear or nonlinear), or they can conduct studies to determine this relationship in their facilities or businesses. Determining the relationship between voltage events and economic factors will allow energy consumers to make faster and better decisions on capital expenditures and / or service retention.

[0191] For example, Fig.15 A 50% production loss for a nominal voltage sag event of 3 milliseconds duration is shown. A recovery time of 8 hours is assumed (see, e.g., Fig.13 ) and the production loss averages $2.5K / hour, the total production loss would be $20K. For example, if ride-through capability helps avoid an operational outage at a cost of $50K, then the payback for investing in voltage sag ride-through equipment may only be about 2.5 voltage events. As mentioned at the beginning of this document, studies have shown that the average industrial customer experiences about 66 voltage sags per year, so the decision for mitigation should be simple in this case.

[0192] d. Upstream / downstream tolerance-influence curve

[0193] As mentioned above, it is well known that electrical systems are sensitive to voltage events to varying degrees. For some energy consumers, voltage events may be just a nuisance (with no significant impact); for other energy consumers, any small voltage anomaly may be catastrophic. As mentioned earlier, quantifying the impact of voltage events helps energy consumers determine the severity, prevalence, and impact of these events on their operations. If a voltage event affects the operation of an energy consumer, the next step is to identify the source of the problem.

[0194] Metering algorithms and other related methods may be used to determine whether the source of a voltage event is upstream or downstream of a metering point (e.g., an electrical installation point of an IED in an electrical system). Fig.16 A simple grid with three metering points (M1, M2, and M3) is shown. A fault (X) is shown to occur between M1 and M2. In an embodiment, the algorithm in M1 may indicate that the source of the fault is downstream of its location (↓), while the algorithm in M2 may indicate that the source of the fault is upstream of its location (↑). Furthermore, in an embodiment, the algorithm in M3 may indicate that the source of the fault is upstream (↑). By evaluating the fault as a system event (i.e., using data from all three IEDs), in an embodiment, the location of the source of the fault in the grid (i.e., with respect to the metering point) can generally be identified.

[0195] This embodiment evaluates the impact of the voltage event against the indicated location (upstream or downstream of the metering point) associated with the source of the voltage event. This is very useful because upstream voltage event sources typically require different mitigation schemes (and associated costs) than downstream voltage event sources. In addition, there may be different economic considerations (e.g., impact costs, mitigation costs, etc.) depending on the location of the voltage event source in the electrical system. The larger the affected area, the higher the cost of mitigating the problem may be. Upstream voltage events may affect a larger portion of the power grid than downstream voltage events, and therefore, the mitigation costs may be higher. Similarly, the cost of mitigating voltage events will be determined on a case-by-case basis because each metering point is unique.

[0196] In an embodiment, the IED installed at the metering point is configured to measure, protect and / or control one or more loads. The IED is usually installed upstream of the load(s) because the current flowing to the load(s) may be a key aspect of measuring, protecting and / or controlling the load(s). However, it will be appreciated that the IED may also be installed downstream of the load(s).

[0197] refer to Fig.16A, another example electrical system includes a plurality of IEDs (IED1, IED2, IED3, IED4, IED5) and a plurality of loads (L1, L2, L3, L4, L5). In an embodiment, loads L1, L2 correspond to a first load type, and loads L3, L4, L5 correspond to a second load type. In some embodiments, the first load type may be the same as or similar to the second load type, or may be different from the second load type in other embodiments. Loads L1, L2 are located at a position "electrically" (or "conductively") downstream relative to at least IEDs: IED1, IED2, IED3 in the electrical system (i.e., IEDs: IED1, IED2, IED3 are upstream of loads L1, L2). In addition, loads L3, L4, L5 are located at a position "electrically" downstream relative to at least IEDs: IED1, IED4, IED5 in the electrical system (i.e., IEDs: IED1, IED4, IED5 are upstream of loads L3, L4, L5).

[0198] In the illustrated embodiment, a power quality event (or fault) X is shown to occur upstream relative to the loads L1, L2. In the illustrated example embodiment, an upward arrow indicates "upstream" and a downward arrow indicates "downstream". As shown, the IEDs: IED1, IED2 are shown pointing toward the fault X. In addition, the IEDs: IED3, IED4, IED5 are shown pointing upstream. In an embodiment, this is because the path to the fault X is upstream of the respective locations of the IEDs: IED3, IED4, IED5 in the electrical system, and downstream of the respective locations of the IEDs: IED1, IED2 in the electrical system. In an embodiment, for example, an algorithm for determining the direction of the fault X may be located (or stored) in an IED, field software, a cloud-based system, and / or a gateway, etc.

[0199] Fig.17 Shows something similar to the above Figure 7 2-D graph of voltage tolerance curves of voltage events captured by an IED; however, the upstream and downstream voltage events are uniquely represented and superimposed / overlaid together. Fig.18 shows a 2-D voltage tolerance curve which only shows the Fig.17 The upstream voltage events are broken down from the total set of voltage events shown. Similarly, Fig.19 shows a 2-D voltage tolerance curve which only shows the Fig.171 and 1 . The diagrams illustrate a breakdown of downstream voltage events from a total set of voltage events. These diagrams allow energy consumers (and the systems and methods disclosed herein) to differentiate between upstream and downstream events, thereby helping to provide a better visual intuition for identifying the primary locations of voltage event sources (and possible causes). Of course, additional or alternative features, parameters, filters, and / or other relevant information (e.g., electrical data, time, metadata, etc.) may be used, displayed, and / or plotted to further efficiently and productively modify the voltage tolerance curve.

[0200] For example, Fig. 20 An exemplary orthogonal view of a tolerance-influencing source location curve combining five parameters is shown: 1) percentage of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percentage of load affected on the z-axis. Although in the illustrated embodiment, the y-axis is presented in units of percentage of nominal voltage, it should be understood that the y-axis units can also be absolute units (e.g., real values ​​such as voltage), or substantially any other descriptor of the magnitude of the y-axis parameters. Furthermore, although in the illustrated embodiment the x-axis is logarithmic, it should be understood that the x-axis need not be logarithmic (e.g., it can also be linear). Fig. 20 Additional dimensions are also included, such as recovery time (size of the data point) and whether a particular event is upstream or downstream of the metering point (white or black center of the data point, respectively). In addition, the z-axis can be used to show the recovery time, while the size of the data point can be used to indicate the percentage of load affected. It should be understood that many other parameters / dimensions can be combined to be meaningful and / or useful.

[0201] e. Use dynamic tolerance curves to mitigate sags / swells / transients

[0202] As mentioned above, electrical systems are often sensitive to voltage events to varying degrees. For some energy consumers, a voltage event may be just a nuisance (with no significant impact); for other energy consumers, any voltage event may be catastrophic. As mentioned earlier, quantifying the impact of voltage events helps energy consumers determine the severity, prevalence, and impact of these events on their operations. If a voltage event affects an energy consumer's operations, the next step should be to identify the problem so that it can be reduced or eliminated entirely.

[0203] In embodiments, for various embodiments discussed throughout this disclosure, eliminating or reducing the impact of voltage sags / swells / transients (as well as instantaneous, temporary and momentary interruptions) can generally be achieved in three ways: 1) removing the source of the voltage event, 2) reducing the number or severity of voltage events generated by the source, or 3) minimizing the impact of the voltage event on the affected equipment. In some embodiments, it is very difficult to remove the source (or multiple sources) of the voltage event because these same sources are often components or loads that are integral to the electrical infrastructure, process and / or operation of the facility. In addition, the source of the voltage event may be located on the utility, thus hindering the ability to directly solve the problem. If the source of the voltage event is located within the facility of the energy consumer, it may be possible to minimize the voltage event at the source by using different technologies (technologies or techniques) (e.g., "soft start" motors instead of "across-the-line" motor start). In some embodiments, removing or replacing the source (or multiple sources) of the voltage event may be costly and require extensive redesign of the electrical system or subsystem. It is also possible to "desensitize" the equipment to the impact of voltage events such as sags, swells and transients. As always, there are economic trade-offs when considering the best way to reduce or eliminate voltage problems. Fig.21 is a generally recognized graphic representation that shows the progression of the cost of mitigating voltage events and other power quality related problems, which tends to increase as the solution moves closer to the source. A thorough economic evaluation may include the initial and total life cycle costs of a given solution. In addition, it may be important to consider the response of any prospective solution to both internal and external sources of system voltage disturbances.

[0204] As an example, motors are important electrical devices used in most processes. Standard (across-the-line) motor starting typically produces a voltage sag due to the impedance between the source and the motor, as well as the motor's inrush current, which is typically 6-10 times the full-load rated current. Removing the motor from the process is likely impractical; however, reducing the voltage sag or minimizing its impact on adjacent equipment may be a viable alternative. Some example solutions may include using different motor technologies, such as variable speed drives, or employing motor soft starting techniques to control or limit inrush current (thereby reducing or eliminating voltage sags at startup). Another example solution is to deploy one or more of several mitigation devices or equipment to reduce the impact of voltage events on sensitive equipment. Again, each electrical system is unique, so the cost of mitigating power quality disturbances may vary by location, system, and customer.

[0205] This embodiment includes evaluating the ride-through characteristics of multiple mitigation devices against the dynamic tolerance-impact curve provided by each capable IED. The output of the evaluation can indicate the additional ride-through benefits of applying any specific mitigation device to any specific metering location. In addition, a comparison of the economic, operational and / or other benefits between two or more ride-through technologies (technologies ortechniques) of a specific system or subsystem can also be provided. In an embodiment, in order to perform the evaluation, a managed collection (or library) of the ride-through characteristics of the mitigation device can be evaluated. The managed collection (or library) of the mitigation device can include (but is not limited to) characteristics and / or capabilities, such as the type of each known variety, technology, amplitude vs. performance of duration, load constraints, typical applications, purchase cost, installation cost, operating cost, availability, purchase source, size / form factor, brand, etc. In an embodiment, the characteristics and capabilities described in the management collection of the mitigation device will be considered (as needed and available) for each (or substantially each) discrete metering point (or subsystem) where data can be obtained and evaluated. One or more crossing characteristic curves (indicative of crossing capability of amplitude vs. duration) of any or each mitigation device found in the management set (library) may be superimposed / overlaid on the dynamic tolerance curve of at least one or more discrete metering points. Alternatively, the assessment may be provided by some other means accordingly. One or more characteristics and / or capabilities of the mitigation device(s) may be included in the assessment against the dynamic tolerance curve, the assessment being based on factors such as those listed and available in the management set (or library). In an embodiment, the assessment may be alarm driven, manually or automatically triggered, planned, or otherwise initiated.

[0206] The dynamic tolerance-impact curve provided by each capable IED of a hierarchy (or portion of a hierarchy) of an electrical system may be evaluated against the ride-through characteristics of one or more mitigation devices. In an embodiment, it may be more feasible, more cost-effective, or otherwise more beneficial to provide ride-through improvements as part of a system, subsystem process, and / or discrete location. While it may be economical / practical / feasible to apply one type of ride-through mitigation solution to one device or one subsystem / zone, it may be more economical / practical / feasible to provide a different ride-through mitigation solution for another device or subsystem / zone within the electrical system. In short, based on the available information, the most economical / practical / feasible ride-through mitigation solution may be provided for the entire (or portion) electrical system. In an embodiment, other factors may be considered when determining ride-through improvements at one or more locations within an electrical system; however, the present application emphasizes the importance of utilizing discretely established dynamic tolerance curves from one or more IEDs.

[0207] Fig. 22 Shown from Figure 52-D dynamic tolerance curves. Again, this example shows a tolerance curve that is customized and updated based on a single 50% voltage sag lasting 3 milliseconds with a 20% load impact. An evaluation can be performed to determine the most economical / practical / feasible method to improve ride-through performance at that specific location in the electrical system. A managed collection (library) of mitigation devices can be evaluated against suitable options and feasible solutions. Fig.23 Shows Schneider Electric's 100% OD overlaid on top of the updated dynamic tolerance curve The ride-through characteristics (amplitude vs. duration) are claimed to comply with SEMI F47. Fig.24 The energy consumer is provided with a graphical indication of the crossing benefit of the SagFighter at that particular location in the electrical system (e.g., Fig.24 Of course, the final mitigation device recommendation provided to the energy consumer may not only depend on the ride-through characteristics of the mitigation device (e.g., economical / practical / feasible / etc.). Furthermore, this approach may be provided to multiple metering points across an electrical system or subsystem.

[0208] f. Use dynamic tolerance curves to determine the opportunity cost of crossing mitigation options

[0209] As is well known, opportunity cost refers to the benefit or gain that could have been obtained but was foregone instead of taking an alternative course of action. For example, a facility manager with a fixed budget might be able to invest funds to expand a facility or improve the reliability of an existing facility. The opportunity cost would be determined based on the economic benefits of the choice that the facility manager did not make.

[0210] In this embodiment of the present disclosure, the "opportunity cost" is expanded to include other benefits, such as production loss, material loss, recovery time, load impact, equipment loss, third party loss, and / or any other loss that can be quantified by some measurement. In addition, an "alternative course of action" may be to decide not to take any action. Some benefits of not taking an action include resource savings, monetary savings, time savings, reduced operational impact, deferral, etc. That is, decision makers typically believe that the benefit of not taking an action is greater than the benefit of taking a particular action(s).

[0211] The decision not to take an action is often based on a lack of information relevant to a given problem. For example, if someone cannot quantify the benefit of taking a particular action, they are less likely to take that action (which may be the wrong decision). Conversely, if someone can quantify the benefit of taking a particular action, they are more likely to make the right decision (either to take an action or not). In addition, having quality information available provides a tool for generating other economic evaluations, such as cost / benefit analyses and risk / reward ratios.

[0212] This embodiment of the present disclosure can continuously (or semi-continuously) evaluate the impact of voltage events (sags / swells / transients) on the crossing tolerance characteristics of one or more mitigation devices, devices and / or equipment. The evaluation can take into account historical data to continuously track voltage events, associated discrete and combined system impacts (e.g., as relative values, absolute values, demands, energy or other quantifiable energy-related characteristics), subsystem and / or system perspectives, hierarchical impacts from two or more devices, zones, cross-zones, or combinations thereof. The information obtained from the evaluation can be used to provide feedback and metrics about operational impacts, which can be avoided if one or more mitigation devices, devices and / or equipment have been installed at one (or more) locations.

[0213] For example, Fig.25 Provides a 2-D diagram showing if a decision is made before the corresponding voltage event to install Then the events (concentric circles) (and any associated impacts) can be avoided. Fig.26 It shows that Fig.25 Similar to the graph shown, but also includes the estimated recovery time that could have been avoided if the mitigation solution was implemented prior to the voltage event. Metrics associated with these potentially avoided events (e.g., relative impact (%), absolute impact (watts, kilowatts, etc.), recovery time per event, cumulative recovery time, downtime, losses, other quantifiable parameters, etc.) can also be provided to energy consumers to help justify investments in addressing voltage sag issues. Energy consumers (or the systems and methods disclosed herein) can also select what level of mitigation is reasonable by comparing different mitigation techniques to historical tolerance curve data (i.e., points in a gradually decreasing region of interest (ROI)). Metrics can be listed for each event or accumulated in a table, provided or plotted as discrete points or from two or more devices (i.e., a system-level perspective), analyzed, or otherwise manipulated to indicate and / or quantify the impact and / or opportunity cost of not installing voltage event mitigation. The same information can be displayed in a 3-D orthogonal view of a tolerance-impact curve that combines at least three parameters, such as: 1) percentage of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percentage of load affected on the z-axis (or recovery time in days, hours, or minutes). Although in the illustrated embodiment, the y-axis is presented in units of percentage of nominal voltage, it should be understood that the y-axis units can also be absolute units (e.g., real values ​​such as voltage), or substantially any other descriptor of the magnitude of the y-axis parameters. In addition, although in the illustrated embodiment the x-axis is logarithmic, it should be understood that the x-axis does not have to be logarithmic (e.g., it can also be linear). Other parameters, characteristics, metadata, and / or mitigation devices can be similarly incorporated into the graphs and / or tables.

[0214] g. Use dynamic tolerance curves to verify the effectiveness of mitigation techniques

[0215] The decision to reassess or re-evaluate an investment facility's infrastructure is often overlooked, assumed, or based solely on guesswork and assumptions. In most cases, the benefits of installing mitigation technologies are only assumed and never quantified. The Measurement and Verification (M&V) process focuses on quantifying energy savings and conservation; however, steps to improve reliability and power quality are not considered.

[0216] Embodiments of the present disclosure periodically or continuously provide the following example benefits:

[0217] Allocating risk between contractors and their clients (e.g. performance contracts),

[0218] Accurately evaluate voltage events to quantify savings (impact, recovery time, uptime, losses or other economic factors),

[0219] Reduce voltage quality uncertainty to a reasonable level,

[0220] Helps monitor equipment performance,

[0221] Identify additional monitoring and / or mitigation opportunities,

[0222] Reduced impact on target equipment, and

[0223] Improved operations and maintenance.

[0224] The dynamic voltage-impact tolerance curve provides a baseline for capturing voltage events at each discrete metering point of an affected or potentially affected process, operation, or facility. A post-installation evaluation can be performed using data obtained from areas where benefits are expected to be obtained. In an embodiment, these post-installation evaluations compare "before vs. after" to quantify the actual benefits of installing mitigation equipment. The quantification determined may include reduced event impacts, recovery time, operating costs, maintenance costs, or any other operational or economic variables. An exemplary equation for determining the savings calculated due to the installation of mitigation equipment may be:

[0225] Savings = (baseline cost – reduced downtime cost) ± adjustment

[0226] “Reduced downtime costs” may include all or some combination of the following:

[0227] Reduced production losses,

[0228] Reduced restart losses,

[0229] Reduced product / material losses,

[0230] Reduced equipment losses,

[0231] Reduced third-party costs, and

[0232] …and / or some other loss reduction.

[0233] The cost of installing mitigation equipment may need to be considered, which may be a "trim" in some embodiments.

[0234] Fig. 27 An example 2-D dynamic voltage tolerance curve according to the present disclosure is shown, where the bold black threshold line (-) represents the crossing of the baseline threshold at a discrete metering point and the bold white line (-) represents the predicted improvement in the voltage event crossing threshold by installing a specific type of mitigation equipment. Fig. 27 The concentric circles in indicate the voltage events (and therefore recovery times) that are expected to be avoided by installing mitigation equipment. Fig.28 An example 2-D dynamic voltage tolerance curve according to the present disclosure is shown, which shows the actual voltage events and recovery time avoided due to the installation of mitigation equipment. The bold white line (-) shows the actual improvement in the voltage crossing threshold by installing the mitigation equipment. In this example, the mitigation equipment exceeded expectations by avoiding three additional voltage events and 22 hours (actual event 42 - predicted event 20) of additional recovery time.

[0235] Each electrical system is unique and performs differently to some extent. Embodiments of the present disclosure use empirical data to characterize the actual performance of mitigation equipment. For example, Fig.28 As shown, the actual threshold for voltage ride-through (-) may perform better than expected because the downstream load on the mitigation equipment is lower than expected. This allows the mitigation equipment to ride through longer than expected. Conversely, exceeding the rated load of the mitigation equipment may result in worse performance than expected. As the load on the mitigation equipment continues to increase beyond its rating, the voltage ride-through threshold (-) will approach the original voltage ride-through threshold (-) or may be more severe.

[0236] Can produce something like Fig.15 3-D dynamic tolerance curves as shown to better demonstrate the impact of mitigation on other parameters such as load impact, restoration time, economic factors, etc. In this case, at least three dimensions will be used to characterize the electrical system at the IED installation point. The 3-D assessment will provide a better intuitive understanding of the historical, current and / or future performance of the mitigation equipment. This will also make the selection of mitigation equipment for future applications less complex and more cost-effective.

[0237] Metrics (e.g., relative impact (%), absolute impact (watts, kilowatts, etc.), reduced losses, other quantifiable parameters, etc.) associated with expected (based on historical data) and actual avoided events can be provided to energy consumers to help justify the original or additional investment to address the voltage sag problem. The metrics can be listed for each event or accumulated in a table, provided or plotted as discrete points or from two or more devices (i.e., a system-level perspective), analyzed, or otherwise manipulated to indicate and / or quantify the benefits and / or costs of each minute of impact avoided due to installed voltage event mitigation. The same information can be displayed as a 3-D orthogonal perspective of a tolerance-impact curve that incorporates at least three parameters, such as: 1) percentage of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percentage of load affected (or recovery time) on the z-axis. Although in the illustrated embodiment, the y-axis is presented in units of percentage of nominal voltage, it should be understood that the y-axis units can also be absolute units (e.g., such as real values ​​of voltage), or substantially any other descriptor of the magnitude of the y-axis parameters. Furthermore, although the x-axis is logarithmic in the illustrated embodiment, it should be understood that the x-axis need not be logarithmic (e.g., it may also be linear). For example, other parameters, characteristics, metadata, and / or mitigation devices may be similarly incorporated into the graphs and / or tables.

[0238] II. Using non-power quality IEDs to help quantify the impact of voltage events

[0239] The ability to quantify the impact of voltage events can be derived from changes in measurements of energy, current, or power flow (i.e., consumption). IEDs can be used to provide these measurements. Measurements can be obtained in real time (e.g., via direct MODBUS readings), historically (e.g., logged data), or by some other means.

