System and method for automatically characterizing disturbances in an electrical system

Abstract

A method for automatically classifying disturbances in an electrical system includes capturing at least one energy-related waveform using at least one intelligent electronic device in the electrical system, and processing electrical measurement data in or derived from the at least one energy-related waveform to identify a disturbance in the electrical system. In response to identifying a disturbance in the electrical system, each sample of the at least one energy-related waveform associated with the identified disturbance is analyzed and classified into one of a plurality of disturbance categories. For example, the disturbance categories can include (a) voltage sag due to upstream line electrical system disturbance, (b) voltage sag due to downstream line electrical system fault, (c) voltage sag due to downstream line transformer and / or motor magnetization, and (d) voltage sag due to other downstream line disturbance.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Application No. 62 / 870,305, filed July 3, 2019, pursuant to 35 USC §119(e), which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure generally relates to electrical / power systems, and more specifically, to systems and methods for automatically characterizing disturbances in electrical systems. Background Technology

[0004] As is well known, power quality issues have one of the most significant and costly impacts on electrical systems (sometimes referred to as "electrical networks"). The Leonardo Power Quality Working Group estimates that poor power quality costs the European economy up to €150 billion annually. 1 . Furthermore, according to research by the Electric Power Research Institute (EPRI), the annual losses suffered by the U.S. economy range from $119 billion to $188 billion. 2 Perhaps the most important statistic is EPRI's finding that 80% of power quality disturbances originate within the facility. An exemplary economic model summarizes the total costs associated with power quality events as follows:

[0005] Total loss = Production loss + Restart loss + Product / material loss + Equipment loss + Third-party fees + Other miscellaneous expenses 3

[0006] Other miscellaneous costs associated with power quality issues may include intangible losses, such as damage to the reputation of customers and suppliers, or more direct losses, such as depreciation of credit ratings and stock prices. Summary of the Invention

[0007] Systems and methods related to automatically classifying or categorizing disturbances in an electrical system are described herein. For example, the electrical system can be associated with at least one load, process, building, facility, vessel, aircraft, or other type of structure. In one aspect of the disclosure, a method for automatically classifying disturbances (or perturbations) in an electrical system includes capturing energy-related waveforms using at least one intelligent electronic device (IED) in the electrical system. For example, the at least one energy-related waveform can include at least one of: a voltage waveform, a current waveform, and other waveforms and / or data (e.g., power) derived from the voltage and / or current waveforms. According to some embodiments of the disclosure, the voltage and current waveforms can include at least one of: single and three-phase voltage and current waveforms. These waveforms can be sampled at various rates, for example, at a rate of about 100 Hz or higher. In one example implementation, the rate is about 1.6 kHz.

[0008] The method also includes processing electrical measurement data in or derived from the at least one energy-related waveform to identify a disturbance in the electrical system. In response to identifying a disturbance in the electrical system, each sample of the energy-related waveforms associated with the identified disturbance is analyzed and classified into one of a plurality of disturbance categories. The disturbance categories can include, for example, (a) voltage sag due to an upstream line electrical disturbance, (b) voltage sag due to a downstream line electrical system fault, (c) voltage sag due to a downstream line transformer and / or motor magnetization, and (d) voltage sag due to other downstream line disturbances.

[0009] The method also includes determining, based on the class of each sample of the at least one energy-related waveform, a disturbance class of the at least one energy-related waveform associated with the identified disturbance. The disturbance class may, for example, be selected from one of a class of disturbances. In some embodiments, at least one action can be taken based on the disturbance class of the at least one energy-related waveform. For example, the action can include at least one of communicating the disturbance class and controlling at least one component in the electrical system. In one example case, where an electrical system device trips offline due to a current magnitude of a voltage sag or inrush event, an operator of the electrical system who is informed that it was an inrush if the waveform measurement is determined by the method to be an inrush measurement rather than a fault, can power the system back on without first having to complete a time-consuming electrical fault search. In some embodiments, the decision to power the system back on can be done manually by a human operator and / or as part of an automated electrical control system. As is well known, electrical events such as faults can cause undesirable effects on the electrical system and systems, devices, and / or equipment associated with and / or in close proximity to the electrical system. According to embodiments of the present disclosure, the method can eliminate or at least significantly reduce the undesirable effects (e.g., a manhole explosion caused by a fault in an underground electrical device), for example, as a result of the at least one action taken.