[0240] Power monitoring systems typically incorporate various IEDs installed throughout the energy consumer's electrical system. These IEDs may have different levels of capabilities and feature sets; some more, some less. For example, energy consumers typically install high-end (many / most capability) IEDs ( Fig.29 The idea is to get as broad a picture as possible of the quality and quantity of the electrical signal as it is received from the source (usually, the utility). Because the budget for metering is usually fixed, and energy consumers generally want to meter as broadly as possible across their electrical system, conventional wisdom dictates that as the metering point is installed closer to the load, IEDs with lower and lower capabilities are used (see, for example, Fig.29 ). In short, most installations incorporate many more low / mid-range IEDs than high-end IEDs.

[0241] "High-end" metering platforms (and some "mid-end" metering platforms) are more expensive and are typically capable of capturing power quality phenomena including high-speed voltage events. "Low-end" metering platforms are less expensive and typically have reduced processor bandwidth, sampling rate, memory, and / or other capabilities compared to high-end IEDs. The focus of low-end IEDs (including energy measurements made in most circuit breakers, UPS, VSDs, etc.) is typically on energy consumption or other energy-related functions, and perhaps some very basic power quality phenomena (e.g., steady-state quantities such as unbalance, overvoltage, undervoltage, etc.).

[0242] The feature utilizes (i.e., correlates, relates, aligns, etc.) one or more voltage event indicators, statistical derivations, and / or other information from a high-end IED, and one or more similar and / or different measured parameters from a low-end IED, with the goal of quantifying the impact, recovery time, or other event characteristics at the low-end IED. An exemplary method of achieving this is by using a voltage event timestamp (an indicator of the moment the voltage event occurred) from a high-end IED as a reference point for evaluating measurable parameters corresponding to the same timestamp at a low-end that does not itself have the ability to capture voltage events. The data evaluated on all three of the high-end, mid-end, and low-end IEDs may include (but is not limited to) event magnitude, duration, phase or line value, energy, power, current, sequential components, imbalances, timestamps, changes before / during / after events, any other measured or calculated electrical parameters, metadata, metering characteristics, etc. Again, measurements may be acquired in real time (e.g., via direct MODBUS reads), historically (e.g., logged data), or by some other means.

[0243] Another example method of utilizing non-power quality IEDs is to extend the use of event alarms (including voltage events) derived from high-end IEDs. For example, when a high-end IED detects a voltage event, the consistent data from the low-end IED is analyzed to determine the impact, recovery time, or other event characteristics and / or parameters. If the analysis of the data from the low-end IED indicates that a certain level of impact has indeed occurred, the system performing the analysis of the consistent data can generate a voltage event alarm, an impact alarm, and / or other types of alarms. The alarm information may include any relevant parameters and / or information measured by the low-end IED, the high-end IED, metadata, metering characteristics, load impact, recovery time, which one or more high-end IEDs triggered the low-end IED alarm, etc.

[0244] Fig.29 and Fig.30A relatively simple example of an embodiment of the present disclosure is shown. At time t0, a corresponding metering point or location M1 where a high-end IED is installed indicates the start of a voltage event. The pre-event load is measured and a recovery time clock of the IED installed at metering location M1 begins. Other relevant data, metrics, and / or statistically derived information may also be measured or calculated as needed. At the same time, the software (on-site and / or cloud-based) and / or hardware managing the metering system evaluates other connected IEDs to determine whether another corresponding metering point or location (e.g., M2, M3, M4, M5, M6, M7, M8, M9, M10, M11, M12, M13, M14, M15, M16, M17, M18, M19, M20, M21, M22, M23, M24, M25, M26, M27, M28, M29, M30, M31, M32, M33, M34, M35, M36, M37, M38, M39, M40, M41, M42, M43, M44, M45, M46, M47, M48, M49, M50, M51, M52, M53, M54, M55 10 ) simultaneously experienced an influential event. In this example, it is found that the IED installed at meter location M7 has experienced a consistent influential event (for simplicity, other devices are ignored in this example). The pre-event load is determined by M7, and the recovery time clock of M7 starts timing using the timestamp of the voltage event as a reference. When the IEDs installed at meter locations M1 and M7 are identified as being affected by the voltage event, the impact is quantified based on pre-event / during-event / post-event electrical parameters (e.g., power, current, energy, voltage, etc.), where t0 is derived from the IED installed at meter location M1 and used as a reference point for both devices M1 and M7. The IED installed at meter location M7 is located downstream of the IED installed at meter location M1 and is subject to a more significant relative impact (i.e., a larger percentage of its pre-event load) due to system impedance and unique affected loads. The recovery time counters of the IEDs installed at meter locations M1 and M7 are independent of each other, and this is true for all IEDs. In this example, the recovery time of the IED installed at the metering location M7 is approximately the same as the recovery time of the IED installed at the metering location M1 (ie, t M1r ≈t M7r ); however, this may not always be the case as the recovery time may be unique at each metering location.

[0245] In an embodiment, virtual metering can also be used to identify the impact of voltage events on unmetered loads. Fig. 30AIn Figure 1, there are two electrical paths downstream of the IED installed at metering location M1. The electrical path on the right is metered by a physical IED (e.g., an IED installed at metering location M2); however, the electrical path on the left is not directly metered by a physical IED. If the load data measured by the IEDs installed at metering locations M1 and M2 are measured synchronously or pseudo-synchronously, the load flowing through the unmetered path V1 can be determined (within the accuracy and synchronization constraints of the IEDs installed at metering locations M1 and M2) by the following equation: V1 = M1 – M2. V1 represents the location of a “virtual meter” or “virtual IED” in the electrical system, which represents the difference between the metering locations M1 and M2 where the IEDs are installed for any synchronous (or pseudo-synchronous) load measurement.

[0246] For this example, Fig. 30A In FIG. 1 , a fault is considered that occurs downstream of an IED installed at metering location M1 and upstream of a virtual meter located at metering location V1. Using the concept of virtual metering as described above, it is determined that a load change has occurred in an unmetered path. Since the load data through the unmetered path can be derived from the IEDs installed at metering locations M1 and M2, the load impact on the unmetered path due to the fault can be calculated. In this example, other important parameters related to this embodiment of the present disclosure can also be derived from the virtual meter, including restoration time, economic impact, etc.

[0247] In one embodiment, the data sampling rate (e.g., power, current, energy, voltage or other electrical parameter) of the IEDs and / or any other IEDs installed at metering locations M1, M7 may be increased as needed, either dependently or independently, after an indicated voltage event, to provide more accurate results (e.g., recovery time). The data may be shown in a tabular format, graphically in 2-D or 3-D, color-coded, as a timeline of discrete IEDs, partitioned bands, hierarchical levels, or as a system (aggregate) view, linearly or logarithmically, or in any other structure or method deemed relevant and / or useful. The output of this embodiment may be via a report, text, email, auditory, screen / display, or by some other interactive means.

[0248] refer to Figures 30B-30I , several example diagrams are provided to further illustrate the concept of virtual metering according to embodiments of the present disclosure. As described above, an electrical system typically includes one or more metering points or locations. Also as described above, one or more IEDs (or other meters, such as virtual meters) can be installed or located (temporarily or permanently) at a metering location, for example, to measure, protect and / or control one or more loads in the electrical system.

[0249] refer to Fig. 30B, shows an example electrical system comprising a plurality of metering locations (here M1, M2, M3). In the illustrated embodiment, at least one first IED is installed at the first metering location M1, at least one second IED is installed at the second metering location M2, and at least one third IED is installed at the third metering location M3. The at least one first IED is a so-called "parent device(s)", and the at least one second IED and the at least one third IED are so-called "child devices". In the illustrated example embodiment, the at least one second IED and the at least one third IED are children of the at least one first IED (and are therefore siblings of each other), for example, since the at least one second IED and the at least one third IED are both installed at respective metering locations M2, M3 in the electrical system, which metering locations "branch" from a common point (here connection 1) associated with the metering location M1 where the at least one first IED is installed. Connection 1 is a physical point in the electrical system where energy flows (as measured by the at least one first IED at M1) diverge to provide energy to left and right electrical system branches (as measured by the at least one second IED and the at least one third IED at M2 and M3, respectively).

[0250] Fig. 30B The electrical system shown is an example of a "fully metered" system, where all branch circuits are monitored by physical IEDs (here, at least one first IED, at least one second IED, and at least one third IED). According to aspects of the present disclosure, dynamic tolerance curves can be developed for each discrete metering location (M1, M2, M3) independently without relying on (multiple) external inputs from other IEDs. For example, electrical measurement data from energy-related signals captured by at least one first IED installed at a first metering location M1 can be used to generate a unique dynamic tolerance curve for the metering location M1 (e.g., Fig. 30C In addition, electrical measurement data from energy-related signals captured by at least one second IED installed at the second metering location M2 may be used to generate a unique dynamic tolerance curve for the metering location M2 (e.g., as shown in FIG. 1 ). Fig.30D In addition, electrical measurement data from energy-related signals captured by at least one third IED installed at the third metering location M3 may be used to generate a unique dynamic tolerance curve for the metering location M3 (e.g., as shown in FIG. 1 ). Fig.30E ), without any input (or data) from the at least one first IED or the at least one second IED.

[0251] refer to Fig.30F (in Fig. 30B), another example electrical system is shown. Fig. 30B The electrical system shown, Fig.30F The electrical system shown includes multiple metering locations (here M1, M2, V1). Fig. 30B The electrical system shown, Fig.30F The electrical system shown comprises at least one metering device installed or located in each of the metering locations (M1, M2). Fig. 30B The electrical system shown, Fig.30F The electrical system shown includes a virtual meter (V1) according to an embodiment of the present disclosure.

[0252] In the illustrated embodiment, at least one first IED is installed at a first "physical" metering location M1, at least one second IED is installed at a second "physical" metering location M2, and at least one virtual meter is derived (or located) at a "virtual" (non-physical) metering location V1. The at least one first IED is a so-called "parent device", and the at least one second IED and the at least one virtual meter are so-called "child devices". In the illustrated example embodiment, the at least one second IED and the at least one virtual meter are children of the at least one first IED (and are therefore considered to be brothers of each other). In the illustrated embodiment, the at least one second IED and the at least one virtual meter are installed and derived at respective metering locations M2, V1 in the electrical system, respectively, which "branch" from a common point (here, connection 1) associated with the metering location M1 where the at least one first IED is installed. Connection 1 is a physical point in the electrical system where the energy flow (as measured by the at least one first IED at M1) diverges to provide energy to the left and right branches (as measured by the at least one second IED at M2, and as calculated for V1 by the at least one virtual meter).

[0253] According to an embodiment of the present disclosure, electrical measurement data associated with a virtual metering location V1 may be created / derived by calculating the difference between synchronized (or pseudo-synchronized) data from at least one first IED (herein, a parent device) installed at a first metering location M1 and at least one second IED (herein, a child device) installed at a second metering location M2. For example, the electrical measurement data associated with the virtual metering location V1 may be derived by calculating the difference between electrical measurement data from energy-related signals captured by at least one first IED and electrical measurement data from energy-related signals captured by at least one second IED at a specific point in time (e.g., for synchronized or pseudo-synchronized data, V1=M1-M2). It should be understood that a virtual meter (e.g., at least one virtual meter located at a virtual metering location V1) may include data from one or more unmetered branch circuits that are inherently aggregated into a single representative circuit.

[0254] Fig.30F The electrical system shown in is an example of a "partially metered" system, in which only a subset of the total circuits are monitored by physical IEDs (here, at least one first IED and at least one second IED). According to aspects of the present disclosure, a dynamic tolerance curve can be developed independently for each physical metering point (M1, M2) without relying on (multiple) external inputs from other IEDs. In addition, according to aspects of the present disclosure, the dynamic tolerance curve of the virtual metering point (V1) is derived using selected synchronous (or pseudo-synchronous) and complementary data (e.g., power, energy, voltage, current, harmonics, etc.) from the physical IEDs (here, at least one first IED and at least one second IED), and is dependent (sometimes, completely dependent) on these devices (here, at least one first IED and at least one second IED). For example, briefly returning to Figures 30C-30E The dynamic tolerance curve of the virtual metering point V1 can be derived from the dynamic tolerance curve data of the physical metering points M1 and M2 (for example, respectively as Fig. 30C and 30D Due to this dependency, it can be understood that in the illustrated embodiment, problems (e.g., accuracy, missing data, asynchronous data, etc.) of at least one first IED and at least one second IED will be reflected in the final virtual meter data. In the illustrated embodiment, the dynamic tolerance curve of the virtual meter point V1 can be compared with Fig.30E The dynamic tolerance curves shown are the same (or similar).

[0255] refer to Figure 30G, another example electrical system comprises at least one virtual meter located at a "virtual" metering location V1, at least one first IED installed at a first "physical" metering location M1, and at least one second IED installed at a second "physical" metering location M2. The at least one virtual meter is a so-called "parent device" or "virtual parent device", and the at least one first IED and the at least one second meter are "child devices". In the example embodiment shown, the at least one first IED and the at least one second IED are children of the at least one virtual meter (and are therefore considered to be brothers of each other).

[0256] As shown, at least one first IED and at least one second IED are both installed (or located) at respective metering locations M1, M2 in the electrical system, which "branch" from a common point (here, connection 1) associated with a virtual metering location V1 from which at least one virtual meter is derived (or located). Connection 1 is a physical point in the electrical system where energy flow (as calculated at V1) diverges to provide energy to left and right branches (as measured by at least one first IED and at least one second IED at M1 and M2, respectively).

[0257] According to an embodiment of the present disclosure, for example, by combining with the above Fig.30F A slightly different method is described for creating / deriving electrical measurement data associated with a first metering location V1. In particular, the electrical measurement data associated with the first metering location V1 may be determined by calculating the sum of synchronous (or pseudo-synchronous) data from at least one first sub-IED installed at the metering location M1 and at least one second sub-IED installed at the metering location M2 (e.g., for synchronous or pseudo-synchronous data, V1=M1+M2).

[0258] Figure 30G The electrical system shown is an example of a "partial metering" system, where only a subset of the total circuits are monitored by physical IEDs. According to aspects of the present disclosure, dynamic tolerance curves can be developed independently for each physical metering point (M1, M2), without relying on (multiple) external inputs from other IEDs. In addition, according to aspects of the present disclosure, the dynamic tolerance curves of the virtual parent meter (V1) are derived using selected complementary data (e.g., power, energy, voltage, current harmonics, etc.) from the physical IEDs (M1, M2) and are completely dependent on these devices (M1, M2). Due to this dependency, it should be understood that any problems with meters M1 and M2 (e.g., accuracy, missing data, unsynchronized data, etc.) will be reflected in the virtual parent device V1.

[0259] refer to Fig. 30H, another example electrical system includes at least one first virtual meter located at a first "virtual" metering location V1, at least one first IED installed at a first "physical" metering location M1, and at least one second virtual meter installed at a second "virtual" metering location V2. The at least one virtual meter is a "parent device" or "virtual parent device", and the at least one first IED and the at least one second virtual meter are "child devices". In the example embodiment shown, the at least one first IED and the at least one second virtual meter are children of the at least one first virtual meter (and are therefore considered to be brothers of each other).

[0260] As shown, at least one first IED and at least one second virtual meter are installed and derived at respective meter locations M1, V2 in the electrical system, which "branch" from a common point (here, connection 1) associated with the first virtual meter location V1 where the at least one first virtual meter is located (or derived). Connection 1 is a physical point in the electrical system where the energy flow (as calculated at V1) diverges to provide energy to the left and right branches (as measured by the at least one first IED at M1 and as calculated at V2).

[0261] According to some embodiments of the present disclosure, Fig. 30H The electrical system shown is mathematically and probabilistically uncertain because there are too many unknown values ​​from the necessary inputs. Assumptions can be made about the occurrence of power quality events (e.g., voltage events) at the virtual devices (V1, V2); however, in this case, the impact of the power quality events may be undefined (or extremely difficult to define). It will be understood from the above and following discussions that the virtual metering data is derived from the data obtained from the physical IEDs. Fig. 30H In the embodiment shown, there are too few physical IEDs to derive "virtual" data. Fig. 30H Some constraints related to virtual IED derivation are shown.

[0262] refer to Fig. 30I, yet another example electrical system includes at least four virtual meters (or IEDs) located at (or derived from) corresponding "virtual" metering locations (V1, V2, V3, V4) in the electrical system, and at least five IEDs installed at corresponding "physical" metering locations (M1, M2, M3, M4, M5) in the electrical system. Specifically, the electrical system includes at least one first "parent" virtual meter located at a first "virtual" metering location V1, at least one first "child" IED installed at a first "physical" metering location M1, and at least one second "child" IED installed at a second "physical" metering location M2 (the at least one first IED at the metering location M1 and the at least one second IED at the metering location M2 are children of the at least one first virtual meter at the metering location / location V1). The electrical system also includes at least one third "child" IED installed at a third "physical" metering location M3 and at least one second "child" virtual meter located at a second "virtual" metering location V2 (the at least one third IED at the metering location M3 and the at least one second virtual meter at the metering location V2 are children of the at least one first IED at the metering location M1).

[0263] The electrical system also includes at least one fourth "sub" IED installed at a fourth "physical" metering location M4 and at least one third "sub" virtual meter located at a third "virtual" metering location V3 (the at least one fourth IED at the metering location M4 and the at least one third virtual meter at the metering location V3 are children of the at least one second virtual meter at the metering location V2). The electrical system also includes at least one fifth "sub" IED installed at a fifth "physical" metering location M5 and at least one fourth "sub" virtual meter located at a fourth "virtual" metering location V4 (the at least one fifth IED at the metering location M5 and the at least one fourth virtual meter at the metering location V4 are children of the at least one third virtual meter at the metering location V3). As shown in the figure, there are basically five layers in the metering hierarchy from the first virtual metering location V1 to the fifth "physical" metering location M5 and the fourth "virtual" metering location V4.

[0264] Fig. 30IThe electrical system shown illustrates a partially metered system, where only a subset of the total circuits are monitored by physical devices / IEDs. According to aspects of the present disclosure, dynamic tolerance curves can be independently developed for each physical metering location (M1, M2, M3, M4, M5) without reliance or interdependence on (multiple) external inputs from other IEDs. The dynamic tolerance curves of the virtual metering locations V1, V2, V3, V4 can be derived from complementary and synchronous (or pseudo-synchronous) data (e.g., power, energy, voltage, current, harmonics, etc.) as measured by the physical IEDs installed at the discrete metering locations M1, M2, M3, M4, M5. In addition, electrical measurement data from energy-related signals captured by at least one second IED installed at the second metering location M2 can be used to generate a dynamic tolerance curve for the metering location M2 without any input (or data) from at least one first IED or at least one third IED.

[0265] In particular, by calculating the sum of the synchronization (or pseudo-synchronization) data from at least one first sub-IED installed at the metering location M1 and at least one second sub-IED installed at the metering location M2 (e.g., for the synchronization or pseudo-synchronization data, V1=M1+M2), the electrical measurement data associated with the first virtual metering location V1 can be determined (and used to help generate the dynamic tolerance curve of the first virtual metering location V1). In addition, by calculating the difference between the synchronization (or pseudo-synchronization) data from at least one first sub-IED installed at the metering location M1 and at least one third sub-IED installed at the metering location M3, the electrical measurement data associated with the second metering location V2 can be determined (e.g., for the synchronization or pseudo-synchronization data, V2=M1-M3) (and used to help generate the dynamic tolerance curve of the second virtual metering location V2).

[0266] By first calculating the difference between synchronization (or pseudo-synchronization) data from at least one first sub-IED installed at the metering location M1 and at least one third sub-IED installed at the metering location M3, and then calculating the difference between the first calculated difference and the synchronization (or pseudo-synchronization) data from at least one fourth sub-IED installed at the metering location M4 (for example, for synchronization or pseudo-synchronization data, V3=M1-M3-M4), electrical measurement data associated with the third virtual metering location V3 can be determined (and used to help generate a dynamic tolerance curve for the third virtual metering location V3).

[0267] Furthermore, by first calculating the difference between the synchronization (or pseudo-synchronization) data from at least one first sub-IED installed at the metering location M1 and at least one third sub-IED installed at the metering location M3, and then calculating the difference between the synchronization (or pseudo-synchronization) data from at least one fourth sub-IED installed at the metering location M4 and at least one fifth sub-IED installed at the metering location M5, the electrical measurement data associated with the fourth virtual metering location V4 can be determined (and used to help generate a dynamic tolerance curve for the fourth virtual metering location V4). The difference between the first calculated difference and the calculated difference between the synchronization (or pseudo-synchronization) data from at least one fourth sub-IED installed at the metering location M4 and at least one fifth sub-IED installed at the metering location M5 can be used to determine the electrical measurement data associated with the fourth virtual metering location V4 (e.g., for synchronization or pseudo-synchronization data, V4=M1-M3-M4-M5).

[0268] It will be further understood from the following discussion that using event triggers or alarms from one or more physical IEDs (M1, M2, M3, M4, M5), pre-event and post-event data from the physical IEDs can be used to develop dynamic tolerance curves, determine event impacts, quantify recovery times, and evaluate other associated costs of virtual meters (and metering locations V1, V2, V3, V4). Similarly, the validity of the exported information of the virtual meters (V1, V2, V3, V4) depends on the authenticity, accuracy, synchronization, and availability of the data from the physical IEDs (M1, M2, M3, M4, M5). In this particular case, there are many interdependencies for exporting data for the virtual meters (and metering locations V1, V2, V3, V4), so it should be understood that one or more of the exports may experience some defects.

[0269] It should be understood that the above examples for determining, deriving and / or generating dynamic tolerance curves for virtual meters in an electrical system can also be applied to aggregation of zones and systems. In the spirit of describing concepts such as "operational impact", "recovery time", "renewable energy cost", etc., it should be understood that aggregation only makes sense if the aggregation is 1) directly useful to the customer / energy consumer, and 2) and / or is used to provide additional customer and / or business-centric benefits (current or future). This is why the best approach to aggregation is often to focus on worst-case scenarios (i.e., event impact, event recovery time, other related event costs, etc.). If aggregation is performed and it does not reflect the customer's experience trying to resolve problem events, it is difficult to gain any usefulness from the aggregation. In short, just because something is mathematically and / or statistically feasible, does not necessarily make it useful.

[0270] III. Evaluating Load Impact and Recovery Time Using Tier and Dynamic Tolerance Curve Data

[0271] In embodiments, when a load-affected voltage event occurs, it is important for energy consumers (or systems and methods disclosed herein) to prioritize the response of "what, when, why, where, who, how / how much / how fast, etc." More specifically: 1) what happened, 2) when did it happen, 3) why did it happen, 4) where did it happen, 5) who is responsible, 6) how do I fix it, 7) how much will it cost, and 8) how soon can I fix it. Embodiments described herein help energy consumers answer these questions.

[0272] Understanding and quantifying the impact of voltage (and / or other) events from an IED, zone, and / or system perspective is extremely important for energy consumers to fully understand the operation of their electrical systems and facilities and to respond to electrical events accordingly. Because each load has unique operating characteristics, electrical characteristics and ratings, functions, etc., the impact of voltage events may vary from load to load. This may result in unpredictable behavior even if comparable loads are connected to the same electrical system and adjacent to each other. It should be understood that some aspects of the embodiments described below may refer to or overlap with previously discussed ideas presented herein.

[0273] The system (or hierarchical) perspective shows how the electrical system or metering system is interconnected. When a voltage event occurs, its impact is strongly affected by the system impedance and sensitivity of a given load. For example, Fig.31 A relatively simple fully metered electrical system experiencing a voltage event (e.g., due to a fault) is shown. In general, the system impedance will determine the magnitude of the fault, the protection equipment will determine the duration of the fault (clearing time), and the location of the fault will be an important factor in the extent of the fault's impact on the electrical system. Fig.31 In the example, the shaded area may (even likely) experience a significant voltage sag followed by an outage (due to the operation of the protection device(s)). In an embodiment, the duration of the event impact will start from the start time of the fault until the system is operating normally again (note: the example states a recovery time of 8 hours). Fig.31 Unshaded areas of the electrical system may also experience voltage events due to faults; however, the recovery time of the unshaded areas may be shorter than that of the shaded areas.

[0274] In an embodiment, Fig.31 Both shaded and unshaded areas of the electrical system shown may be affected by a fault; however, the two may exhibit different restoration durations. If the processes served by both the shaded and unshaded areas are critical to the operation of the facility, then the system restoration time will be equal to the greater of the two restoration times.

[0275] In an embodiment, it is important to identify and prioritize IEDs, zones, and / or systems. Zones can be determined at the electrical system level based on factors such as protection schemes (e.g., each circuit breaker protects a certain zone, etc.), separately derived sources (e.g., transformers, generators, etc.), processes or subsystems, load types, sub-billing groups or tenants, network communication schemes (e.g., IP addresses, etc.), or any other logical classification. Each zone is a subset of the metering system hierarchy, each zone can be prioritized by type, and if applicable, each zone can be assigned more than one priority (e.g., high priority load types with low priority processes). For example, if a protection device also acts as an IED and is incorporated into the metering system, it and the devices below it can be considered as one zone. If the protection devices are layered in a coordinated scheme, the zones will be similarly layered to correspond to the protection devices. In Fig.32 Another approach to automatically determining zones involves utilizing hierarchical context to evaluate voltage, current, and / or power data (other parameters may also be used as necessary) to identify transformer locations. Fig.32 Three zones are indicated: Common Source, Transformer 1, and Transformer 2. Fig.33 is an exemplary illustration of a customized zone configuration for an energy consumer.

[0276] Once the zones are established, prioritizing each zone will help energy consumers better respond to voltage events (or any other event) and their impacts. While there are techniques to automatically prioritize zones (e.g., from largest to smallest load, load type, recovery time, etc.), the most prudent approach will be for the energy consumer to rank the priority of each zone. It is certainly feasible (and expected) for two or more zones to have equal rankings in priority. Once the zone priorities are established, the load impact and recovery time of voltage events can then be analyzed from a zone perspective. Again, all of this can be automated using the techniques described above for establishing zones, prioritizing based on the historical impact of voltage events within the electrical system, and providing an analytical summary to the energy consumer based on these classifications.

[0277] Zoning also helps identify practical and cost-effective ways to mitigate voltage events (or other power quality issues). Because mitigation options can range from system-wide to targeted, it is beneficial to evaluate mitigation opportunities in the same way. For example, as above Fig.21 As shown, the mitigation of voltage events becomes more expensive as the proposed solution is brought closer to the electrical main switchgear.

[0278] In an embodiment, evaluating zones to identify mitigation opportunities for voltage events can produce a more balanced, more economical solution. For example, one zone may be more sensitive to voltage events than another zone (e.g., perhaps due to local motor startup). It is possible to provide electrical service to sensitive loads from another zone. Alternatively, it may be wise to move the cause of the voltage event (e.g., a local motor) to another service point in another zone.

[0279] Yet another example benefit of evaluating zones is the ability to prioritize capital expenditure (CAPEX) investments for voltage event mitigation based on the priority of each respective zone. Assuming the zones have been appropriately prioritized / ranked, important metrics such as percentage of load affected (relative), total load affected (absolute), worst case severity, restoration time, etc. can be aggregated over time to indicate the best solution and location for mitigation equipment. Using aggregated zone voltage tolerance data from IEDs within a zone can provide a "best" solution for the entire zone, or target a solution for one or more loads within a zone.

[0280] IV. IED alarm management using dynamic tolerance curves and related impact data

[0281] As mentioned above, each location in an electrical system / network typically has a unique voltage event tolerance characteristic. Dynamically (continuously) generating different voltage event tolerance characteristics for one or more metering points in an electrical system provides many benefits, including better understanding of the behavior of the electrical system at the metering point, appropriate and economical techniques for mitigating voltage anomalies, verification that installed mitigation equipment meets its design standards, and utilizing non-power quality IEDs to help characterize voltage event tolerances.

[0282] Another example advantage of characterizing voltage event tolerance at IED points is to customize alarms at the point of installation of the IED. Using dynamic voltage event characterization to manage alarms has several benefits, including ensuring 1) capturing relevant events, 2) preventing excessive alarms (better "alarm effectiveness"), 3) configuring appropriate alarms, and 4) prioritizing important alarms.

[0283] Existing approaches to alarm configuration and management typically include:

[0284] Energy consumers manually configure based on standards, recommendations or guesswork.

[0285] Some form of set point learning, which requires configuring a “learning period” to determine what is normal. Unfortunately, if an event occurs during a learning period, it will be considered normal behavior unless the energy consumer catches on to it and ignores that data point.

[0286] A “catch-all” approach, which requires energy consumers to apply filters to distinguish which alarms are important and which are not.

[0287] In short, energy consumers (who may not be experts) may need to proactively discern which event alarms / thresholds are important, either before or after they are captured in a "live system".

[0288] Currently, IED voltage event alarms have two important thresholds, which are usually configured as: 1) magnitude, and 2) duration (sometimes called alarm hysteresis). Equipment / loads are designed to operate at a given optimal voltage magnitude (i.e., rated voltage) defined by an acceptable range of voltage magnitudes. In addition, loads may operate outside of the acceptable voltage range, but only for a short period of time (i.e., duration).

[0289] For example, a power supply may be rated for a voltage amplitude of 120 volts rms ±10% (i.e. ±12 volts rms). Therefore, the power supply manufacturer specifies that the power supply should not be operated continuously outside the 108-132 volts rms range. More precisely, the manufacturer makes no promises about the performance or damage susceptibility of the power supply outside the specified voltage range. Less obvious is how the power supply performs during momentary (or longer) voltage excursions / events outside the specified voltage range. Power supplies may provide some voltage ride-through due to their inherent energy storage capabilities. The length of the voltage ride-through depends on many factors, primarily the amount / number of loads connected to the power supply during the voltage excursion / event. The greater the load on the power supply, the shorter the power supply's ability to ride through the voltage excursion / event. In summary, this confirms two parameters (voltage amplitude and duration during a voltage event), which happen to be the same two parameters exemplified in the basic voltage tolerance curve. It also validates load as an additional parameter that can be considered when focusing on the effects of voltage events and IED alarm thresholds.

[0290] In an embodiment of the present disclosure, the voltage magnitude alarm threshold of an IED device may be initially configured with a reasonable set point value (e.g., 5% of the rated voltage of the load). The corresponding duration threshold may be initially configured to zero seconds (highest duration sensitivity). Alternatively, the voltage magnitude alarm threshold of an IED device may be configured to any voltage excursion above or below the rated voltage of the load (highest magnitude sensitivity). Likewise, the corresponding duration threshold (alarm hysteresis) may be initially configured to zero seconds (highest sensitivity).

[0291] When the metered voltage deviates from the voltage alarm threshold (regardless of its configured set point), the IED device may alarm about the voltage disturbance event. The IED may capture characteristics related to the voltage event, such as voltage amplitude, timestamp, event duration, relevant pre-event / during-event / post-event electrical parameters and characteristics, waveforms and waveform characteristics, and / or any other monitoring system indications or parameters that the IED is capable of capturing (e.g., I / O status location, relevant timestamps, consistent data from other IEDs, etc.).

[0292] Voltage events can be evaluated to determine / verify whether there is a meaningful discrepancy between pre-event electrical parameter values ​​(e.g., load, energy, phase imbalance, current, etc.) and their corresponding post-event values. If no discrepancy exists (pre-event vs. post-event), the voltage event can be considered "no impact," meaning there is no indication that the operation and / or equipment of the energy consumer was functionally affected by the voltage event. The voltage event data may still be retained in memory; however, when the voltage event is captured by the IED, it may be classified as having no impact on the operation of the energy consumer. The existing voltage alarm magnitude and duration threshold set points may then be reconfigured to the magnitude and duration of the no-impact event (i.e., reconfigured to less sensitive set points). Ultimately, in an embodiment, more severe voltage events that do not indicate any operational and / or equipment functional impact at the IED point will become the new voltage magnitude and duration thresholds for the voltage event alarm of the corresponding IED.

[0293] If a pre-event vs. post-event difference does exist, the voltage event may be deemed "impactful," meaning there is at least one indication that the energy consumer's operations and / or equipment were functionally impacted by the voltage event. Voltage event data may be retained in memory, including all measured / calculated data and metrics associated with the impactful event (e.g., percentage affected, absolute impact, voltage magnitude, event duration, etc.). In addition, additional relevant data associated with the voltage event may be appended to the voltage event data record / file at a later time (e.g., calculated event recovery time, additional voltage event information from other IEDs, determined event source location, metadata, IED data, other electrical parameters, updated historical standards, statistical analysis, etc.). Because the voltage event is determined to be "impactful," the voltage alarm magnitude and duration threshold set points remain unchanged to ensure that less severe, but still impactful, events continue to be captured by the IEDs at the corresponding installation points within the electrical system.

[0294] In an embodiment, the end result of this process is that the discrete IED device generates a customized voltage alarm template at the installation point, which indicates the voltage events (and their respective characteristics) that produce impact events and / or distinguishes between impact voltage events and non-impact voltage events. As more voltage events occur, the customized voltage alarm template more accurately represents the true voltage event sensitivity of the IED installation point. In an embodiment, any (or substantially any) voltage event that exceeds any standardized or customized threshold can be captured; however, the energy consumer can choose to prioritize impact events as a unique category of alarms / indicators. For example, this can be used to minimize the flooding of redundant voltage alarms in the energy consumer's monitoring system by only notifying the prioritized alarms that are considered to indicate that an impact has occurred.

[0295] As noted above in conjunction with other embodiments of the present disclosure, the tailored voltage tolerance curve constructed for customizing voltage event alarm notifications can also be used to recommend mitigation equipment to improve the ride-through characteristics of IED installation points. If the energy consumer installs mitigation equipment, the system can provide / detect manual or automatic indications, so that a new version of the voltage tolerance template can be created based on system modifications (e.g., mitigation equipment installation). In an embodiment, a practical method can be a manual indication of supplementary mitigation equipment added to the system; however, for example, automatic indications can also be provided based on "uncharacteristic changes" in the response of the electrical system to voltage events at the installation point of the IED. These "uncharacteristic changes" can be established, for example, by statistically evaluating (e.g., via an analytical (analytics) algorithm) one or more electrical parameters (i.e., voltage, current, impedance, load, waveform distortion, etc.). In an embodiment, they can also be identified by any sudden changes in the voltage event ride-through at the IED installation point. Inquiries can be made to energy consumers or electrical system managers to verify any additions, eliminations, or changes to the electrical network. Feedback from energy consumers can be used to better refine any statistical evaluations (e.g., analytical algorithms) associated with voltage events (or other metering characteristics). Historical information (including customized voltage tolerance curves) will be retained for a variety of evaluations, such as validating the effectiveness of mitigation techniques, the impact of new equipment installations on voltage ride-through characteristics, etc.

[0296] As part of this embodiment, more than two event parameters may be used to configure thresholds to trigger an alarm for a voltage event. In the above description, the magnitude of the voltage deviation and the duration of the voltage event are used to configure and trigger a voltage event alarm. In an embodiment, more dimensions, such as load impact and / or recovery time, may also be included to configure a voltage event alarm. Just as the voltage event set point threshold may be set to an alarm only when any load is affected, the voltage event set point threshold may also be configured to allow a certain level of impact on the load. Through manual or automatic load identification (based on electrical parameter identification), an alarm may only be triggered when certain types of loads are affected by a voltage event. For example, some loads have certain signatures, such as elevated levels of specific harmonic frequencies. In an embodiment, if those specific harmonic frequencies are no longer apparent, a voltage event alarm may be triggered.

[0297] Other parameters can be used to customize the alarm template. For example, an energy consumer may only be interested in voltage events with a recovery time greater than 5 minutes. The voltage event characteristics that usually generate a recovery time of less than 5 minutes can be filtered out using historical event data to configure the alarm template accordingly. In addition, an energy consumer may only be interested in voltage events that generate a monetary loss greater than $500. Similarly, historical data can be used to filter out voltage event characteristics that usually generate a monetary loss of less than $500 to configure the alarm template accordingly. Obviously, any other useful parameters derived from the voltage event characteristics can be similarly used to tailor and provide actual alarm configurations. Multiple parameters (e.g., recovery time>5 minutes, and monetary loss>$500) can also be used simultaneously to provide more complex alarm schemes and templates, and so on.

[0298] In an embodiment, as more voltage events occur, additional voltage pre-event / during-event / post-event attributes and parameters are captured at both the discrete and system levels and integrated into a typical historical characterization (historical standard). For example, such additional characterization of voltage events can be used to estimate / predict expected recovery times from both the discrete and system levels. Furthermore, energy consumers can be advised on how to achieve faster recovery times based on historical event data regarding effective sequencing of re-powering loads.

[0299] In an embodiment, customer alarm prioritization (for voltage events or any other event type) may be performed based on load levels measured at one or more discrete metering / IED points within the electrical system. When an indication is received from a metering / virtual / IED point that one or more loads have changed (or are operating atypically), the voltage event alarm set point thresholds may be re-evaluated and modified based on the load levels measured at one or more discrete points (or based on the atypical operation of the loads). For example, when one or more IEDs indicate that the measured load is low (indicating that the facility is offline), it may be advantageous to null, silence, or reduce the priority of the voltage event alarm. Conversely, when one or more IEDs indicate that additional loads are starting up, it would be wise to increase the priority of the voltage event alarm.

[0300] As mentioned earlier in this section, in an embodiment, this feature can be used to prioritize alarms (including voltage event alarms). The IED can be configured to capture data related to substantially any perceptible voltage variation from the nominal voltage (or (multiple) load rated voltages) at the installation point and take (multiple) actions including storage, processing, analysis, display, control, aggregation, etc. In addition, the same (multiple) actions can be performed on substantially any alarm (including voltage event alarms) that exceeds a certain predefined set point / threshold, such as those defined by dynamic voltage tolerance curves, (multiple) standards, or other recommendations (such as derived from any number or combination of electrical parameters, I / O, metadata, IED characteristics, etc.). In embodiments, any or all captured events (including voltage events) may then be analyzed to automatically prioritize alarms at a discrete, zonal, and / or system level based on any number of parameters, including: alarm type, alarm description, alarm time, alarm magnitude, phase(s) affected, alarm duration, restoration time, waveform characteristics, load impact associated with the alarm, location, tier aspects, metadata, IED characteristics, load type, customer type, economic aspects, relative importance to operations or loads, and / or any other variable, parameter, or combination thereof relevant to events (including voltage events) and the operation of energy consumers. Prioritization may be related to inherent characteristics of discrete events, or involve comparison of more than one event (including voltage events), and may be performed at the onset of an event, postponed to a later time, or based on the aforementioned parameters. In embodiments, prioritization may be performed interactively with energy consumers, automatically, or both interactively and automatically, with the goal of promoting the preferences of energy consumers.

[0301] In an embodiment, the parameters to be considered may include at least electrical data (from at least one phase), control data, time data, metadata, IED data, operational data, customer data, load data, configuration and installation data, energy consumer preferences, historical data, statistical and analytical data, economic data, material data, any derived / developed data, etc.

[0302] For example, Fig.34 A relatively simple voltage tolerance curve for an IED is shown, with the voltage alarm threshold set to ±10% of the nominal voltage for any event ranging from 1 microsecond to steady state. Fig.35 In the example, a voltage sag event occurs at the IED, which sags to 50% of the nominal voltage and lasts for a duration of 3 milliseconds. The pre-event / during / post-event analysis of the event shows that no load is affected. In an embodiment, because no load is affected, the voltage event is considered to be a load event when (sometimes, only when) the magnitude and duration of the voltage event are greater than Fig.35 When the events described in are more severe, the alarm set point thresholds in the IED are reconfigured to indicate / prioritize the occurrence of voltage events. Fig.36 The change to the original voltage tolerance curve is shown. In short, the voltage event occurring in region R of the figure is expected to have no effect, while the voltage event occurring in region G of the figure may have an effect or may have no effect. Fig.37 In the example above, another voltage event occurs and is captured by the same IED. In this second voltage event, a voltage interruption (to 0% of nominal voltage) occurs and lasts for a duration of 1 millisecond. Again, the pre-event / during-event / post-event analysis of the second event shows that no loads were affected. And again, the voltage event is captured when (sometimes, only when) the magnitude and duration of the voltage event are greater than Fig.36 When the events described in are more severe, the alarm set point thresholds in the IED are reconfigured to indicate / prioritize the occurrence of voltage events. Fig.38 Changes to the original voltage tolerance curve are shown.

[0303] exist Fig.39 In the example above, a third voltage event occurs and is captured by the IED. In this third voltage event, the voltage dips to 30% of the nominal voltage for a duration of 2 milliseconds. This time, the pre-event / during-event / post-event analysis of the third event shows that 25% of the load was affected (e.g., disconnected) at 30% of the nominal voltage. Subsequently, the alarm setpoint threshold remains unchanged because the impact on the load is 25% (i.e., the load impact occurred where it was expected to occur). Fig.40The final setting of the voltage event alarm threshold after these three voltage events is shown. Note that the third event is not shown on the figure because the purpose of this embodiment of the present disclosure is to reconfigure / modify the voltage event set point threshold. The energy consumer can be notified of the occurrence of the third event, and the voltage event data, calculations, derivations and any analysis can be stored for future reference / benefit.

[0304] V. Evaluate and quantify the impact of voltage events on energy and demand

[0305] Establishing the losses incurred due to voltage events is often complex; however, embodiments of the present disclosure provide interesting metrics (or metrics) to help quantify the contribution of energy and demand to total losses. When a voltage event occurs, facility processes and / or equipment may trip offline. The activity of restarting the processes and / or equipment consumes energy and (in some cases) creates peak demand on the facility. Although these costs are often overlooked, they can be considerable over time, but contribute little to the actual production and profitability of the facility operation. There may be ways to recover some of these costs through insurance policy coverage, tax write-offs in certain jurisdictions, or even peak demand "forgiveness" from the utility. Perhaps most importantly, quantifying the financial impact of voltage events on utility bills can provide an incentive to mitigate voltage events that result in these unanticipated and potentially damaging losses.