[0010] The method may, for example, be implemented on at least one of the following: at least one IED or other power meter, a microprocessor relay, an edge server software or gateway that can collect measurements from monitoring devices, and / or a cloud-based advisory service. It should be understood that these are just a few of the many possible ways in which the method can be implemented.

[0011] It should also be understood that the captured at least one energy-related waveform described above in connection with the method (and other methods and systems discussed below) can be associated with an energy-related signal captured or measured by the at least one IED. For example, according to some embodiments of the present disclosure, the captured at least one energy-related waveform can be generated from the at least one energy-related signal captured or measured by the at least one IED. According to IEEE Standard 1057-2017, a waveform is “a representation or depiction (e.g., a graph, plot, oscilloscope presentation, discrete-time sequence, equation, table of coordinates, or statistical data) or visualization of a signal.” In view of this definition, the at least one energy-related waveform can correspond to a representation or depiction or visualization of the at least one energy-related signal. It should be understood that the above relationship is based on one standard body’s (in this case, IEEE’s) definition of a waveform, and other relationships between waveforms and signals are of course possible, as will be understood by one of ordinary skill in the art.

[0012] The above method and other methods (and systems) described below can individually include one or more of the following features, alone or in combination with other features in some embodiments. Processing electrical measurement data in or derived from the at least one energy-related waveform to identify a disturbance in the electrical system can include determining voltage and current phase information of the electrical measurement data associated with the disturbance, and analyzing the voltage and current phase information to determine whether the source of the disturbance is electrically upstream or electrically downstream of at least one electrical node or location in the electrical system to which the IED is electrically coupled.

[0013] Additionally or alternatively, processing the electrical measurement data can include grouping the electrical measurement data based on electrical nodes and locations in the electrical system associated with the at least one energy-related waveform, and processing the grouped electrical measurement data to identify a disturbance at the electrical nodes or locations. For example, the electrical measurement data can be grouped using a temporal clustering technique such that measurements occurring close in time are grouped together. A sampling rate at which the at least one energy-related waveform associated with the electrical measurement data was captured can be determined, and a sampling rate of the electrical measurement data can be adjusted to a desired sampling rate to at least one of calibrate and group the electrical measurement data. For example, the sampling rate of the electrical measurement data can be adjusted by upsampling, downsampling, and / or resampling the electrical measurement data. According to some embodiments of the disclosure, the desired sampling rate is a lowest sampling rate of the sampling rate at which the at least one energy-related waveform associated with the electrical measurement data was captured.

[0014] According to some embodiments of the disclosure, the electrical measurement data in or derived from the at least one energy-related waveform captured by the at least one IED can be processed on at least one of: a cloud-based system, on-site software, a gateway, and other front-end systems associated with the electrical system. The at least one IED can be communicatively coupled to at least one of: the cloud-based system, the on-site software, the gateway, and the other front-end systems on which the electrical measurement data is processed.

[0015] Determining the disturbance class of the at least one energy-related waveform can include analyzing a class of each sample of the at least one energy-related waveform to formulate a confidence factor of the disturbance class of the at least one energy-related waveform, and determining the disturbance class of the at least one energy-related waveform in response to the confidence factor of the disturbance characterization satisfying a threshold. According to some embodiments of the disclosure, analyzing the class of each sample of the energy-related waveform includes identifying a class pattern of the energy-related waveform samples.

[0016] Taking one or more actions based on the disturbance category can include triggering one or more alarms based on the disturbance category. According to some embodiments of the disclosure, the alarms are prioritized based on the importance / criticality of the electrical node or location from which the disturbance originated. Additionally, according to some embodiments of the disclosure, the alarms are prioritized based on the size of the load measured at the electrical node or location from which the disturbance originated. In some embodiments, in response to triggering the alarms, the disturbance can be reported and / or at least one component in the electrical system can be operated as a response to the disturbance to prevent or reduce damage to the electrical system devices. The one or more actions can be, for example, automatically performed by a control system associated with the electrical system. The control system can be communicatively coupled to at least one IED and / or a cloud-based system, on-premise / edge software, gateways, and other front-end systems associated with the electrical system.

[0017] A corresponding system for automatically classifying disturbances in an electrical system is also provided herein. In particular, in one aspect, a system for automatically classifying disturbances in an electrical system includes at least one processor and at least one memory device coupled to the at least one processor. The at least one processor and the at least one memory device are configured to process electrical measurement data in or derived from at least one energy-related waveform captured by at least one IED in the electrical system to identify a disturbance in the electrical system. The at least one processor and the at least one memory device are further configured to analyze each sample of the at least one energy-related waveform associated with the identified disturbance and classify it into one of a plurality of disturbance categories. Similar to the method described above, the disturbance categories can include, for example, (a) voltage sag due to upstream line electrical system disturbance, (b) voltage sag due to downstream line electrical system fault, (c) voltage sag due to downstream line transformer and / or motor magnetization, and (d) voltage sag due to other downstream line disturbance.

[0018] The at least one processor and the at least one memory device are additionally configured to determine, based on the category of each sample of the at least one energy-related waveform, a disturbance category of the at least one energy-related waveform associated with the identified disturbance. Similar to the method described above, the disturbance category can be, for example, selected from one of the disturbance categories. At least one action can be taken by the at least one processor and the at least one memory device (or other systems and devices in the electrical system) based on the disturbance category of the at least one energy-related waveform. In some embodiments, the one or more actions include generating an output signal in accordance with the disturbance category, and providing the output signal to at least one device for further processing. In some embodiments, the at least one device includes at least one of: the at least one IED, a control system associated with the electrical system, a cloud-based system, on-premise / edge software, gateways, and other front-end systems associated with the electrical system.

[0019] In some embodiments, the above-described system can correspond to a control system for monitoring or controlling one or more parameters associated with an electrical system. In some embodiments, the control system can be a meter, an IED (e.g., an IED in an IED responsible for capturing energy related waveforms), a field / front-end software (i.e., a software system), a cloud-based control system, a gateway, a system that routes data over an Ethernet or some other communication system, etc.

[0020] As used herein, an IED is a computing electronic device optimized to perform a particular function or set of functions. Examples of IEDs include smart utility meters, power quality meters, microprocessor relays, digital fault recorders, and other metering devices. IEDs can also be embedded in variable speed drives (VSDs), uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical device. IEDs can be used to perform monitoring and control functions in a variety of installation facilities. These installation facilities can include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing hosting centers, data centers, power distribution networks, or any other structure, process, or load that uses electrical energy. For example, where the IED is a power monitoring device, it can be coupled to (or installed in) an electrical power transmission or distribution system and configured to sense / measure and store data as electrical parameters representative of operating characteristics of the electrical distribution system (e.g., voltage, current, waveform distortion, power, etc.). These parameters and characteristics can be analyzed by a user to assess potential performance, reliability, or power quality related issues. An IED can include at least a controller (which can be configured to run one or more applications simultaneously, serially, or both in certain IEDs), firmware, memory, a communication interface, and connectors to connect the IED to external systems, devices, and / or components at any voltage level, configuration, and / or type (e.g., AC, DC). At least certain aspects of the monitoring and control functions of an IED can be embodied in computer programs accessible by the IED.