[0306] When a voltage event occurs, the above analysis may be performed to determine the level of impact on the load or operation. If no evidence of impact on the load, process and / or system is found, this aspect of this embodiment of the present disclosure may be ignored. If it is found that the voltage event affects the load, process and / or system, a pre-event / during-event / post-event analysis of the electrical parameters is performed. The recovery time clock starts, and this embodiment of the present disclosure classifies energy consumption, demand, power factor, and any other parameters related to the utility billing structure as being associated with the recovery time interval. These parameters may be evaluated and analyzed to determine discrete, zone and / or system metrics (including aggregation), comparisons with historical event metrics, incremental energy / demand / power factor costs, etc. These metrics may be evaluated against the local utility rate structure to calculate the total energy-related recovery cost, the most sensitive and costly discrete, zone, and / or system for target mitigation during the recovery period, expectations based on historical voltage event data (e.g., number of events, event recovery period, event energy cost, etc.), opportunities for operational / procedural improvement of voltage event response time, and the like.

[0307] In embodiments, data and analysis collected before, during, and / or after the restoration period may be filtered, truncated, summarized, etc. to help energy consumers better understand the impact of voltage events (or other events) on their electrical systems, processes, operations, response times, procedures, costs, equipment, productivity, or any other relevant aspect of their business operations. It may also provide a useful summary (or detailed report) for discussions with utilities, management, engineering, maintenance, accounting / budgeting, or any other relevant party.

[0308] VI. Using recovery time to decompose typical and atypical operating data

[0309] It is important to recognize that the operation of a facility during a recovery period is often abnormal or atypical compared to non-recovery time (i.e., normal operation). Identifying, "marking" (i.e., representing), and / or distinguishing abnormal or atypical operational data from normal operational data (i.e., non-recovery data) is useful for performing calculations, metrics, analysis, statistical evaluations, etc. Metering / monitoring systems do not inherently distinguish between abnormal operational data and normal operational data. Distinguishing and marking operational data as abnormal (i.e., due to being in recovery mode) or normal provides several advantages, including, but not limited to:

[0310] 1. Analysis (such as described above) may assume uniformity of operation across all data; however, it is useful to separate abnormal or atypical operating modes from normal operating modes in order to better assess and understand the significance of the data being analyzed. Data analysis is improved by providing two different categories of operation; normal and abnormal / atypical. Each can be automatically and independently analyzed to provide unique and / or more accurate information about each operating mode within a facility or system. Distinguishing between normal operating data and atypical operating data (i.e., due to voltage events) further supports decisions based on the conclusions of the analysis.

[0311] 2. Distinguishing between normal and abnormal operating modes makes it possible to provide discrete baseline information for each operating mode. This provides the ability to better standardize operating data, as atypical data can be excluded from system data analysis. In addition, abnormal operating modes can be analyzed to help understand, quantify, and ultimately mitigate the impacts associated with influential voltage events. In the case of event mitigation, data analysis of abnormal operating periods will help identify potentially more effective and / or more economical methods of reducing the impact of voltage events.

[0312] 3. Losses incurred due to voltage events are often difficult to establish; however, assessing tagged (i.e., partitioned, represented, etc.) data as abnormal / atypical can be used to identify energy consumption anomalies associated with voltage events. This information can be used to help quantify the energy and demand contribution of the event to total losses. When a voltage event occurs, equipment may be inadvertently tripped offline. The process of restarting equipment and processes consumes energy and may (in some cases) create new peak demands on the facility. Although these costs are often overlooked / omitted, they can be considerable over time, but contribute little to the actual production and profitability of the operation. There may be ways to recover some of these costs through insurance policy coverage, tax write-offs in certain jurisdictions, or even peak demand "forgiveness" from utilities. Perhaps most importantly, quantifying the financial impact of voltage events on utility bills can provide incentives to mitigate voltage events that result in these unanticipated and potentially impactful losses.

[0313] VII. Other Assessments and Metrics Related to Voltage Event Impact and Recovery Time

[0314] As is well known, voltage events including outages are the leading global cause of business interruption-related losses. Based on a study by Allianz Global, the estimated annual economic losses for medium and large enterprises are estimated to be between $104 billion and $164 billion. In an embodiment, by combining additional economic metadata, individual voltage events can be evaluated to determine the monetary impact of these events. In addition, in an embodiment, the voltage event impact can be aggregated by aggregating data and information from a single event. Some examples of useful financial information that help quantify the economic impact of voltage events include: average material loss / event / hour, utility rate tariffs (as described above), average production loss cost / event / hour, estimated equipment loss / event / hour, average third-party cost / event / hour, or any other monetary measure related to downtime cost based on per event or per day / hour / minute. Using the recovery time from the above calculation, a measure can be determined for substantially any loss that has been quantified monetaryly. These measures can be determined at discrete IED, zone and / or system levels accordingly.

[0315] This article describes a number of new voltage event-related indices as useful metrics for qualifying and quantifying voltage events and anomalies. Although these new indices focus on voltage sags, in embodiments, they can also be considered for any other voltage event or power quality event category. Example indices include:

[0316] ○ Mean Time Between Events (MTBE)As used herein, the term "MTBE" is used to describe the average or expected time that a system or portion of a system operates between an event and the recovery time following the event. This includes both impact and non-impact events, so there may or may not be a certain amount of recovery time associated with each event.

[0317] ○ Mean Time Between Impactful Events (MTBIE) As used herein, the term "MTBIE" is used to describe the average or expected time that a system or portion of a system operates between an event and the recovery time following the event. In an embodiment, this metric is limited to impactful events only, and there may be a certain amount of recovery time associated with each event.

[0318] ○ Mean restart time Time to Restart, MTTR) As used herein, the term "MTTR" is used to describe the average time it takes to restart production at a system or portion (e.g., load, zone, etc.) of a system. This "mean time" includes all (or substantially all) factors involved in restarting production, including (but not limited to): repair, reconfiguration, reset, reinitialization, review, retest, recalibration, restart, replacement, retraining, repositioning, revalidation, and any other aspect / function / work that affects the time to resume operations.

[0319] ○ Sag rate As used herein, the term "sag rate" is used to describe the average number of voltage sag events for a system or portion of a system over a given period of time, such as an hour, month, year, or other period of time.

[0320] ○ Production Availability As used herein, the term "production availability" generally refers to production readiness and is defined as the ability of a facility to perform its required operations at a given time or period. This metric focuses on event-driven (multiple) parameters and can be determined by:

[0321]

[0322] In an embodiment, systems, zones and / or discrete IED points are characterized by their "Number of 9's Production Up-Time", which is an indication of production availability excluding restoration duration. Similar to the Number of 9's in the usual sense, this metric may be determined annually (or normalized to an annual value) to provide an indication or measure of the impact of voltage events (or other events) on operational productivity. This metric may help identify mitigation investment opportunities and prioritize them accordingly.

[0323] In an embodiment, the above metrics may be used to estimate / predict restoration times based on historical restoration time information. For example, the magnitude, duration, location, metadata, IED characterization, or other calculated / derived data and information of a voltage event may be used to facilitate these estimates and predictions. The measurements may be performed and provided to energy consumers at discrete IED points, zones, and / or system levels in the form of one or more reports, texts, emails, audible indications, screens / displays, or by any other interactive means.

[0324] Some examples of supplemental metrics that may be specific to an energy consumer’s operations and that can help prioritize the placement of mitigation equipment, investments, etc., include:

[0325] ○ Average Zonal Interruption Frequency Index (AZIFI). AZIFI is an example metric that can be used to quantify the zones in an electrical system that experience the “most” interruptions. As used herein, AZIFI is defined as:

[0326]

[0327] ○ Zonal Impact Average Interruption Frequency Index (ZIAIFI). ZIAIFI is an example metric that can be used to show trends in zonal interruptions and the number of zones affected in an electrical system. As used herein, ZIAIFI is defined as:

[0328]

[0329] ○ Average Zonal Interruption Duration Index (AZIDI). AZIDI is an example metric that can be used to indicate the overall reliability of a system based on the average of zonal impacts. As used herein, AZIDI is defined as:

[0330]

[0331] ○ Zonal Total Average Interruption Duration Index (ZTAIDI). ZTAIDI is an example metric that can be used to provide an indication of the average recovery period for a zone that has experienced at least one voltage-impacting event. As used herein, ZTAIDI is defined as:

[0332]

[0333] While the foregoing metrics focus on zone-related impacts, in embodiments, some or all of the concepts may be re-used for discrete IED point or (in some cases) system impact metrics. It should be understood that the purpose here is to document examples of the ability to create useful metrics for energy consumers and their operations; not to define every possible metric or combination thereof.

[0334] It should also be understood that each of the above metrics can be further determined and divided appropriately for upstream, downstream, internal (e.g., facilities) and external (e.g., utility) voltage event sources. The latter two mentioned (internal / external) may require a certain level of hierarchical classification of the IED and / or electrical system. For example, other classifications of the hierarchy (e.g., protection layout schemes, separately derived sources, processes or subsystems, load types, sub-billing groups or tenants, network communication schemes, etc.) can be used to create / derive additional useful metrics as needed to better assess the impact of voltage events on the operation of the facility. Outputs from embodiments of the present disclosure may be provided by one or more reports, texts, emails, audible indications, screens / displays, or by any other interactive means. Indications may be provided at IEDs, field software, clouds, gateways, or other monitoring system components and / or accessories. In an embodiment, outputs and indications may be generated by circuit systems and systems according to the present disclosure in response to circuit systems and systems receiving and processing respective inputs.

[0335] VIII. Voltage event recovery status tracking

[0336] An example method for reducing the recovery time period according to the present disclosure includes providing a method for tracking recovery as the recovery progresses. By identifying and monitoring the recovery period through discrete IEDs, zones, levels and / or systems in real time, energy consumers (and the systems and methods disclosed herein) are able to better identify, manage and accelerate the recovery process of events throughout their facilities. Event recovery tracking allows energy consumers to understand the recovery status and make better and faster decisions to promote their recovery. This embodiment will also allow energy consumers to review historical data for recovery improvements, generate and / or update recovery procedures, identify zone recovery limitations, troublesome equipment, etc. to improve future event recovery situations (thereby increasing system uptime and availability). Alarm capabilities can be incorporated into the recovery situation to provide an indication of a constraint location within a zone or facility. Historical recovery metrics or some other configured set points can be used to determine the recovery alarm threshold settings for IEDs, system software and / or clouds, and the output from the embodiments of the present disclosure can be provided through one or more reports, texts, emails, auditory indications, screens / displays, or through any other interactive means.

[0337] IX. Development of various baselines related to voltage events

[0338] Another example method of determining expected restoration time uses factors such as market segmentation and / or customer type, process-based assessments, and / or load and equipment types to determine expected restoration time. For example, by defining restoration time based on these and other factors, a restoration time baseline or reference can be developed with respect to the magnitude, duration, percentage of load affected, and / or any other electrical parameter, metadata, or IED specification of the voltage event. The baseline / reference can be used to set restoration alarm thresholds, evaluate restoration time performance and identify improvement opportunities, estimate actual restoration time and cost vs. expected restoration time and cost, improve the accuracy of influential voltage event estimates, etc. Actual historical voltage event impact and restoration time data can be used to generate relevant models by various means, including statistical analysis (and / or parsing) and evaluation, simple interpolation / extrapolation, and / or any other method that produces (multiple) reasonable typical values. The baseline / reference model can range from simple to complex and can be created or determined for discrete IED locations, zones, or entire systems, and the output from the disclosed embodiments can be provided via one or more reports, texts, emails, audible indications, screens / displays, or via any other interactive means.

[0339] X. Evaluate the similarity of voltage events to identify repetitive behavior

[0340] In an embodiment, evaluating voltage events across an electrical system to examine event similarities may be useful to energy consumers. Similarities may be event time, seasonality, recovery time characteristics, electrical parameter behavior, zone characteristic behavior, operating process behavior, and / or any other significant behavior or commonality. Identifying repeated behaviors and / or commonalities may be an important strategy for prioritizing and addressing the impact of voltage events. In addition, analysis / parsing of historical data may provide the ability to predict system impacts and recovery times due to voltage events after the initial onset of the voltage event.

[0341] XI. Voltage Event Prediction

[0342] As mentioned in the previous embodiments of the present disclosure, it is important to be able to identify beneficial opportunities for mitigating voltage events for energy consumers. Another metric that may be considered is the estimated number of predicted interruptions, the estimated impact, and the total restoration time (and associated costs). In an embodiment, this metric may be extremely useful for planning purposes, supporting capital investment opportunities in voltage event mitigation equipment, and even for predicting the expected savings of installing the mitigation equipment. These predictions may be evaluated at a later point in time to determine their accuracy and to fine-tune the next prediction and expectation.

[0343] XII. Other graphs related to voltage event impact and recovery time

[0344] In addition to the various graphs (or diagrams) discussed in conjunction with the above embodiments, there are other additional useful methods for displaying data related to voltage events. Figures 41-44 The graphs described are merely a few examples of displaying data in a useful format; there may be many other ways to present voltage event data in a meaningful way that can benefit energy consumers. For example, graphs, charts, tables, diagrams, and / or other illustrative techniques may be used to summarize, compare, contrast, verify, rank, trend, demonstrate relationships, explain, and / or the like. These data types may be real-time, historical, modeled, projected, baselined, measured, calculated, statistical, derived, summarized, and / or estimated. Graphs may also be of any dimensionality (e.g., 2-D, 3-D, etc.), color, shading, shape (e.g., line, bar, etc.), and / or the like to provide a unique and useful perspective.

[0345] Fig.41 An example of load impact vs. recovery time for a single event is shown. The solid color filled area represents the normal or expected range of the operating parameter, the diagonal filled area highlights the recovery time period, and the black line is the load over time. Fig.42 An example of a series of impactful events vs. their recovery time from a single IED (multiple IEDs may also be used here) is shown. In this example, solid color filled areas represent normal or expected operating parameters, and diagonal filled areas highlight the period during which the system experienced an impactful event and experienced a recovery period. Fig.43 shows additional data with Fig.41 An example of data integration is shown. In this example, solid color fill areas represent normal or expected ranges for operating parameters, diagonal fill areas highlight recovery time periods, solid lines show loads over time, dashed lines show expected loads over time, and dashed lines show typical pre-event profiles. Based on experience, the behavior of upstream events over time may be less predictable than downstream events. Fig.44 An example of load impact percentage vs. recovery time before / during / after an event of a voltage event is shown. Likewise, different variables, metrics, characteristics, etc. may be plotted, illustrated, etc. as needed or used.

[0346] XIII. Aggregation / consolidation of voltage event impact and recovery time data

[0347] As is well known, voltage events are often widespread, affecting multiple loads, processes, or even entire systems simultaneously. In an embodiment, a metering system according to the present disclosure may display multiple alarms from different IEDs located throughout a facility. For example, a source event often affects the entire system, causing each (or substantially each) capable IED to indicate that an event has occurred.

[0348] In an embodiment, aggregating / merging large amounts of voltage event data, alarms, and impacts across a system is important for several reasons. First, many energy consumers tend to ignore "alarm avalanches" in monitoring systems, so aggregating / merging voltage event data reduces the number of alarms that the energy consumer must review and confirm. Second, data from a string of alarms is often the result of one voltage event from the same root cause. In this case, it is much more efficient to coordinate all of the consistent voltage events captured by multiple IEDs into a single event for coordination. Third, bundled voltage events are easier to analyze than independent voltage events because most of the relevant data and information is available in one place. For the sake of brevity, there are many other reasons for aggregating / merging voltage events that are not listed here.

[0349] The ability to aggregate / consolidate the impact of voltage events and their often accompanying recovery times is important because it helps avoid redundancy in event data. Redundant event data distorts metrics and inflates conclusions, which can lead to flawed decisions. This disclosure focuses on three layers of aggregation / consolidation within an electrical system: IED, zone, and system.

[0350] In an embodiment, the first layer (IED) requires minimal aggregation / merging because the data is acquired from a single point / device and (hopefully) the device should not generate redundant information within itself from the voltage event. In some cases, there may be somewhat redundant alarm information from a single device. For example, a three-phase voltage event may provide an alarm for each of the three phases experiencing the voltage event. In addition, alarms may be triggered for both event pickup and loss, resulting in six total voltage event alarms (pickup and loss alarms for each of the three phases). Although this alarm-rich example may be annoying and confusing, many devices and monitoring systems already aggregate / merge multiple event alarms into a single event alarm, as just described. In some embodiments, a single voltage event alarm may be provided from each IED for each voltage event that occurs in the electrical system.

[0351] As mentioned above, a voltage event often affects multiple IEDs within a monitoring system; especially those that are capable of capturing abnormal voltage conditions. Since zones and systems are often composed of multiple IEDs, the need to aggregate / merge the impact and subsequent effects of voltage events is relevant to both (zones and systems). Although a zone can encompass an entire system, a zone is configured as a subset / subsystem of an electrical and / or metering system. However, since both zones and systems are often composed of multiple devices, they will be treated similarly.

[0352] In an embodiment, there are different methods / techniques to aggregate / merge zones. A first example method includes evaluating the voltage event impact and recovery time from all IEDs within a particular zone, and attributing the most severe impact and recovery time from any single IED within the zone to the entire zone. Because event impact and recovery time are independent variables and therefore may be derived from different IEDs, these two variables should be treated independently of each other. Of course, it is important to track which zone device is considered / believed to have experienced the most severe impact and which zone device has experienced the longest recovery time. The same method can also be used for the system by utilizing the conclusions generated from the zone assessment. Ultimately, the system's recovery time is not considered complete until all relevant IEDs indicate that this is the case.

[0353] A second example method includes evaluating voltage events within a zone by using statistical evaluations of all IEDs from a particular zone (e.g., average impact and average recovery time, etc.). In this case, the severity of the voltage event can be determined for the entire zone by statistically evaluating data from all IEDs and providing results for the entire zone representing each specific voltage event. Statistical determinations including average, standard deviation, correlation, confidence, error, accuracy, precision, bias, coefficient of variation, and any other statistical methods and / or techniques can be used to aggregate / merge data from multiple IEDs into one or more representative values ​​for the zone. The same statistical methods can be used to combine zones into representative metrics / values ​​of system impact and recovery time. Similarly, the recovery time of the system will depend on each relevant IED indicating that the situation is such.

[0354] Another example method of evaluating voltage events is by load type. In an embodiment, an energy consumer (or the system and method disclosed herein) may choose to classify and aggregate / merge loads by similarity (e.g., motors, lighting, etc.), regardless of their location in the electrical system of the facility, and evaluate the impact and recovery time of these loads accordingly. Voltage events may also be evaluated by their respective processes. By aggregating / merging loads associated with the same process (regardless of type, location, etc.), the impact and recovery time of the process may be quantified. Another method of aggregating / merging voltage events is by source and / or separately derived sources. When a voltage event is related to an energy source within a facility (or on an outside utility network), this method will help quantify the impact and recovery time of a voltage event. Other useful logical methods of aggregating / merging voltage event information from two or more IEDs may also be considered (e.g., by building, by product, by cost, by maintenance, etc.).

[0355] In an embodiment, the fundamental purpose of aggregating / merging voltage event data is to identify opportunities to reduce the overall impact of these events on the energy consumer's business to reduce downtime and make it more profitable. One or more of the methods described herein (or a combination of methods) can be used to achieve this goal. It may be useful or even necessary for one or more of these methods to be configured by the energy consumer (or alternative) or the systems and methods disclosed herein. The ability to consider the impact and recovery time of voltage events at discrete IEDs is not mutually exclusive with any method of considering and evaluating the aggregated / merged voltage event impact and recovery time.

[0356] Another interesting prospect is to evaluate the operational performance of an energy consumer after an initial voltage event has occurred. For example, a voltage event may cause one load to trip offline. Shortly thereafter, another related load may also trip offline due to the first load tripping; rather than due to another voltage event. The extent of this chain reaction / propagation will be of interest in determining the outcome of providing ride-through relief to the first load. In this example, it may be wise to provide a timeline of load reactions over a recovery period due to the original voltage event to help minimize the overall impact of the voltage event on the energy consumer's operations.

[0357] In an embodiment, the results of the analysis of the voltage and current data are applied to the point in the network to which the IED capturing the data is connected. Each IED in the network may generally perform a different analysis of the event, assuming each IED is uniquely placed. As used herein, the term "uniquely placed" generally refers to an installation location within an electrical system that affects impedance, metered / connected loads, voltage levels, etc. In some cases, the voltage event data may be interpolated or extrapolated as appropriate.

[0358] In an embodiment, in order to accurately characterize a power quality event (e.g., a voltage sag) and its subsequent (multiple) network impacts, it is important to measure the voltage and current signals associated with the event. The voltage signal can be used to characterize the event, the current signal can be used to quantify the impact of the event, and both the voltage and current can be used to derive other relevant electrical parameters related to the present disclosure. Although the results of the voltage and current data analysis apply to the point in the network to which the IED capturing the data is connected, the voltage event data can also be interpolated and / or extrapolated as appropriate. Given the unique placement of each IED, each IED in the network typically performs a different analysis of the event.

[0359] In embodiments, there are multiple factors that can affect the impact (or lack thereof) of a voltage sag. The impedance of the energy consumer's electrical system can cause a voltage event to produce a more severe voltage sag deep into the system hierarchy (assuming a radial-fed topology). Voltage event magnitude, duration, fault type, operating parameters, event timing, phase angle, load type, and various other factors related to functional, electrical, and even maintenance parameters can all affect the impact of a voltage sag event.

[0360] It should be understood that in some embodiments, any relevant information and / or data derived from the IED, customer type, market segment type, load type, IED capabilities and any other metadata may be stored, analyzed, displayed and / or processed in the cloud, on-site (software and / or gateway) or in the IED.