[0021] In some embodiments, the term "IED" used herein can refer to a hierarchy of IEDs operating in parallel and / or in series. For example, the IEDs can correspond to a hierarchy of energy meters, power meters, and / or other types of source meters. The hierarchy can include a tree-based hierarchy, such as a binary tree, a tree with one or more child nodes descending from each parent or node, or a combination thereof, where each node represents a particular IED. In some instances, the hierarchy of IEDs can share data or hardware resources, and can execute shared software. It should be appreciated that the hierarchy can be non-spatial, such as a billing hierarchy, where the IEDs grouped together can be physically unrelated.

[0022] In some embodiments, the metering devices (e.g., IEDs) and apparatuses / loads of the systems and methods described above and below are installed, positioned, or sourced from different individual locations (i.e., multiple locations) or metering points in the electrical system. For example, a particular IED (e.g., a second IED) can be upline (or upstream) of another IED (e.g., a third IED) in the electrical system, while being downline (or downstream) of yet another IED (e.g., a first IED) in the electrical system.

[0023] As used herein, the terms “upline” and “downline” (sometimes also referred to as “upstream” and “downstream,” respectively) are used to refer to electrical locations within an electrical system. More specifically, the electrical locations “upline” and “downline” relate to the electrical location of an IED that collects data and provides that information. For example, in an electrical system that includes multiple IEDs, one or more IEDs can be positioned (or installed) at an electrical location that is upline with respect to one or more other IEDs in the electrical system, and the one or more IEDs can be positioned (or installed) at an electrical location that is downline with respect to one or more additional IEDs in the electrical system. A first IED or load positioned upline on a circuit of a second IED or load, for example, can be positioned electrically closer to an input or source (e.g., a generator or utility feed) of the electrical system than the second IED or load. Conversely, a first IED or load positioned downline on a circuit of a second IED or load can be positioned closer to the end or termination of the electrical system than the other IED.

[0024] In embodiments, a first IED or load electrically connected in parallel (e.g., on a circuit) with a second IED or load can be considered to be “electrically” upline of the second IED or load, and vice versa. In embodiments, the algorithm for determining the direction (i.e., upline or downline) of a power quality event is located (or stored) in an IED, a cloud, on-site software, a gateway, etc. As one example, an IED can record voltage and current phase information (e.g., by sampling the individual signals) of an electrical event and communicatively transmit this information to a cloud-based system. The cloud-based system can then analyze the voltage and current phase information (e.g., instantaneous, root mean square (rms), waveforms, and / or other electrical characteristics) to determine whether the source of the power quality event is electrically upline or downline of the IED electrically coupled to the electrical system (or network).

[0025] It should be understood that there are multiple types of power quality events and that these types of power quality events have certain characteristics. For example, a power quality event can include at least one of a voltage sag, a voltage swell, and a voltage transient. According to IEEE Standard 1159-2019, for example, a voltage sag is a decrease in rms voltage or current at power frequency between 0.1 and 0.9 per unit (pu) with a duration of 0.5 cycles to 1 minute. Typical values are 0.1 to 0.9 pu. Further, according to IEEE Standard 1159-2019, a voltage swell is an increase in rms voltage or current at power frequency with a duration of 0.5 cycles to 1 minute. Below is a table from IEEE Standard 1159-2019 (known art) that defines various categories and characteristics of power system electromagnetic phenomena.

[0026]

[0027]

[0028] a The parameter pu refers to per unit, which is dimensionless. The parameter 1.0 pu corresponds to 100%. Nominal conditions are typically considered to be 1.0 pu. In this table, the nominal peak value is used as the base for transients, and the nominal rms value is used as the base for rms variations.

[0029] b Flicker severity index, P st as defined in IEC 61000-4-15:2010 [B17] and IEEE Standard 1453 TM [B31].

[0030] It should be understood that the above table is a way that a standard body (in this case, IEEE) 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 can have different descriptions or power quality event types, characteristics, and terminology. It should also be understood that the types and descriptions of power quality events can change over time, and the systems and methods disclosed herein are intended to apply to current and future types and descriptions of power quality events. According to embodiments of the present disclosure, the power quality event can additionally or alternatively be a custom power quality event (e.g., defined by a user).

[0031] Among the seven recognized categories of power quality defined by IEEE 1159-2019, short duration root mean square (rms) variations are generally the most disruptive and have the most widespread economic impact on energy consumers. Short duration rms variations include voltage sags / dips, swells, momentary interruptions, and brief interruptions. An example study by the Electric Power Research Institute (EPRI) estimates that industrial customers experience an average of about 66 voltage sags per year. As the trend of industries becoming more dependent on sag-sensitive equipment grows, the impact of these events is also increasing.

[0032] The most common cause of voltage sags is a short circuit in the electrical system, also known as an electrical system fault, whether on the upward line at the monitored location (that is, toward the source of power) or on the downward line at the monitored location (that is, away from the source). Voltage sags can also be due to energization of a power transformer or start-up of a large load. When the source of the voltage sag is on the downward line at the monitored location, different voltage and current waveform signatures will be measured based on whether the cause of the voltage sag is due to a fault, due to a large inrush current associated with the energization of a transformer and / or motor, or due to start-up of a large load.

[0033] The systems and methods disclosed herein automatically differentiate / classify voltage sag measurements (and other types of measurements) into multiple categories. For example, with respect to voltage sag measurements, the systems and methods disclosed herein can classify voltage sag measurements as voltage sags due to upward line disturbances, voltage sags due to downward line faults, voltage sags due to downward line transformer and / or motor energization inrush, and voltage sags due to other downward line disturbances that do not have fault or inrush characteristics, as a few examples.

[0034] It is important to be able to differentiate between downward line fault events and downward line inrush events because it allows the operator of the electrical system or electrical facility to take appropriate action when other power equipment trips offline due to a voltage sag or when protective equipment operates due to a large current inrush. If a notification of the presence of a fault is provided with the meter firmware or server software application of the present invention, the operator of the affected equipment (and / or the system on which the disclosed methods are implemented) can choose not to restart the equipment until the fault location has been identified and possibly repaired. However, if an inrush measurement or large load start-up is detected, the affected equipment can be restarted without having to complete a time-consuming search for a non-existent fault.

[0035] It should be appreciated that many other advantages associated with the disclosed systems and methods will be appreciated from the following discussion. BRIEF DESCRIPTION OF DRAWINGS

[0036] The foregoing features of the present disclosure, and other aspects thereof, are apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0037] Figure 1 An example electrical system is shown in accordance with embodiments of the present disclosure;

[0038] Figure 2 An example intelligent electronic device (IED) that can be used in an electrical system is shown in accordance with embodiments of the present disclosure;

[0039] Figure 3 is a flowchart showing an example method for automatically classifying disturbances in an electrical system in accordance with embodiments of the present disclosure;

[0040] Figures 4-4C is a flowchart showing another example method for automatically classifying disturbances in an electrical system in accordance with embodiments of the present disclosure;

[0041] Figure 5 Example voltage and current waveforms recorded during a transformer inrush are shown;

[0042] Figure 6 Example current waveforms recorded during a motor inrush are shown;

[0043] Figure 7 Example voltage and current waveforms recorded during a single-phase fault event are shown;

[0044] Figure 8 Example voltage and current waveforms recorded during a transformer inrush followed by a single-phase fault are shown;

[0045] Figure 9 Example voltage and current waveforms recorded during an upline voltage sag are shown;

[0046] Figure 10 Example voltage and current waveforms recorded during a load start are shown; and

[0047] Figure 11 Example voltage and current root mean square (rms) samples recorded during a load start are shown. DETAILED DESCRIPTION

[0048] Features and other details of the concepts, systems, and techniques sought to be protected herein will now be more fully described with reference to the accompanying drawings. It should be understood that any specific implementation described herein is illustrative, and not restrictive, of the disclosure and concepts described herein. Features of the subject matter described herein can be employed in various embodiments without departing from the scope of the concepts sought to be protected.