[0361] refer to Figures 45-48 , several flow charts (or flow block diagrams) are shown to illustrate various methods of the present disclosure. Fig.45 Element 4505 in FIG. 4 is typical), which may be referred to herein as a "processing block" and may represent a computer software and / or IED algorithm instruction or instruction group. Fig.45 4525 in ), which may be referred to herein as a "decision block," represents computer software and / or IED algorithm instructions, or groups of instructions, that affect the execution of computer software and / or IED algorithm instructions represented by a processing block. Processing blocks and decision blocks may represent steps performed by functionally equivalent circuits, such as digital signal processor circuits or application specific integrated circuits (ASICs).

[0362] The flowchart does not depict the syntax of any particular programming language. Instead, the flowchart shows the functional information required by a person of ordinary skill in the art to make circuits to perform the processing required for a particular device or to generate computer software. It should be noted that many conventional program elements, such as loops and initialization of variables and the use of temporary variables, are not shown. It will be understood by a person of ordinary skill in the art that the specific order of the blocks described is illustrative only and may vary, unless otherwise indicated herein. Therefore, unless otherwise indicated, the blocks described below are unordered; this means that, where possible, the blocks can be executed in any convenient or desired order, including that sequential blocks can be executed simultaneously and vice versa. It will also be understood that various features from the flowcharts described below can be combined in some embodiments. Therefore, unless otherwise indicated, features from one of the flowcharts described below can be combined with features of other flowcharts described below, for example, to capture various advantages and aspects of the systems and methods associated with dynamic tolerance curves that the present disclosure seeks to protect.

[0363] refer to Fig.45 , a flowchart illustrating an example method 4500 for managing power quality events (or disturbances) in an electrical system, which method may, for example, be implemented in a processor of an IED (e.g., Figure 1A The method 4500 may also be implemented in a gateway, cloud, field software, etc., away from the IED and / or control system.

[0364] like Fig.45 As shown, method 4500 begins at block 4505, where an IED (and / or a control system) coupled to the load measures a signal related to one or more loads (e.g., Figure 1A 111 ) and associated voltage and / or current signals (or waveforms), and capture, collect, store data, etc.

[0365] At block 4510, electrical measurement data from voltage and / or current signals is processed to identify at least one power quality event associated with one or more loads. For example, in some embodiments, identifying at least one power quality event may include identifying: (a) a power quality event type of the at least one power quality event, (b) a magnitude of the at least one power quality event, (c) a duration of the at least one power quality event, and / or (d) a location of the at least one power quality event in the electrical system. In embodiments, the power quality event type may include one of a voltage sag, a voltage swell, and a voltage transient.

[0366] At block 4515, an impact of at least one identified power quality event on one or more loads is determined. In some embodiments, determining the impact of at least one identified power quality event includes measuring one or more first parameters associated with the load at a first time (e.g., a time before the event) (e.g., "pre-event" parameters), measuring one or more second parameters associated with the load at a second time (e.g., a time after the event) (e.g., "post-event" parameters), and comparing the first parameters to the second parameters to determine the impact of the at least one identified power quality event on the load. In embodiments, the power quality event(s) may be characterized as an impact event or a non-impact event based at least in part on the determined impact of the event(s). An impact event may, for example, correspond to an event that interrupts the operation (or effectiveness) of a load and / or an electrical system including the load. This in turn may affect the output of the system, such as the yield, quality, speed, etc. of a product generated by the system. In some embodiments, the product may be a physical / tangible object (e.g., a widget). Further, in some embodiments, the product may be a non-physical object (e.g., data or information). Conversely, a non-impact event may correspond to an event that does not interrupt (or minimally interrupts) the operation (or effectiveness) of the load and / or the electrical system including the load.

[0367] At block 4520, at least one identified power quality event and the determined impact of at least one identified power quality event are used to generate or update an existing tolerance curve associated with one or more loads. In an embodiment, the tolerance curve characterizes the tolerance level of the load to certain power quality events. For example, a tolerance curve (e.g., Figure 4 The tolerance curves may be displayed on a graphical user interface (GUI) of the IED (e.g., as shown in FIG. 1 ), to indicate “prohibited zones”, “no damage zones”, and “no functional interruption zones” associated with the load (and / or electrical system), wherein each zone corresponds to a different level of tolerance of the load to certain power quality events. For example, the tolerance curves may be displayed on a graphical user interface (GUI) of the IED (e.g., as shown in FIG. 1 ). Figure 1B 230) and / or on a GUI of the control system. In embodiments where a tolerance curve has already been generated prior to block 4520, for example, because an existing tolerance curve exists, the existing tolerance curve can be updated to include information derived from at least one identified power quality event and the determined impact of the at least one identified power quality event. For example, in embodiments where a baseline tolerance curve exists, or in embodiments where a tolerance curve has already been generated using method 4500, an existing tolerance curve can exist (e.g., an initial tolerance curve generated in response to a first or initial power quality event). In other words, in embodiments, a new tolerance curve is typically not generated after each identified power quality event, but rather each identified power quality event can result in an update to an existing tolerance curve.

[0368] At block 4525 (optional in some embodiments), it is determined whether the effect of at least one identified power quality event exceeds a threshold or falls outside a range or zone indicated in a tolerance curve (e.g., a "no interruption of functionality zone"). If it is determined that the effect of at least one identified power quality event falls outside the range indicated in the tolerance curve (e.g., the event causes an interruption of load functionality as indicated by an electrical parameter measurement or by some external input), the method may proceed to block 4530. Alternatively, in some embodiments, if it is determined that the effect of at least one identified power quality event does not fall outside the range indicated in the tolerance curve (e.g., the event does not cause an interruption of load functionality), the method may end. In other embodiments, the method may return to block 4505 and repeat again. For example, in embodiments where it is desirable to continuously (or semi-continuously) capture voltage and / or current signals and dynamically update the tolerance curve in response to power quality events identified in these captured voltage and / or current signals, the method may return to block 4505. Alternatively, in embodiments where it is desired to characterize a power quality event identified in a set of captured voltage and / or current signals, the method may end.

[0369] Furthermore, in embodiments, the event information may be used to adjust (eg, extend) the "uninterrupted functionality" zone, for example, by generating a custom tolerance curve (similar to Figure 2 ). It should be appreciated that characterizing an electrical system at some point is extremely useful to the user as they can better understand the behavior of their system.

[0370] In some embodiments, the range indicated in the tolerance curve is a predetermined range, e.g., a user-configured range. In other embodiments, the range is not predetermined. For example, I might choose to have no "functional non-interruption" zone, and say that anything that deviates from the nominal voltage needs to be evaluated. In this case, the voltage range might be all over the place, and I might have dozens of power quality events; however, my load might not experience any disturbances. Therefore, these events are considered to have no impact. In this case, I relax / extend my "non-interruption" zone from essentially the nominal voltage outward to the point where these events begin to disturb my load (based on load impact measured before the event vs. after the event).

[0371] In other words, the present invention is not limited to the ITIC curve (or any other predetermined range or (multiple) curves). Instead, embodiments of the present invention require that a custom voltage tolerance curve be "created" for a specific location in an electrical system or network (i.e., where the IED is located). The curve may be based on an ITIC curve, a SEMI curve, or any number of other curves. Furthermore, the curve may be a custom curve (i.e., may not be based on a known curve, but may be developed without an initial reference or baseline). It should be understood that a predetermined tolerance curve is not required for the present invention, but rather, it is merely used to explain the present invention (in conjunction with this figure, and in conjunction with the figures described above and below).

[0372] At block 4530 (optional in some embodiments), an action affecting at least one component of the electrical system may be automatically performed in response to a determined impact of at least one identified power quality event exceeding a range indicated by a tolerance curve. For example, in some embodiments, a control signal may be generated in response to a determined impact of at least one identified power quality event exceeding a range, and the control signal may be used to affect at least one component of the electrical system. In some embodiments, at least one component of the electrical system corresponds to at least one load monitored by an IED. The control signal may be generated by an IED, a control system, or another device or system associated with the electrical system. As described above, in some embodiments, the IED may include or correspond to a control system. Additionally, in some embodiments, the control system may include an IED.

[0373] As another example, for example, in a facility associated with the impact, an action that may be affected at block 4530 is starting and stopping a timer to quantify the length (or duration) of the impact on production. This will help the user make better decisions regarding the operation of the facility during atypical conditions.

[0374] After block 4530, in some embodiments, the method can end. In other embodiments, the method can return to block 4505 and repeat again (for substantially the same reasons discussed above in conjunction with block 4525). For example, in some embodiments where the method ends after block 4530, the method can be started again in response to user input and / or control signals.

[0375] refer to Fig.46 , a flowchart illustrating an example method 4600 for quantifying power quality events (or disturbances) in an electrical system, which method may be implemented, for example, in a processor of an IED (e.g., Figure 1A121 shown) and / or implemented on a processor of a control system. Method 4600 can also be implemented away from the IED in a gateway, cloud, field software, etc. The method 4600 evaluates voltage and / or current signals measured and captured by the IED to determine whether the electrical system is affected (e.g., at the (multiple) IED level) using pre-event / post-event power characteristics. In an embodiment, a threshold value can be used to determine the recovery time (e.g., the post-event power is 90% of the pre-event power). This allows us to quantify the impact of power quality disturbances on (multiple) loads, (multiple) processes, (multiple) systems, (multiple) facilities, etc.

[0376] like Fig.46 As shown, method 4600 begins at block 4605, where voltage and / or current signals (or waveforms) are measured and captured by the IED.

[0377] At block 4610, the voltage and / or current signals are processed to identify one or more loads (e.g., Figure 1A In some embodiments, pre-event, event, and post-event recorded data may also be used to identify power quality events. Pre-event, event, and post-event recorded data may be stored, for example, on a memory device associated with an IED and / or gateway, cloud, and / or field software application.

[0378] At block 4615, pre-event parameters are determined from the voltage and / or current signals. In embodiments, the pre-event parameters correspond to substantially any parameters that can be directly measured and / or derived from the voltage and current, including but not limited to power, energy, harmonics, power factor, frequency, event parameters (e.g., disturbance time, disturbance amplitude, etc.), etc. In embodiments, pre-event data may also be derived from "statistical standards". Metadata may also be used to help derive additional parameters accordingly.

[0379] At block 4620, the impact of the power quality event is determined, measured, or calculated. In an embodiment, the event impact is calculated based on pre-event parameters vs. post-event parameters. In an embodiment, this includes both the characteristics of the event at the metering point of the system (i.e., magnitude, duration, type of disturbance, etc.) and its impact on load(s), process(es), system(s), facility(s), etc.

[0380] At block 4625, the recovery threshold (or condition) is compared to the real-time parameter. In an embodiment, the recovery threshold may correspond to a percentage of a pre-event condition that is considered a system, subsystem, process, and / or load recovery condition. In an embodiment, industry standards, market segmentation recommendations, historical analysis, independently determined variables, and / or load characteristics may be used to provide the recovery threshold. Additionally, statistical criteria may be used to provide the recovery threshold. In an embodiment, the recovery threshold is a configured (e.g., pre-configured) recovery threshold stored on a memory device associated with the IED. An alternative approach is to pass all voltage event information to a cloud or field software, where it is then filtered using the recovery threshold. In this case, the recovery threshold would be stored in the cloud or field, rather than in the IED.

[0381] At block 4630, the IED determines whether the real-time parameter satisfies a restoration threshold (or condition). If the IED determines that the real-time parameter satisfies the restoration threshold, the method proceeds to block 4635. Alternatively, if the IED determines that the real-time parameter does not satisfy the restoration threshold, the method may return to block 4625, and block 4625 may be repeated again. In an embodiment, the output here is a determination of a restoration time; therefore, it may remain in a loop until the post-event level satisfies a predetermined threshold.

[0382] At block 4635, the IED calculates a restoration time based on the power quality event. In an embodiment, the restoration time is calculated from a time associated with the power quality event (eg, an initial occurrence of the power quality event) until a time when a restoration threshold is met.

[0383] At block 4640, an indication of a power quality disturbance (or event) is provided at the output of the IED. In an embodiment, the indication may include one or more reports and / or one or more control signals. A report may be generated to include information from any discrete IED of the electrical system, including: recovery time, impact on power, cost associated with event impact, I / O state change, event time / recovery time, voltage / current change, imbalance change, affected area, etc. In an embodiment, the recovery time and impact may be based on data from one or more IEDs. The report may be provided to a customer, sales team, supply management, engineering team, and / or any other interested party, etc. A control signal may be generated to control one or more parameters or characteristics associated with the electrical system. As an example, a control signal may be used to adjust one or more parameters associated with the load(s) that the IED is configured to monitor.

[0384] At block 4640, an indication of the power quality disturbance (and other data associated with method 4600) may also be stored. In some embodiments, the indication may be stored locally, for example, at the same location as the IED (or on the IED device itself). Additionally, in some embodiments, the indication may be stored remotely, for example, in the cloud and / or in field software. After block 4640, method 4600 may end.

[0385] refer to Fig.47 , a flowchart shows an example method 4700 for generating an extended qualitative guide for power quality. Fig.46 The method 4600 described, for example, in an embodiment, the method 4700 can be implemented on a processor of an IED and / or a processor of a control system. The method 4700 can also be implemented away from the IED in a gateway, cloud, field software, etc. In an embodiment, by evaluating the pre-event / post-event power characteristics of the power quality event, the sensitivity of the electrical system at the metering point to power quality disturbances can be quantified. This information can be used to identify product offerings for mitigation solutions and provide better qualitative guidance for organizations marketing these solutions. In an embodiment, when a power quality event occurs, the method 4700 can also be used for energy saving opportunities (e.g., power factor correction, increased equipment efficiency, etc.).

[0386] like Fig.47 As shown, method 4700 begins at block 4705, where voltage and / or current signals (or waveforms) are measured and captured by the IED.

[0387] At block 4710, the voltage and / or current signals are processed to identify power quality events associated with one or more loads monitored by the IED. In some embodiments, pre-event, event, and post-event recorded data may also be used to identify power quality events. Pre-event, event, and post-event recorded data may be stored, for example, on a memory device associated with the IED and / or a gateway, cloud, and / or field software application.

[0388] At block 4715, pre-event parameters are determined based on the voltage and / or current signals. In embodiments, pre-event parameters correspond to substantially any parameters that can be directly measured and / or derived from voltage and current, including but not limited to power, energy, harmonics, power factor, frequency, event parameters (e.g., disturbance time, disturbance amplitude, etc.), etc. In embodiments, pre-event data may also be derived from "statistical standards". Metadata may also be used to help derive additional parameters accordingly.

[0389] At block 4720, the impact of the power quality event is calculated. In an embodiment, the event impact is calculated based on pre-event parameters vs. post-event parameters. In an embodiment, this includes both the characteristics of the event at the metering point of the system (i.e., magnitude, duration, type of disturbance, etc.) and its impact on load(s), process(es), system(s), facility(s), etc.

[0390] At block 4725, the event characteristics are compared to mitigation solutions (e.g., product solutions). In an embodiment, there may be a library of design and application standards for solutions to mitigate problems associated with power quality events or disturbances. The library of design and application standards for solutions may be stored on a memory device associated with the IED or accessed by the IED (e.g., remotely via the cloud). In some embodiments, block 4725 may be executed in the cloud or in on-site software. In this way, energy consumers can see everything from a system level.

[0391] At block 4730, the IED determines whether a particular entity (e.g., Schneider Electric) provides a mitigation solution for the particular event. If the IED determines that the particular entity provides a mitigation solution for the particular event, the method proceeds to block 4635. Alternatively, if the IED determines that the particular entity does not provide a mitigation solution for the particular event, the method proceeds to block 4750. In some embodiments, an "IED" may be defined as being in the cloud or in the field (however, away from the meter). In embodiments, it may be wise to place the solution and much of the analytics in the cloud or in the field software because it is easier to update, it is more accessible to energy consumers, and it provides an aggregated system view.

[0392] At block 4735, a list of solutions provided by a particular entity is established for a particular event or problem (or type of event or problem). At block 4740, a report is generated and provided to a customer, a sales team associated with a particular entity, or other appropriate representative of the entity. In an embodiment, the report may include information from any discrete metering device (or as a system), including: recovery time, impact on power, I / O state changes, event time / recovery time, voltage / current changes, phase balance changes, processes and / or affected areas, etc. The report may include information about SE solutions (e.g., customer-oriented literature, features and benefits, technical specifications, costs, etc.), approximate solution size required for a given event (or type of event), comparison with external standards, location, etc. Electrical and / or metering system hierarchies and / or other metadata (e.g., load characteristics, etc.) may be used to assist in the evaluation.

[0393] At block 4745, the report (and other information associated with method 4700) may be stored. In some embodiments, the report may be stored locally, for example, at the same location as the IED (or on the IED device itself). Additionally, in some embodiments, the report may be stored remotely, for example, in the cloud. In an embodiment, blocks 4740 and 4745 may be performed substantially simultaneously.

[0394] Returning now to block 4730, if it is determined that a particular entity does not provide a mitigation solution for a particular event, the method proceeds to block 4750. At block 4750, event parameters and / or characteristics (and other information associated with method 4700) may be stored (e.g., locally and / or in the cloud). At block 4755, a report is generated based at least in part on the selected information stored at block 4750. In an embodiment, the report may include an assessment of the impact on energy consumers and an assessment of the need for potential future solution development, third-party solutions, etc. After block 4755 (or blocks 4740 / 4745), method 4700 may end.

[0395] refer to Fig.48 , a flow chart illustrates an example method 4800 for dynamic tolerance curve generation for power quality. Similar to the above-described methods 4500, 4600, and 4700, in an embodiment, the method 4800 may be implemented on a processor of an IED and / or a processor of a control system. The method 4800 may also be implemented away from the IED in a gateway, cloud, field software, etc. In an embodiment, by evaluating pre-event / event / post-event power characteristics of power quality events, a customized event tolerance curve may be automatically developed (over time) for substantially any given energy consumer. This is very useful for helping energy consumers identify, characterize, analyze, and / or desensitize their systems to power quality events.

[0396] like Fig.48 As shown, method 4800 begins at block 4805, where voltage and / or current signals (or waveforms) are measured and captured by the IED.

[0397] At block 4810, the voltage and / or current signals are processed to identify power quality events associated with one or more loads monitored by the IED. In some embodiments, pre-event, event, and post-event recorded data may also be used to identify power quality events. Pre-event, event, and post-event recorded data may be stored, for example, on a memory device associated with the IED and / or a gateway, cloud, and / or field software application.

[0398] At block 4815, pre-event parameters are determined from the voltage and / or current signals. In embodiments, the pre-event parameters correspond to substantially any parameters that can be directly measured and / or derived from the voltage and current, including but not limited to power, energy, harmonics, power factor, frequency, event parameters (e.g., disturbance time, disturbance amplitude, etc.), etc. In embodiments, pre-event data may also be derived from "statistical standards". Metadata may also be used to help derive additional parameters accordingly.

[0399] At block 4820, the impact of the power quality event is determined. In an embodiment, the event impact is calculated based on pre-event parameters vs. post-event parameters. In an embodiment, this includes both the characteristics of the event at the metering point of the system (i.e., magnitude, duration, type of disturbance, etc.) and its impact on load(s), process(es), system(s), facility(s), etc.

[0400] At block 4825, a disturbance threshold (or condition) is compared to the determined impact of the event. In an embodiment, the disturbance threshold may correspond to a percentage change between pre-event and post-event conditions that is considered a "significant" system, subsystem, process, and / or load disturbance. For example, a 5% load reduction due to an electrical (or other) event may be considered "significant." In an embodiment, the disturbance threshold is a configured (e.g., pre-configured) disturbance threshold stored on a memory device associated with the IED and / or gateway, cloud, and / or field software application.

[0401] At block 4830, the IED determines whether the system, subsystem, process, facility, and / or load has experienced (or is experiencing) a "significant" disturbance (e.g., based on the comparison of block 4825). If the IED determines that the system, subsystem, process, facility, and / or load(s) have experienced a "significant" disturbance, the method proceeds to block 4835. Alternatively, if the IED determines that the system, subsystem, process, facility, and / or load(s) have not experienced a "significant" disturbance, the method proceeds to block 4840.

[0402] At block 4835, disturbance points are generated and plotted as disturbances (e.g., affecting a system, subsystem, process, facility, and / or load(s)). At block 4845, a baseline tolerance curve (e.g., SEMI-F47, ITIC, CBEMA, etc.) is modified, changed, and / or customized based on characteristics associated with the particular recorded disturbance (here, at block 4835).

[0403] Alternatively, at block 4840, in response to the IED determining that the system, subsystem, process, facility, and / or load did not experience a "significant" disturbance, a disturbance point is generated and plotted as non-disturbant (e.g., not affecting the system, subsystem, process, facility, and / or (multiple) loads). At block 4845, the baseline tolerance curve is modified, changed, and / or customized based on the characteristics associated with the particular recorded disturbance (here, at block 4840). For example, the line in the curve may move between a "no disruption zone" and a "no damage / inhibit zone." Alternatively, the line in the curve may not move at all.

[0404] At block 4850, a report is generated. In an embodiment, the report may include information from substantially any discrete IED (or as a system), including: restoration time, impact on power, I / O state changes, event time / restore time, voltage / current changes, imbalance changes, affected areas and loads, etc. The report may include updated graphs of tolerance curve(s), highlighted changes in curve(s), recommended mitigation solution(s), etc.

[0405] At block 4855 (optional in some embodiments), at least one alarm setting may be updated at the discrete metering point(s) to match the new tolerance curve (e.g., the tolerance curve generated at block 4845). At block 4860, the new tolerance curve (and other information associated with method 4800) may be stored (e.g., locally in a gateway, in field software, and / or in the cloud). In some embodiments, two or more of blocks 4850, 4855, and 4860 may be performed substantially simultaneously. After blocks 4850, 4855, and 4860, method 4800 may end.

[0406] Usually, if Fig.49 and Fig.50 As shown, equipment (e.g., loads or other electrical infrastructure) is designed to have a rated voltage and a recommended operating range. The rated voltage is the voltage amplitude / level required for optimal equipment operation. In addition, the recommended operating range is an area around the rated voltage (above and below the rated voltage) in which the equipment can still operate successfully and continuously, although not necessarily optimally (e.g., lower efficiency, additional heating, higher current, etc.). The IED voltage event alarm threshold (also referred to herein as the "alarm threshold" for simplicity) is typically configured (but not always) to align with the recommended operating range so that excursions beyond the recommended operating range can be measured, captured, and stored. This is because there is a strong correlation between excessive voltage excursions and temporary or permanent damage to the equipment that experiences these excursions. In addition, voltage excursions may cause operational problems, interruptions, data loss, and / or any other number of effects on equipment, processes, and / or operations.

[0407] While the "recommended operating range" of a load, process, and / or system is typically related to voltage magnitude, the duration of these excursions is also an important consideration. For example, a 1 millisecond voltage excursion outside the recommended operating range of +10% may not adversely affect the operation of the load, process, and / or system, nor affect its expected operating life. Alternatively, a 20 millisecond voltage excursion outside the recommended operating range of +10% may cause the same load, process, and / or system to experience an outage and / or reduce its expected life (to some extent).

[0408] Fig.49 and Fig.50 shows two representations of the same concept. That is, Fig.49 Showing the rms waveform, Fig.50 Shows the instantaneous waveform. Fig.49 The rms waveform shown is calculated using the well-known equation (root mean square) from Fig.50 The instantaneous waveform data shown is derived from the waveform data. Both waveform representations are useful for analyzing power and energy related problems and troubleshooting power quality problems. Each corresponding graph shows an exemplary rated voltage, upper alarm threshold and lower alarm threshold for a theoretical load, process and / or system. In this case, the recommended operating range (shaded area) is assumed to be aligned with the upper and lower limits of the alarm threshold, respectively.

[0409] refer to Fig.51 , a flowchart illustrating an example method 5100 for characterizing a power quality event in an electrical system, the method may be performed, for example, in at least one IED (e.g., Figure 1A In some embodiments, the method 5100 may also be implemented in a gateway, a cloud-based system, an on-site software, or another head-end system away from at least one IED.

[0410] like Fig.51 As shown, method 5100 begins at block 5105, in which at least one first IED among a plurality of IEDs in the electrical system measures energy-related signals and captures, collects, stores data, etc. The at least one first IED is installed at a first metering point (e.g., a physical metering point) in the electrical system (e.g., Fig.30F Metering point M1 shown).

[0411] At block 5110, at least one second IED among the plurality of IEDs in the electrical system measures energy-related signals and captures, collects, stores, etc. the data. The at least one second IED is installed at a second metering point (e.g., a physical metering point) in the electrical system (e.g., Fig.30F Metering point M2 shown).

[0412] In some embodiments, the energy-related signals captured by at least one first IED and the energy-related signals captured by at least one second IED include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage.

[0413] At block 5115, electrical measurement data of at least one first virtual meter in the electrical system is derived from (a) electrical measurement data derived from or derived from energy-related signals captured by at least one first IED, and (b) electrical measurement data derived from or derived from energy-related signals captured by at least one second IED. In an embodiment, the at least one first virtual meter is derived from or is located at a third metering point in the electrical system (e.g., Fig.30F In an embodiment, the third meter point (eg, a virtual meter point) is different from both the first meter point and the second meter point.

[0414] In some embodiments, electrical measurement data of at least one first virtual meter may be derived based on a known location of the at least one first virtual meter relative to the at least one first IED and the at least one second IED. Figures 30B-30I As described, electrical measurement data of a virtual meter (eg, at least one first virtual meter) may be derived based on known locations of the virtual meter and other meters (eg, IEDs) in the electrical system and parent-child relationships therebetween.

[0415] At block 5120, the derived electrical measurement data of the at least one first virtual meter is used to generate or update a dynamic tolerance curve associated with a third metering point or location. As discussed in conjunction with the above figures, the dynamic tolerance curve can characterize the impact of a power quality event (or multiple power quality events) in the electrical system. Also as discussed above in conjunction with the figures, in some embodiments, in response to analysis of the dynamic tolerance curve, at least one means for mitigating the impact of the power quality event (or multiple power quality events) can be selected and applied.

[0416] After block 5120, in some embodiments, method 5100 can end. In other embodiments, method 5100 can be repeated again, for example in response to a control signal or user input, or automatically repeated again to ensure that the dynamic tolerance curve associated with the third metrology point or position is up to date.

[0417] In some embodiments, electrical measurement data derived from or from energy-related signals captured by at least one first IED may also be used to generate or update a dynamic tolerance curve associated with the first metering point. In addition, in some embodiments, electrical measurement data derived from or from energy-related signals captured by at least one second IED may also be used to generate or update a dynamic tolerance curve associated with the second metering point.

[0418] For example, in some embodiments, electrical measurement data derived from or derived from energy-related signals captured by at least one first IED in the electrical system can be processed to identify a power quality event at a first metering point and determine an impact of the identified power quality event at the first metering point. The identified power quality event and the determined impact of the identified power quality event at the first metering point can be used to generate or update a first dynamic tolerance curve associated with the first metering point. In some embodiments, the first dynamic tolerance curve characterizes at least the impact of the power quality event(s) on the first metering point.

[0419] In some embodiments, the at least one first IED may be configured to monitor one or more loads in the electrical system. In these embodiments, the first dynamic tolerance curve may further characterize a response of the one or more loads to the power quality event.

[0420] In some embodiments, at least one second IED may not be configured to capture power quality events, or at least one second IED may not be capable of capturing power quality events. For example, in these embodiments, the impact of the identified power quality event at the second metering point may be determined based on an evaluation of electrical measurement data derived from or derived from energy-related signals captured by at least one second IED near the time of occurrence of the power quality event identified at the first metering point. For example, the time of occurrence of the power quality event identified at the first metering point may be determined by processing electrical measurement data derived from or derived from energy-related signals captured by at least one first IED.

[0421] In some embodiments, the identified power quality event and the determined impact of the identified power quality event at the second metering point may be used to generate or update a second dynamic tolerance curve associated with the second metering point. In some embodiments, the second dynamic tolerance curve characterizes at least the impact of the power quality event(s) on the second metering point.

[0422] In some embodiments, the at least one second IED may be configured to monitor one or more loads in the electrical system. In these embodiments, the second dynamic tolerance curve may further characterize a response of the one or more loads to the power quality event.

[0423] In the above embodiments where at least one second IED may not be configured to capture power quality events, or at least one second IED may not be able to capture power quality events, at least the determined time of occurrence of the power quality event identified at the first metering point may be communicated from the at least one first IED to at least one of the cloud-based system, the field software, the gateway, and another head-end system. In some embodiments, the impact of the power quality event identified at the second metering point may be determined on at least one of the cloud-based system, the field software, the gateway, and another head-end system.

[0424] In some embodiments, transmitting the determined occurrence time from at least one first IED to at least one of a cloud-based system, field software, a gateway, and another head-end system includes: generating at least one of a timestamp, an alarm, and a trigger indicating the determined occurrence time on at least one first IED; and transmitting at least one of the timestamp, the alarm, and the trigger to at least one of the cloud-based system, the field software, the gateway, and another head-end system.

[0425] refer to Fig.52 , a flowchart illustrating an example method 5200 for characterizing the impact of a power quality event on an electrical system. The method 5200 may be performed, for example, in at least one IED (e.g., Figure 1A The system is implemented on a processor of 121) and / or remote from at least one IED in at least one of a cloud-based system, field software, a gateway, or another head-end system.

[0426] like Fig.52 As shown, method 5200 starts at block 5205, in which at least one metering device in the electrical system measures an energy-related signal (or waveform), and captures, collects, stores data, etc. In some embodiments, the at least one metering device includes at least one of an IED and / or a virtual meter. In addition, in some embodiments, the energy-related signal includes at least one of the following: voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage.

[0427] At block 5210, electrical measurement data derived from or derived from the energy-related signal captured by at least one metering device at block 5205 is processed to identify at least one load (e.g., Figure 1A At least one metering device and at least one load are installed at corresponding locations in the electrical system.

[0428] At block 5215 , it is determined whether the identified power quality event has an impact on at least one load or the electrical system. If it is determined that the identified power quality event has an impact on at least one load or the electrical system, the method proceeds to block 5220 .

[0429] At block 5220, a recovery time is determined for at least one load or electrical system to recover from the identified power quality event. Additionally, at block 5225, based on the determined recovery time, data captured, collected, and / or stored during the recovery time is marked (or otherwise indicated) as atypical or abnormal. In some embodiments, the recovery time is marked (or otherwise indicated) in a dynamic tolerance curve associated with the at least one load or electrical system.

[0430] Returning briefly now to block 5215, if it is determined that the identified power quality event has no impact on at least one load or the electrical system, the dynamic tolerance curve may be updated, and in some embodiments, the method may end. After block 5225, in some embodiments, the method may also end.

[0431] In other embodiments, the method may include one or more additional steps. For example, in some embodiments, in response to determining that the identified power quality event has an impact on at least one load or electrical system, one or more metrics associated with the electrical measurement data may be compared against a local utility rate structure to calculate a total energy-related cost of the identified power quality event and identify opportunities to reduce the total energy-related cost. It should be understood that in some embodiments, the total energy-related cost of the identified power quality event and the identified opportunities to reduce the total energy-related cost are based on the marked restoration time data.

[0432] Furthermore, in some embodiments, an economic impact of the identified power quality event can be determined based at least in part on one or more metrics associated with the determined restoration time.Example metrics are discussed throughout this disclosure.

[0433] refer to Fig.53 , a flowchart illustrates an example method 5300 for reducing recovery time from a power quality event in an electrical system, for example, by tracking response characteristics of the electrical system. The method 5300 may be performed, for example, in at least one IED (e.g., Figure 1A The system is shown as being implemented on a processor of 121) and / or remote from at least one IED in at least one of a cloud-based system, field software, a gateway, or another head-end system.

[0434] like Fig.53As shown, method 5300 starts at block 5305, in which energy-related signals (or waveforms) are measured by at least one IED in the electrical system, and data is captured, collected, stored, etc. In some embodiments, the energy-related signals include at least one of voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage.

[0435] At block 5310, electrical measurement data derived from or derived from energy-related signals captured by at least one IED at block 5305 is processed to identify power quality events associated with one or more portions of the electrical system.

[0436] At block 5315, at least one means for recovering from the identified power quality event is determined. Additionally, at block 5320, a selected one of the at least one means for recovering from the identified power quality event is applied.

[0437] At block 5325, in response to a selected one of at least one means for recovering from the identified power quality event being applied, a response characteristic of the electrical system is tracked. In some embodiments, the response characteristic of the electrical system is tracked relative to a baseline response of the electrical system. In some embodiments, tracking the response characteristic includes identifying recurring event data. The identified recurring event data may be used, for example, to predict power quality events in the electrical system.

[0438] As discussed above in conjunction with Section VI of this disclosure, "Using Recovery Time to Decompose Typical and Atypical Operational Data," it is important to recognize that the operation of a facility during the recovery period is often abnormal or atypical compared to non-recovery time (i.e., normal operation). In addition, identifying and "marking" (i.e., representing) and distinguishing abnormal or atypical operational data from normal operational data (i.e., non-recovery data) is useful for performing calculations, metrics, analysis, statistical evaluation, and the like. Metering / monitoring systems do not inherently distinguish between abnormal operational data and normal operational data. Distinguishing and marking operational data as abnormal (i.e., due to being in recovery mode) or normal provides several advantages, examples of which are provided in Section VI of this disclosure.

[0439] At block 5330, the response characteristics of the electrical system are evaluated to determine the effectiveness of a selected one of the at least one means for recovering from the identified power quality event. If it is determined that the selected one of the at least one means for recovering from the identified power quality event is ineffective, then in some embodiments, the method may return to block 5315. Upon returning to block 5315, at least one other means for recovering from the identified power quality event may be determined. Additionally, at block 5320, a selected one of the at least one other means for recovering from the identified power quality event may be applied.

[0440] Returning now to block 5330, if it is alternatively determined that the selected one of the at least one means for recovering from the identified power quality event is effective, then in some embodiments, the method can end. Alternatively, for example, information about the problem and its solution can be included and / or appended to a history file. In other embodiments, the method can return to block 5325 (e.g., so that the response characteristics of the electrical system can be further tracked).

[0441] As discussed above in conjunction with the accompanying drawings, an electrical system may include multiple metering points or locations, such as Fig.29 , Fig. 30A , Fig. 30B and Figure 30F-33 Metering points M1, M2, etc. are shown. In some embodiments, it may be desirable to generate, update, or derive a dynamic tolerance curve for each of the plurality of metering points. Furthermore, in some embodiments, it may be desirable to analyze power quality events in the electrical system, for example, based on analysis of data aggregated, extracted, and / or derived from the dynamic tolerance curve.

[0442] refer to Fig.54 , a flowchart illustrating an example method 5400 for analyzing power quality events in an electrical system. The method 5400 may be performed, for example, in at least one metering device (e.g., Figure 1A 121) and / or remote from at least one metering device in at least one of a cloud-based system, on-site software, a gateway, or another head-end system. In some embodiments, at least one metering device may be located at a corresponding metering location (e.g., Fig.29 M1 shown).

[0443] like Fig.54As shown, method 5400 begins at block 5405, where at least one of a plurality of metering devices in an electrical system measures an energy-related signal (or waveform), and captures, collects, stores, etc. data. At least one of the plurality of metering devices can each be associated with a corresponding metering location in a plurality of metering locations in the electrical system. For example, a first of the plurality of metering devices can be associated with a first metering location (e.g., Fig.29 M1 shown), a second of the plurality of metering devices may be associated with a second metering location in the electrical system (e.g., Fig.29 M2 shown), and a third of the plurality of metering devices may be associated with a third metering location in the electrical system (e.g., Fig.29 M3) shown.

[0444] In some embodiments, the plurality of metering devices includes at least one IED (e.g., Figure 1A 121 shown). In addition, in some embodiments, the energy-related signals measured by the plurality of metering devices include at least one of the following: voltage, current, energy, active power, apparent power, reactive power, harmonic voltage, harmonic current, total voltage harmonic distortion, total current harmonic distortion, harmonic power, single-phase current, three-phase current, phase voltage, and line voltage.

[0445] At block 5410, using one or more techniques disclosed herein, such as in combination with Fig.45 and Fig.51 , processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices at block 5405 to generate, update, and / or derive at least one of a plurality of dynamic tolerance curves. In some embodiments, at least one of the plurality of dynamic tolerance curves is generated, updated, and / or derived for each of a plurality of metering locations in the electrical system. The plurality of metering locations includes at least one physical metering point (e.g., Fig.30F In some embodiments, the plurality of metering locations may further include at least one virtual metering point (e.g., Fig.30F V1 shown).

[0446] Similar to the dynamic tolerance curves discussed throughout this disclosure, each of the plurality of dynamic tolerance curves generated, updated, and / or derived at block 5410 can characterize and / or depict a response characteristic of the electrical system. More specifically, each of the plurality of dynamic tolerance curves can characterize and / or depict a response characteristic of the electrical system at a corresponding metering point of a plurality of metering points in the electrical system (e.g., Fig.29In an embodiment where the plurality of metering points include at least one virtual metering point, the dynamic tolerance curve of the at least one virtual metering point may be derived from the dynamic tolerance curve data of the at least one physical metering point, for example, as described above in conjunction with Fig.30F and as discussed in the other figures herein.

[0447] In some embodiments, one or more of the plurality of dynamic tolerance curves may have associated thresholds or set points, e.g., for triggering an alarm in the electrical system. In some embodiments, the thresholds or set points may include one or more upper alarm thresholds and / or one or more lower alarm thresholds, e.g., Fig.49 and Fig.50 Several examples thereof are described. According to aspects of the present disclosure, in response to a power quality event being above one or more upper alarm thresholds or below or below one or more lower alarm thresholds, an alarm can be triggered. Further, according to aspects of the present disclosure, in response to an alarm (or alarms) being triggered, an action (or actions) can be taken.

[0448] Example actions that may be performed in response to an alarm triggered, for example, due to an abnormal voltage condition may include, for example, reporting the abnormal voltage condition (e.g., via a voltage event alarm generated by at least one IED) and / or automatically performing an action (or actions) affecting at least one component of the electrical system, such as starting a diesel generator, operating a throw-over switch or static switch, etc. Fig.45 The method 4500 described, in some embodiments, can generate a control signal in response to an abnormal voltage condition, and the control signal can be used to affect at least one component of the electrical system (e.g., a load monitored by at least one IED, or a mitigation device such as a diesel generator, a transfer switch or a static switch, etc.). The control signal can be generated by at least one IED, a control system, or another device, software, or system associated with the electrical system. As discussed in the above figures, in some embodiments, at least one IED can include or correspond to a control system. In addition, in some embodiments, the control system can include at least one IED.

[0449] Further example actions that may be performed in response to a triggered alarm, for example, to prevent potential tripping due to voltage sag duration or magnitude, may include, for example, "turning off" some operating process steps identified as potential "sag aggravators", such as postponing (if possible) motor start-up during an existing voltage sag. In addition, in some embodiments, a user alarm (e.g., sent to a mobile phone application or other device application) may be triggered in response to a triggered alarm (i.e., a system alarm). In addition, in some embodiments, the alarm may be used as an input to a manufacturing SCADA system, for example, to change or delay another process. The alarm may also be remedial, for example, by providing a maintenance team with a precise location of the problem and a priority list of which (which) problems are most critical, which (which) problems are to be solved first, second, etc. In some embodiments, the system may also provide a recommended response (or multiple responses) to the problem, for example, for correcting the problem. An example recommended response may be to disconnect and / or reconfigure the load that caused the problem, wherein the disconnection and / or reconfiguration is performed manually by the user and / or automatically by the system. As described above, the types of actions that may be performed in response to an alarm may take a variety of forms.

[0450] In some embodiments, the above thresholds or set points (i.e., the thresholds for triggering alarms) may be generated during a so-called "learning period", for example, as described below in conjunction with Fig.55 Further discussion.

[0451] According to some aspects of the present disclosure, the dynamic tolerance curve generated, updated and / or derived at 5410 can be prepared by inferring relevant signal characteristics from the above-mentioned electrical measurement data. Examples of relevant signal characteristics (and information) may include severity (magnitude), duration, power quality type (e.g., sag, swell, interruption, oscillation transient, pulse transient, etc.), occurrence time, process(es) involved, location, affected equipment, relative or absolute impact, recovery time, event period or event type, etc. Additional examples of relevant signal characteristics may include identified changes in current, phase shift, etc., which lead to an interpretation (or interpretations), such as "type of inductive or capacitive load added or removed from the system during the event", "percentage of pre-event load added or dropped from the system", etc. In some embodiments, these relevant signal characteristics are determined from a portion of the electrical measurement data before the power quality event start time and a portion of the electrical measurement data after the power quality event end time. Furthermore, in some embodiments, these relevant signal characteristics are determined from a portion of the electrical measurement data during the power quality event (such as magnitude, duration, phases affected, etc.).

[0452] In some embodiments, for example, at least one of the plurality of dynamic tolerance curves may be displayed on a graphical user interface (GUI) (e.g., Figure 1B 230) and / or a GUI of a control system associated with the electrical system. In some embodiments, in response to user input, at least one of the plurality of dynamic tolerance curves is displayed. As a few examples, the control system may correspond to or include an electrical or industrial / manufacturing SCADA system, a building management system, and an electrical monitoring system.

[0453] In some embodiments, the method may proceed to block 5415 after block 5410. In other embodiments, the method may return to block 5405, and blocks 5405 and 5410 may be repeated, for example, to capture additional energy-related signals and update the multiple dynamic tolerance curves generated at block 5410. In this way, multiple dynamic tolerance curves may be automatically / dynamically maintained at their optimal levels. It should be understood that optimal may mean different things due to the nature of the load, segment, process and / or other inputs. This may result in a variety of different curves, each or all of which are associated with different uses (applications). According to some aspects of the present disclosure, the "dynamic" of the dynamic tolerance curve means that it changes over time. Therefore, time may be an important factor in modeling, or an important factor included in any model or curve. This is the core of every meter innovation, followed by global system-level analysis, aggregation, action recommendations or any related automated changes or commands (non-exhaustive list). Therefore, a model that evolves over time can be established. To simplify understanding, this can be represented by two (or more) different link graphs, for example. Trends in the optimal value (which is the value of the "initial impact" point) at each change point. Also, the relative impact on load or critical load, such as the percentage by which the load drops from the initial impact point over time.

[0454] At block 5415, power quality data from multiple dynamic tolerance curves is selectively aggregated. In some embodiments, the power quality data is selectively aggregated based on the location of multiple metering points in the electrical system. For example, power quality data from dynamic tolerance curves associated with metering points in the same or similar portion of the electrical system may be aggregated.

[0455] Additionally, in some embodiments, power quality data is selectively aggregated based on the criticality or sensitivity of multiple meter points to power quality events. For example, power quality data from a dynamic tolerance curve associated with highly critical or sensitive meter points can be aggregated. In some embodiments, the criticality of a meter point is based on the type of load(s) at the meter point, and the importance of the load to the operation of a process(es) associated with the electrical system. Additionally, in some embodiments, the sensitivity of a meter point is based on the sensitivity of the load to power quality events.

[0456] According to some aspects of the present disclosure, degradation or improvement of the sensitivity or resilience of a load or electrical system to a power quality event can be identified at each corresponding metering point in an electrical system. The degradation or improvement of the sensitivity or resilience of the electrical system to a power quality event can be identified, for example, based on the identified changes in the dynamic tolerance curve of each corresponding metering point. In some embodiments, the identified degradation or improvement of the sensitivity or resilience of the load or electrical system to a power quality event can be reported. For example, the identified degradation or improvement of the sensitivity or resilience of the load or electrical system to a power quality event can be reported by generating and / or initiating an alert (e.g., a key performance indicator), which indicates the identified degradation or improvement of the sensitivity or resilience of the electrical system to a power quality event. The alert can be transmitted via at least one of an interface such as a report, text, email, auditory, and a screen / display. In some embodiments, the screen / display may correspond to a screen / display presenting (multiple) dynamic tolerance curves. The transmitted alert can provide actionable suggestions for responding to the identified degradation or improvement of the sensitivity or resilience of the electrical system to a power quality event.

[0457] According to further aspects of the present disclosure, power quality data from a plurality of dynamic tolerance curves may be selectively aggregated to generate one or more aggregated dynamic tolerance curves.

[0458] In some embodiments, multiple dynamic tolerance curves may be aggregated by plotting them in a composite graph (or graphs). For example, the response characteristics of the electrical system at each meter point (e.g., as indicated in multiple tolerance curves) may be presented as sub-graphs in a composite graph (or graphs). In some embodiments, multiple tolerance curves may be presented in a composite graph (or graphs), for example, in order of appearance in the graph (or graphs), ranked based on criticality or sensitivity of the meter point (first to last). It should be understood that other groupings may be overlaid, for example, to reflect analysis by location. The grouping may be first by location, such as one graph per location, and then the meters may be sorted in descending order of sensitivity or criticality.

[0459] According to some aspects, for example, common(s) impact models may be overlaid and key characteristics of each meter highlighted by color coding. For example, a dynamic tolerance curve generated for the most sensitive, high criticality (e.g., at initial startup of magnitude) meter in an electrical system may be used as a reference curve for each and all other meters. This may then be played on other graphical components to highlight (e.g., by using line thickness, or coloring the curve red, green, or blue for "highly critical," "slightly critical," "non-critical," etc.) the criticality of the load / process (if labeled for each or certain meters).

[0460] In addition, according to some aspects, the dynamic tolerance curve associated with each group of related meters can be presented as a sub-graph in a composite graph (or multiple graphs). The relevance of the group can depend on, for example, the target (or multiple targets) of the composite graph (or multiple graphs). It should be understood that many different related groupings can be used in different contexts. For example, curves / meters can be grouped for analysis based on location (e.g., meter location). In addition, curves / meters can be grouped by criticality. For example, highly critical meters are grouped vs. other categories of meters. In addition, all individual highly critical meters can be plotted relative to an aggregate curve of less critical meters. Curves / meters can also be grouped by impact, for example, grouping meters by similar percentages at a given voltage sag magnitude and duration level. In addition, meters can be grouped by sensitivity curves or ranges, such as the distance between the initial impact and the final impact.

[0461] It will be appreciated that the above criteria may be combined. For example, filters may be applied to each location, thus grouping by location. A typical location may then be plotted, with all meters at that location used to calculate the average thresholds and change points. The aggregate typical plot of highly critical meters may then be compared to the minimum "Initial Impact Value" curve from the typical (average) "Initial Impact Value" curve. This will indicate if one or a few IEDs are creating a "weak point" in the system that may need to be made more robust (e.g., a potential vulnerability to be analyzed). solution).

[0462] For example, the ranking of the order of appearance of each meter (from first to last) can be based on the criticality or sensitivity of the electrical system and / or the load(s) at the meter point(s) associated with the meter. However, other groupings can be overridden. For example, to reflect an analysis by location, the meters can be first grouped by location and then sorted in order of decreasing sensitivity or criticality.

[0463] After block 5415, the method may proceed to block 5420. At block 5420, power quality events in the electrical system are analyzed based on the selectively aggregated power quality data. For example, the selectively aggregated power quality data may be processed or analyzed to determine a relative criticality score for each of the power quality events to a process or application associated with the electrical system. For example, the relative criticality score may be based on the impact of the power quality event on the process or application. For example, a rooftop HVAC unit or system going down for three hours may be considered less critical for an office building than for a data center. Additionally, motors in a steel production line may be considered more critical than lighting rails or fixtures above the steel production line. In some embodiments, the relative criticality scores may be used as inputs to supervised learning. Supervised learning means that some (certain) variables may be used to teach a computing engine which questions are more valuable than others.

[0464] In some embodiments, the impact of the power quality event is related to the tangible or intangible cost of the power quality event to the process or application. Further, in some embodiments, the impact of the power quality event is related to the relative impact on the loads in the electrical system. In some embodiments, there may be an absolute threshold or unacceptable tripping offline of a load (or multiple loads) associated with the process or application. For example, in the above-mentioned data center example, the cooling pump of the HVAC system may be considered a critical component that should never be tripped offline. Further, in the above-mentioned steel production line example, there may be a path in the production line that does not have redundant motors on the conveyor belt. This may be a component or process that should never be misoperated, tripped offline, and / or failed.

[0465] In some embodiments, for example at block 5420, the determined relative criticality scores may be used to prioritize reactions (or responses) to power quality events. For example, the determined relative criticality scores (e.g., criticality scores using historical analysis of the impact of voltage events) may be used to determine how a significant load loss affects system operation. The more significant the impact, the lower the relative criticality score relative to other IEDs in the system. This may also be performed based on discrete locations of the IEDs (i.e., some locations are more significantly affected by voltage events than others). Certain locations may inherently have worse criticality scores than other locations due to their processes, functions, and / or loads.

[0466] The relative criticality scores can also be used as input to drive manufacturing SCADA, for example, to determine whether an alternative process should be triggered or terminated. For example, a voltage sag may result in poorer product quality or product defects, such as fluctuations in thermal and pressure conditions producing an increase in the number of bubbles in a rubber product. Higher criticality scores can reflect these parts of the production process. In this case, an alternative / corrective process can be triggered to supplement the current process step or the product can be scrapped.

[0467] In embodiments where power quality data from a plurality of dynamic tolerance curves has been selectively aggregated at block 5415 to generate one or more aggregated dynamic tolerance curves, the aggregated dynamic tolerance curves may be analyzed or otherwise evaluated to analyze power quality events in the electrical system.

[0468] The power quality events analyzed at block 5420 may include, for example, at least one of a voltage sag, a voltage swell, a voltage transient, a momentary interruption, a momentary interruption, a temporary interruption, and a long-term root mean square (rms) variation.

[0469] In some embodiments, at block 5420, it may also be determined whether there are any differences between the selectively aggregated power quality data. For example, the differences may include inconsistent naming conventions in the selectively aggregated data. As an example, the naming convention of power quality data in a dynamic tolerance curve associated with a first metering point in the electrical system may be different from the naming convention of power quality data in a dynamic tolerance curve associated with a second metering point in the electrical system. In embodiments where differences are identified, additional information may be requested (e.g., from a system user) to reconcile the differences. Recommendations may be made.

[0470] After block 5420, in some embodiments, the method can end. In other embodiments, the method can return to block 5405 and repeat again (e.g., to capture additional energy-related signals, update dynamic tolerance curves associated with metering points in the electrical system, and selectively aggregate power quality data from the updated dynamic tolerance curves to analyze power quality events).

[0471] It should be understood that in some embodiments, the method 5400 may include one or more additional blocks. For example, the method 5400 may include marking the power quality event in one or more of the plurality of dynamic tolerance curves with relevant and characteristic information, for example based on the information extracted about the power quality event. In some embodiments, the relevant and characteristic information includes at least one of the following: severity score, restoration time, percentage of load increased or lost during the event, (multiple) inductive or capacitive loads increased or decreased during the event, location within the system, type of (multiple) loads downstream of the IED providing the information, impact on facility operation, etc. In addition, in some embodiments, the information about the power quality event can be extracted from a portion of electrical measurement data from power-related signals captured before the start time of the power quality event. In some embodiments, the information about the power quality event can be extracted from a portion of electrical measurement data from energy-related signals captured after the end time of the power quality event. Information can also be extracted from a portion of electrical measurement data from energy-related signals captured during the power quality event.

[0472] According to some aspects of the present disclosure, tagging and data enrichment can be performed at substantially any time. For example, tagging and data enrichment can be performed before or during field commissioning based on the load to be run, and based on calculation and process planning (time for preparation and calculation), to name a few examples. During normal operation, events may be tagged on the fly (time for normal operation). In addition, events may be tagged during re-commissioning or expansion or maintenance (e.g., change time).

[0473] As discussed above in conjunction with the accompanying drawings, a dynamic tolerance curve can characterize the impact of a power quality event (or multiple power quality events) in an electrical system. Also as discussed above in conjunction with the accompanying drawings, in some embodiments, in response to analysis of the dynamic tolerance curve, at least one of the means for mitigating the impact of the power quality event (or multiple power quality events) can be selected and applied. For example, according to some aspects of the present disclosure, the dynamic tolerance curve can be used to prevent the tripping of more critical loads by driving counter measures including system-level process optimization. For illustrative purposes, if a user specifies an absolute threshold, the system (e.g., a control system) can calculate an optimal curve to avoid the risk of a given meter tripping (protection relay). In addition, the system can decide to shut down typical loads that are known (or calculated) as possible causes of power quality events (e.g., voltage sags), or to first (faster) cut off other potential process steps that may generate risks of increasing or maintaining the duration of longer power quality events.

[0474] It should be understood that one or more steps of method 5400 may be combined with one or more steps of other methods discussed throughout this disclosure, for example, to generate, update, and / or derive a dynamic tolerance curve.

[0475] refer to Fig.55 , the flowchart shows an example method 5500 for generating a dynamic tolerance curve according to an embodiment of the present disclosure. According to some embodiments of the present disclosure, the method 5500 shows that the above Fig.54 Example steps performed at blocks 5405 and 5410 of method 5400 discussed above. Similar to method 5400, method 5500 may be performed at at least one metering device (e.g., Figure 1A The system is implemented on a processor of 121) and / or remote from at least one metering device in at least one of a cloud-based system, on-site software, a gateway, or another head-end system.

[0476] like Fig.55 As shown, method 5500 begins at block 5505, where a so-called "learning period" begins. At block 5510, energy-related signals (or waveforms) are measured by a plurality of metering devices in the electrical system, and data is captured, collected, stored, etc. In some embodiments, at block 5515, the captured data corresponds to the measurement data required to calculate a threshold or set point.

[0477] At block 5515, a threshold or set point (e.g., an alarm threshold) is calculated based on the energy-related signal measured at block 5510. In addition, according to some embodiments of the present disclosure, an anticipatory threshold (e.g., a preventive threshold) may be determined at block 5515, for example, as a percentage (e.g., 90%) of a conventional threshold to prevent damage to the load of the electrical system. Critical and non-critical loads may have different pre-thresholds, for example, based on their impact on the electrical system. For critical or more sensitive loads, the pre-threshold may be tighter or closer to the nominal voltage. In addition, for non-critical or less sensitive loads, the pre-threshold may be looser or farther from the nominal voltage. For example, for some "critical loads", such as for voltage sags, the pre-threshold may be set between a sag alarm threshold of ±10% of the nominal voltage and an "initial impact voltage threshold" (also referred to as an initial impact point). In addition, for some "non-critical loads", the pre-threshold may be set to a loss of 30% of the load, such as for voltage sags. Example threshold calculations for a single meter in an electrical system are shown in the following table.

[0478]

[0479] Additionally, example threshold calculations for groups of meters in an electrical system (eg, based on criticality groups) are shown in the following table.

[0480]

[0481] As described and shown above, the meters can be grouped based on criticality. For example, the meters can be grouped into "high," "medium," and "low" criticality groups, for example, based on whether the meter includes or is coupled to a critical or non-critical load. Meters grouped in the high criticality group can select thresholds, for example, to prevent voltage sags. Further, meters grouped in the medium criticality group can select thresholds, for example, to trigger process changes. Further, meters grouped in the low criticality group can select thresholds, for example, such that a loss of HVAC capacity produces only excess consumption and a drop in comfort. For example, in Fig.55A An example method for moving from a single meter threshold calculation to a meter group threshold calculation as shown in the table above is shown in FIG.

[0482] According to an embodiment of the present disclosure, the above thresholds can be adjusted (manually, automatically or semi-automatically). For example, the thresholds can be manually adjusted in response to usage and / or user / process impact analysis (e.g., by type of load / process), as shown in the following table.

[0483]

[0484]

[0485] Additionally, the thresholds may be automatically adjusted, for example, in response to the duration of the recovery, as shown in the following table.

[0486]

[0487] Referring now to block 5520, at block 5520, a determination is made as to whether the learning period should continue. For example, in some embodiments, the learning period may have an associated (e.g., preprogrammed or user-configured) time period, and once the time associated with the learning period reaches the associated time period, it may be determined that the learning period does not need to continue. In other embodiments, it may be determined whether "enough" data has been obtained to generate a threshold or set point, and once sufficient data has been obtained, it may be determined that the learning period does not need to continue.

[0488] In some embodiments, during the learning period, events, process components, or meters in the electrical system may be marked based on measurement data collected during the learning period (eg, at block 5510).

[0489] If it is determined that the learning period should continue, the method returns to block 5510 and repeats blocks 5510, 5515, and 5520. Alternatively, if it is determined that the learning period does not need to continue, the method proceeds to block 5525.

[0490] At block 5525 (similar in some embodiments to block 5405 of method 5400), an energy-related signal (or waveform) is measured by at least one of a plurality of metering devices (i.e., IEDs) in the electrical system, and the data is captured, collected, stored, etc. Further, at block 5530 (similar in some embodiments to block 5410 of method 5400), electrical measurement data from or derived from the energy-related signals captured by the plurality of metering devices at block 5525 is processed to generate, update, and / or derive at least one of a plurality of dynamic tolerance curves. In some embodiments, at least one of the plurality of dynamic tolerance curves is generated, updated, and / or derived for each of a plurality of metering points in the electrical system.

[0491] After block 5525, in some embodiments, the method can end. In other embodiments, the method can return to block 5525 and repeat again (e.g., to capture additional energy-related signals and update dynamic tolerance curves associated with metering points in the electrical system). In further embodiments, the method can return to block 5505 and repeat again. More specifically, according to some aspects, for example, in response to user input, etc., the learning period can be initiated again from time to time.

[0492] Similar to method 5400, it will be appreciated that in some embodiments, method 5500 may include one or more additional blocks. For example, in some embodiments, the threshold or set point calculated at block 5515 may be defined or redefined after the above-described learning period. In addition, similar to method 5400, it will be appreciated that one or more steps of method 5500 may be combined with one or more steps of other methods discussed throughout the present disclosure, for example, to calculate thresholds or set points and / or generate, update, and / or derive dynamic tolerance curves.

[0493] refer to Fig.56 , flowchart illustrating an example method 5600 for marking a criticality score, for example, during a learning period, such as in conjunction with Fig.55 Similar to method 5500, method 5600 may be performed on at least one metering device (e.g., Figure 1A The system is implemented on a processor of 121) and / or remote from at least one metering device in at least one of a cloud-based system, on-site software, a gateway, or another head-end system.

[0494] like Fig.56As shown, method 5600 begins at block 5605, where a criticality score is marked for each metering device in the electrical system. As discussed throughout the present disclosure, each metering device (e.g., an IED) in the electrical system can be associated with a corresponding metering point in the electrical system, and electrical measurement data from energy-related signals captured by each metering device can be used to generate a dynamic tolerance curve associated with the corresponding metering point. As previously described, an absolute or relative criticality score can be determined for a metering device, a new power quality event, and the like. According to some aspects of the present disclosure, at block 5605, a user can mark a criticality score for each metering device in the electrical system. According to other aspects of the present disclosure, at block 5605, a system (e.g., a system for analyzing power quality events) can mark a criticality score for each metering device in the electrical system.

[0495] At block 5610, in some embodiments, each new power quality event may be marked with a criticality score by the system (in other embodiments, by the user). Each new power quality event may be marked, for example, using one or more of the techniques discussed in conjunction with the above figures (e.g., Fig.45 In block 5615, information tags may be added for each new power quality event or criticality score (such as the criticality of a particular industrial or building process running at the time of the event). For example, tag information (i.e., metadata, real data, etc.) may be attached / updated for each new power quality event. In short, new information may optionally be attached to the device, process, and / or system as the event occurs. As relevant new data arrives, the criticality score may also be changed and / or updated at the device(s), process(es), and / or system(s). In some embodiments, these information tags may correspond to system-added or inferred information tags. System-added or inferred information tags may, for example, help identify co-occurrences, possible sources, and the like, such as power factor changes, source change identification, increase or loss of inductive or resistive loads, capacitor banks being "turned on" or "turned off," and / or metadata from, for example, a building management system or SCADA system. In addition, system-added or inferred information tags may indicate correlations between power quality events associated with different metering devices in the electrical system. For example, correlations are derived from analysis of power quality events using one or more techniques previously disclosed herein.

[0496] After block 5615, in some embodiments, the method can end. In other embodiments, the method can return to block 5605 and repeat again (e.g., to mark more new events with criticality scores, and / or update the criticality scores of the metering devices). In further embodiments, the method can include one or more additional steps, as will be appreciated by one of ordinary skill in the art.

[0497] refer to Fig.57 , a flowchart illustrating another example method 5700 for marking a criticality score, for example, during a learning period, such as in conjunction with Fig.55 Similar to method 5500, method 5700 may be performed on at least one metering device (e.g., Figure 1A The method 5700 is implemented on a processor of the device 121 and / or remote from at least one metering device in at least one of a cloud-based system, field software, a gateway, or another head-end system. According to some aspects, the method 5700 corresponds to a data-driven system marking of a criticality score for each phase of each new power quality event.

[0498] like Fig.57 As shown, method 5700 begins at block 5705, where a percentage of load lost in response to each new event is determined. As discussed in conjunction with the above figures, the electrical system can include one or more loads, and the metering device (e.g., IED) can be configured to monitor at least one of the one or more loads. In some embodiments, the percentage of load loss can be determined by evaluating a new initial impact threshold and / or a new final impact threshold and the position between these thresholds (e.g., distribution or function approximation), for example, to interpolate the impact of the voltage event using different models. These different models, such as Figures 57A-57K As shown, for example, as will be discussed further below, can be used to approximate how the electrical system responds to each new power quality event.

[0499] According to some embodiments, the initial impact threshold may correspond to a threshold for detecting a first percentage of load loss. For example, an illustrative initial impact threshold or initial impact point may correspond to a threshold for detecting a 20% load loss at 75% of the nominal voltage, for example, within a specific duration. In the above example, for example, Fig.57A As shown, a 20% load loss corresponds to the first load loss percentage and initial impact point.

[0500] According to some embodiments, the final impact threshold may correspond to a threshold for detecting when all load (i.e., 100% of the load) is lost. For example, one illustrative final impact threshold or final impact point may correspond to a threshold for detecting a 100% load loss at 55% of the rated voltage, e.g., for a particular duration. In the above example, a 100% load loss corresponds to a final impact point, e.g., Fig.57A shown.

[0501] As briefly discussed above, the location (or region) between the initial impact threshold and the final impact threshold may be determined, for example, using one or more models. According to an example linear model, Fig.57B and Fig.57C As shown, for example, between 75% and 55% of the nominal voltage, for every 1% decrease in the nominal voltage, the load loss is 4%. In addition, according to an example logarithmic (or logarithmic) model, as Figures 57B-57D As shown, for example, the logarithmic percentage of load loss for every 1% drop in nominal voltage between 75% and 55% of nominal voltage.

[0502] According to some embodiments, these inferred "curve model" functions can be determined by essentially any approximation or modeling of a line curve calculation or curve fitting algorithm. The correlation model can be calculated and applied as a maximum function, for example, as Fig.57B and Fig.57C As shown, where the curve represents a "normal" curve. It should be understood that other statistical and inferential model types can be included in the method, such as, for example, mean, median, minimum, loss, regression and other calculations.

[0503] When applying these functions, such as in Fig.57C and Fig.57D In the table provided directly below this paragraph, the form of the curve(s) may change due to a step-by-step function superimposed on the "curve model" function. This is typical and reflects that the closer the IED is to the end of the electrical system, the more significant the load changes (here there may be five different motors). Conversely, the closer the IED is to the source (such as a utility delivery point), the load changes will generally be less significant. Therefore, in many cases, the load profile may appear more "continuous" due to the greater diversity of downstream loads, such as in our example, where there may be ten (or any number of) different motors.

[0504] Amplitude Linear Continuous Linear less load step Linear Multiple Load Steps Log continuous Logarithmic less load step Logarithmic Multiple Load Steps 75 20 20 20 20 20 20 74 24 20 20 45 20 20 73 28 20 28 58 20 58 72 32 20 28 65 20 58 71 36 20 36 71 20 71 70 40 40 36 75 75 71 69 44 40 44 78 75 78 68 48 40 44 81 75 78 67 52 40 52 83 75 83 66 56 40 52 85 75 83 65 60 60 60 87 87 87 64 64 60 60 89 87 87 63 68 60 68 91 87 91 62 72 60 68 92 87 91 61 76 60 76 93 87 93 60 80 80 76 95 95 93 59 84 80 84 96 95 96 58 88 80 84 97 95 96 57 92 80 92 98 95 98 56 96 80 92 99 95 98 55 100 100 100 100 100 100

[0505] It will be appreciated that loads (e.g., motors) can be of different sizes and have different response characteristics. In a given example, a larger load may be more sensitive to relay tripping (i.e., due to a voltage sag) than a smaller load. This would explain a logarithmic function such as provided in the example discussed above.

[0506] It should also be understood that the above models and functions can be based on power quality event sensitivity, or adjusted based on power quality event sensitivity. Fig.57E and Fig.57F As shown in , the load loss curve can be modeled based on the maximum sensitivity of the event. Figure 57G and Figure 57H As shown, the load loss curve can be modeled based on the event median sensitivity. The outliers in the curve can be identified as the most oversensitive, e.g. Fig.57IIn some embodiments, outliers can be identified as first candidates for correlation analysis, for example, to determine whether they accurately reflect how the electrical system responds to power quality events. Fig.57J It will be appreciated that in some embodiments, the above thresholds (eg, initial impact and final impact thresholds) may be used to identify a 50% (or another percentage) load loss. Figure 57K For example, the curves of multiple meters (eg, three meters) may be aggregated using the most sensitive meter of the multiple meters.

[0507] Now back to Fig.57 , at block 5710 of method 5700, the severity of each new power quality event may be determined. In some embodiments, the severity is indicated in the form of a severity score, which may be based on, for example, the magnitude and duration of the power quality event. A "severity score" may be based on a relative value (including a percentage of the total load on any particular device of the device measuring the voltage event) or an absolute value (based on actual measurements expressed in kilowatts, kilovolt-amperes, and / or other parameters), where the duration corresponds to the duration of the event. One example way to calculate the severity score includes combining the magnitude of the power quality event with the duration of the power quality event, i.e., the severity score is the product of the magnitude and duration. For example, the duration of the power quality event (e.g., the actual impact duration) may be combined (e.g., multiplied) with the magnitude of voltage and / or current measurements from before, during, and / or after the power quality event to determine the severity score. It should be understood that there may be many other ways to determine the severity score. For example, the severity score may be determined or derived from at least one of the power quality event magnitude, duration, recovery time, recovery loss percentage, power quality event location, etc. It should be understood that different zones in the electrical system may have a higher priority than other zones. In addition, it should be understood that in addition to the severity score, other scores may be determined, such as the percentage of load loss in the electrical system in response to the power quality event. In some embodiments, the percentage of load loss may be used to calculate the severity.

[0508] At block 5715, for example, using a method in conjunction with the above diagram (e.g., Fig.53 ) discusses techniques for determining the recovery time for the system to recover (e.g., return to normal or near normal) from each new power quality event.

[0509] At block 5720, information tags are added for each new quality event. In some embodiments, these information tags may correspond to system-added or inferred information tags, as described above.

[0510] After block 5720, in some embodiments, the method can end. In other embodiments, the method can return to block 5705 and repeat again (e.g., for additional marking). In further embodiments, as will be appreciated by one of ordinary skill in the art, the method can include one or more additional steps.

[0511] refer to Fig.58 , flowchart illustrating an example method 5800 for generating a dynamic tolerance curve, for example, after a learning period, such as above in conjunction with Fig.55 According to some aspects, the dynamic tolerance curve of each metering position can be modeled after learning, as described above in conjunction with Figure 34-40 As described above, and will be understood from the further discussion below. Similar to method 5500, method 5800 can be performed in at least one metering device (e.g., Figure 1A The system is implemented on a processor of 121) and / or remote from at least one metering device in at least one of a cloud-based system, on-site software, a gateway, or another head-end system.

[0512] like Fig.58 As shown, method 5800 begins at block 5805, in which the phase of each new power quality event in the electrical system is calculated. For example, the phase of each new event can be coordinated by calculating the aggregated phase of each new power quality event (resulting in an average, maximum, minimum, etc. of "all phases average", "worst phase", "unaffected phase", etc.). According to some aspects of the present disclosure, these phases refer to the phases of a three-phase system here. A power quality event can originate from any one, two, or all three phases; however, as the power quality event develops, it can move to (involve) any other phase. Aggregating the phases includes evaluating the power quality event from any one or two phases with respect to all three phases in the system. This method can also be used for single-phase systems with multiple legs (e.g., a typical house). According to some aspects of the present disclosure, the electrical system can be viewed through a three-phase lens that provides energy to the electrical system. Aggregation (and dynamic tolerance curves) can occur on each phase, independent of the other two phases. Therefore, each phase in a three-phase system can be shown with its own dynamic tolerance curve, either for a single metering location or from a system perspective. For example, a dynamic tolerance curve is generated for phase 'A' at a specific metering location, or for phase 'A' in an electrical system that contains more than one metering device. These 'devices' can also include virtual meters.

[0513] At block 5810, an initial impact dynamic tolerance curve is generated or derived for each metering location in the electrical system. Additionally, at block 5815, a final impact dynamic tolerance curve is generated or derived for each metering location in the electrical system. In some embodiments, the initial impact dynamic tolerance curve corresponds to a dynamic tolerance curve generated or derived from electrical measurement data captured by a metering device at each metering location at a first initial time. Additionally, in some embodiments, the final impact dynamic tolerance curve corresponds to a dynamic tolerance curve generated or derived from electrical measurement data captured by a metering device at each metering location at a second time after the first time. Both the initial impact dynamic tolerance curve and the final impact dynamic tolerance curve may indicate an impact of a power quality event at a metering point in the electrical system.

[0514] At block 5820, a sensitivity function for the dynamic tolerance curve may be calculated or approximated. In some embodiments, the sensitivity function may be calculated or approximated based on the progression of load loss as the voltage gradually decreases between the initial impact and the final impact.

[0515] At block 5825, an acceptability threshold for each meter point or location may be defined or inferred from a marker (e.g., a system or user marker) in the dynamic tolerance curve. For example, an "unacceptable impact" may be equivalent to the "initial impact dynamic tolerance curve," but for a particular meter, a 30% load loss may be considered acceptable, while other meters may have a 10% acceptability threshold. According to some aspects of the present disclosure, in the vast majority of cases, a load loss is either "acceptable" or "unacceptable." For example, when a meter is monitoring multiple loads that contain both "acceptable" and "unacceptable" conditions, such a combination of "acceptable" and "unacceptable" impacts may result in unique thresholds at discrete meter locations.

[0516] At block 5830 (optional in some embodiments), other systems (such as BMS, power SCADA, manufacturing SCADA) can add new criticality scores (especially if there is no user or system tag). For example, in the case of a manufacturing site with an industrial SCADA system, the criticality score can be calculated based on load loss, for example, without additional user input. In some embodiments, the criticality score of each meter can be inferred from the load loss. For example, "no load loss" can correspond to a criticality score of 0, "20% initial load loss" can correspond to a criticality score of 20, "30% initial load loss" can correspond to a criticality score of 30, and so on. In some embodiments, the industrial SCADA system can be linked to the power SCADA system, and some industrial processes can be mapped with some loads and meters in the electrical system. For example, when a voltage sag is measured, the meters responsible for stopping the critical processes in the electrical system can be identified. This information can be added and fed to the power SCADA system, and the criticality scores of these meters can be updated (e.g., from 0 to 100).

[0517] At block 5835, a dynamic tolerance curve for criticality modeling is generated. For example, criticality marks can be modeled by the system using expert rules or by feeding them into a supervised learning algorithm or other artificial intelligence (AI) tool to establish a criticality model for each meter or meter point. In some embodiments, the criticality model can coordinate potential differences on an event-by-event basis and across all events marked by meters, users, and other systems. Therefore, the output is a dynamic tolerance curve (or curves) for criticality modeling. According to some embodiments, the (multiple) dynamic tolerance curves for criticality modeling can be generated using simple rules (such as the rules above) or other more complex calculations, for example, in response to one or more user inputs. For example, the impact of voltage sags on some production quality metrics can be monitored to determine how voltage sags affect the process (e.g., from 2 defective parts per million to 100 defective parts per million (ppm)). In parallel, users can mark IEDs. IEDs linked to production can correct the original user criticality scores by new "ppm-related scores". In some embodiments, "ppm" instead of "load loss" may be applied as the dynamic tolerance curve threshold calculation.

[0518] After block 5835, in some embodiments, the method can end. In other embodiments, as will be appreciated by one of ordinary skill in the art, the method can include one or more additional steps.

[0519] refer to Fig.59 , a flowchart illustrating an example method 5900 for utilizing and combining dynamic tolerance curves. Similar to method 5800, method 5900 may be performed in at least one metering device (e.g., Figure 1A The system is implemented on a processor of 121) and / or remote from at least one metering device in at least one of a cloud-based system, on-site software, a gateway, or another head-end system.

[0520] like Fig.59 As shown, method 5900 begins at block 5905, in which meter (or metering device) rankings and groups are determined or defined. As described above, each metering device (e.g., IED, virtual metering device, etc.) in the electrical system can be associated with a corresponding metering location (physical or virtual) in the electrical system. In some embodiments, meter rankings and groups are defined using different meter criticalities defined for each meter based on meter criticality scores (when available). In addition, in some embodiments, meter rankings and groups can be defined using relative acceptability levels (or unacceptability) of different event markers. Manual configuration is also possible.

[0521] Example rankings according to the present disclosure include, but are not limited to:

[0522] “Initial impact” ranking based on magnitude value, e.g., starting with the most sensitive to initial impact (e.g., amount of load drop) and ranking metering devices starting at 80% of nominal voltage, and in one example, to the last metering device where the initial impact starts at only 45% of nominal voltage.

[0523] The “Final Impact” ranking is similar to the “Initial Impact” ranking, except that the magnitude of the final impact is used to rank the metered devices.

[0524] • For a "significant load loss" predetermined value (eg, 25% load loss), the magnitude of the load loss may be used to rank metering devices.

[0525] · Ranking related to "criticality score" may be another possible ranking.

[0526] Ranking related to “load size”, e.g. using the largest energy consumption and ranking in descending order.

[0527] Example groupings according to the present disclosure include, but are not limited to:

[0528] By load type (e.g., HVAC, motor, lighting, etc.).

[0529] By physical or geographic location (by park, by building, by floor, by zone, etc.).

[0530] By electrical hierarchy branch or level (e.g., main meter, branch meter, sub-meter, etc.).

[0531] • By sensitivity (eg, in one possible implementation, based on an "initial impact" ranking, using clustering techniques to infer coherent groups).

[0532] At block 5910, the dynamic tolerance curves associated with each metering device (and metering point) are aggregated, for example, based on the rankings and groups defined at block 5905. For example, at block 5905, each metering device may be grouped based on three criticality levels (e.g., high, medium, and low), as described below.

[0533] High: IEDs monitoring process steps where voltage sags could interrupt critical process steps, generating lengthy process outages and expensive waste of raw or semi-converted materials.

[0534] Medium: IEDs monitoring process steps where voltage sags result in non-quality production costs, but do not cause major damage even during an outage.

[0535] • Low: IEDs monitoring process steps where disruptions may be ridden through without any identified or measurable impact.

[0536] System users may wish to have two (or more) different dynamic tolerance curves to identify the or...

Claims

1. A method for analyzing power quality events in an electrical system, comprising: processing electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices to generate or update a plurality of dynamic tolerance curves, wherein each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a respective one of a plurality of metering points in the electrical system; and Selectively aggregate power quality data from multiple dynamic tolerance curves; analyzing power quality events in the electrical system based on selectively aggregated power quality data; and One or more parameters, processes, conditions, or loads associated with the electrical system are adjusted or controlled in response to the analyzed power quality events.

2. The method according to claim 1, wherein: The plurality of dynamic tolerance curves are generated or updated by deriving relevant signal characteristics from electrical measurement data.

3. The method according to claim 2, wherein: The relevant signal characteristics include at least one of: amplitude, duration, power quality type, time of occurrence, process involved, location, equipment affected, relative or absolute impact, recovery time, and event period or event type.

4. The method according to claim 2, wherein: The relevant signal characteristic includes at least one of: an identified change in current or phase shift that results in one or more interpretations.

5. The method according to claim 4, wherein: The interpretation or interpretations include the type of inductive or capacitive loads added or removed from the electrical system during the event and / or the percentage of pre-event loads added or removed from the electrical system.

6. The method according to claim 2, wherein: The relevant signal characteristic is determined from a portion of the electrical measurement data before a start time of the power quality event and a portion of the electrical measurement data after an end time of the power quality event.

7. The method according to claim 2, wherein: The relevant signal characteristic is determined from a portion of the electrical measurement data during the power quality event.

8. The method according to claim 1, wherein: The plurality of dynamic tolerance curves are generated or updated after a learning period.

9. The method according to claim 8, wherein: The threshold value for triggering an alarm is generated during the generation or updating of the dynamic tolerance curve, during the learning period or after the learning period.

10. The method according to claim 9, wherein: The learning period continues until there is enough data to generate the threshold.

11. The method according to claim 1, wherein: Processing electrical measurement data derived from or derived from energy-related signals captured by a plurality of metering devices in an electrical system to generate or update a plurality of dynamic tolerance curves, including: processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices at a first initial time to generate or derive an initial impact dynamic tolerance curve for each metering point in the electrical system; and processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices at a second time subsequent to a first initial time to generate or derive a final impact dynamic tolerance curve for each metering location in the electrical system, The plurality of dynamic tolerance curves include an initial impact dynamic tolerance curve and a final impact dynamic tolerance curve, and the initial impact dynamic tolerance curve and the final impact dynamic tolerance curve indicate the impact of a power quality event at a metering point in the electrical system.

12. The method according to claim 11, further comprising: The sensitivity functions of the plurality of dynamic tolerance curves are calculated based on a progression of load loss as voltage gradually decreases between an initial impact and a final impact indicated by the initial impact dynamic tolerance curve and the final impact dynamic tolerance curve.

13. The method according to claim 12, further comprising: An acceptability threshold for each meter point is defined or inferred from the markings in the plurality of dynamic tolerance curves, the acceptability threshold indicating an acceptable or unacceptable influence or condition for each meter point.

14. The method according to claim 12, further comprising: calculating a criticality score based on the load loss and marking the criticality score with an associated criticality mark; and Criticality modeling dynamic tolerance curves are generated in response to using expert rules or feeding criticality markers into a supervised learning algorithm or other artificial intelligence (AI) tool to build a criticality model for each of a plurality of metrology devices or a plurality of metrology points.

15. The method according to claim 1, wherein: The one or more parameters, processes, conditions, or loads are dynamically adjusted or controlled by a control system associated with the electrical system.

16. The method according to claim 1, wherein: The loads include a plurality of loads monitored by a plurality of metering devices.

17. The method according to claim 16, further comprising: evaluating the initial impact threshold and / or the final impact threshold and positions between the initial impact threshold and / or the final impact threshold to determine a percentage of a plurality of load losses in response to each of the power quality events, Wherein, the initial impact threshold corresponds to a threshold for detecting a first percentage of the plurality of loads being lost, and the final impact threshold corresponds to a threshold for detecting when all of the plurality of loads are lost.

18. The method according to claim 17, wherein: The position between the initial impact threshold and / or the final impact threshold is determined using one or more models.

19. The method according to claim 18, wherein: The one or more models include at least one of a linear model, a logarithmic model, and a curvilinear model.

20. The method according to claim 19, wherein: The curve model is determined by modeling line or curve calculation or curve fitting algorithm.

21. The method according to claim 18, wherein: The one or more models are used to approximate how the electrical system responds to each of the power quality events.

22. The method according to claim 21, wherein: The power quality events include voltage events, and the one or more models are used to interpolate effects of the voltage events on the electrical system.

23. A system for analyzing power quality events in an electrical system, comprising: at least one input coupled to at least a plurality of metering devices in the electrical system; at least one output coupled to at least a plurality of loads monitored by a plurality of metering devices; and a processor coupled to receive electrical measurement data from at least one system input or derived from energy-related signals captured by a plurality of metering devices, the processor being configured to: processing the electrical measurement data to generate or update a plurality of dynamic tolerance curves, wherein each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a corresponding meter point of a plurality of meter points in the electrical system; Selectively aggregate power quality data from multiple dynamic tolerance curves; analyzing power quality events in the electrical system based on the selectively aggregated power quality data; and In response to the analyzed power quality event, one or more parameters, processes, conditions, or loads associated with the electrical system are adjusted or controlled.

24. The system of claim 23, wherein: The plurality of dynamic tolerance curves are generated or updated by deriving relevant signal characteristics from electrical measurement data.

25. The system of claim 24, wherein: The relevant signal characteristics include at least one of: amplitude, duration, power quality type, time of occurrence, process involved, location, equipment affected, relative or absolute impact, recovery time, and event period or event type.

26. The system of claim 24, wherein: The relevant signal characteristic includes at least one of: an identified change in current or phase shift that results in one interpretation or multiple interpretations.

27. The system of claim 26, wherein: The interpretation or interpretations include the type of inductive or capacitive loads added or removed from the electrical system during the event and / or the percentage of pre-event loads added or removed from the electrical system.

28. The system of claim 24, wherein: The relevant signal characteristic is determined from a portion of the electrical measurement data before a start time of the power quality event and a portion of the electrical measurement data after an end time of the power quality event.

29. The system of claim 24, wherein: The relevant signal characteristic is determined from a portion of the electrical measurement data during the power quality event.

30. The system of claim 23, wherein: The plurality of dynamic tolerance curves are generated or updated after a learning period.

31. The system of claim 30, wherein: The threshold value for triggering an alarm is generated during the generation or updating of the dynamic tolerance curve, during the learning period or after the learning period.

32. The system of claim 31, wherein: The learning period continues until there is enough data to generate the threshold.

33. The system of claim 23, wherein: Processes electrical measurement data to generate or update multiple dynamic tolerance curves, including: processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices at a first initial time to generate or derive an initial impact dynamic tolerance curve for each metering point in the electrical system; and processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices at a second time subsequent to a first initial time to generate or derive a final impact dynamic tolerance curve for each metering location in the electrical system, The plurality of dynamic tolerance curves include an initial impact dynamic tolerance curve and a final impact dynamic tolerance curve, and the initial impact dynamic tolerance curve and the final impact dynamic tolerance curve indicate the impact of a power quality event at a metering point in the electrical system.

34. The system of claim 33, wherein: The processor is further configured to: The sensitivity functions of the plurality of dynamic tolerance curves are calculated based on a progression of load loss as voltage gradually decreases between an initial impact and a final impact indicated by the initial impact dynamic tolerance curve and the final impact dynamic tolerance curve.

35. The system of claim 34, wherein: The processor is further configured to: An acceptability threshold for each meter point is defined or inferred from the markings in the plurality of dynamic tolerance curves, the acceptability threshold indicating an acceptable or unacceptable influence or condition for each meter point.

36. The system of claim 34, wherein: The processor is further configured to: calculating a criticality score based on the load loss and marking the criticality score with an associated criticality mark; and Criticality modeling dynamic tolerance curves are generated in response to using expert rules or feeding criticality markers into a supervised learning algorithm or other artificial intelligence (AI) tool to build a criticality model for each of a plurality of metrology devices or a plurality of metrology points.

37. The system of claim 23, wherein: The one or more parameters, processes, conditions, or loads are dynamically adjusted or controlled by a control system associated with the electrical system.

38. The system of claim 23, wherein: The loads include a plurality of loads monitored by a plurality of metering devices.

39. The system of claim 38, wherein: The processor is further configured to: evaluating the initial impact threshold and / or the final impact threshold and positions between the initial impact threshold and / or the final impact threshold to determine a percentage of a plurality of load losses in response to each of the power quality events, Wherein, the initial impact threshold corresponds to a threshold for detecting a first percentage of the plurality of loads being lost, and the final impact threshold corresponds to a threshold for detecting when all of the plurality of loads are lost.

40. The system of claim 39, wherein: The position between the initial impact threshold and / or the final impact threshold is determined using one or more models.

41. The system of claim 40, wherein: The one or more models include at least one of a linear model, a logarithmic model, and a curvilinear model.

42. The system of claim 41, wherein: The curve model is determined by modeling line or curve calculation or curve fitting algorithm.

43. The system of claim 40, wherein: The one or more models are used to approximate how the electrical system responds to each of the power quality events.

44. The system of claim 43, wherein: The power quality events include voltage events, and the one or more models are used to interpolate effects of the voltage events on the electrical system.

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