[0049] For convenience, certain introductory concepts and terminology used in this specification (and adopted from or derived from IEEE Standard 1159-2019) are collected here.

[0050] As used herein, the term "non-periodic event" is used to describe an electrical event that occurs non-cyclically, arbitrarily, or without a specific time regularity. For purposes herein, both short duration root mean square (rms) variations and transients are considered non-periodic events (i.e., dips are considered harmonic phenomena).

[0051] As used herein, the term "momentary interruption" is used to describe a deviation of 0-10% from the nominal value with a duration of 1 / 2 cycle to 3 seconds.

[0052] As used herein, the term "dip" (of which "voltage dip" is an example) is used to describe, for example, a deviation of 10-90% from the nominal value with a duration of 1 / 2 cycle to 1 minute.

[0053] As used herein, the term "short duration rms variation" is used to describe a deviation from the nominal value with a duration of 1 / 2 cycle to 1 minute. Subcategories of short duration rms variation events include momentary interruption, momentary interruption, dip, and swell.

[0054] As used herein, the term "swell" is used to describe, for example, a deviation of more than 110% from the nominal value with a duration of 1 / 2 cycle to 1 minute.

[0055] As used herein, the term "momentary interruption" is used to describe a deviation of 0-10% from the nominal value with a duration of 3 seconds to 1 minute.

[0056] As used herein, the term "transient" is used to describe a deviation from the nominal value with a duration of less than 1 cycle. Subcategories of transients include impulse (unidirectional polarity) and oscillatory (bidirectional polarity) transients.

[0057] As used herein, the term "voltage dip due to upstream electrical system disturbance" is used to describe a temporary reduction in voltage caused by an electrical short circuit or a large load start-up on the upstream line (towards the electrical source) at the electrical monitoring location. For example, as discussed further below, Figure 9 An example voltage dip due to an upstream electrical system disturbance is shown.

[0058] As used herein, the term "voltage sag due to downline electrical system fault" is used to describe a temporary voltage reduction caused by an increase in current due to an electrical system fault. This is caused by accidental grounding of a circuit due to lightning, insulation failure, device failure, interference from trees or cars, vandalism, fire, etc. The larger current during the fault causes a larger voltage drop across the impedance of the conductors supplying the load, resulting in a lower than expected voltage. For example, as discussed further below, Figure 7 and 8 An example voltage sag due to downline electrical system fault is shown.

[0059] As used herein, the term "voltage sag due to downline transformer and / or motor magnetization" is used to describe a temporary voltage reduction caused by an increase in current due to normal energization of a power transformer or normal magnetization of an AC induction motor. These loads can cause large, harmonic-rich current waveforms during energization or start-up. The larger current during energization causes a larger voltage drop across the impedance of the conductors supplying the load, resulting in a lower than expected voltage. For example, as discussed further below, Figure 5 and 6 An example voltage sag due to downline transformer and / or motor magnetization is shown.

[0060] As used herein, the term "voltage sag due to other downline disturbances" is used to describe a temporary voltage reduction caused by, for example, the start-up of lighting, heating, other motors, appliances, power supplies, etc. For example, as discussed further below, Figure 10 and Figure 11 An example voltage sag due to other downline disturbances is shown. It should be understood that this category can additionally or alternatively include voltage sags associated with normal operation. Further, it should be understood that this category, as well as the other categories described herein, apply to single-phase and / or multi-phase electrical systems.

[0061] In embodiments, the degree of impact of a short-duration rms variation on a facility of an energy consumer, for example, depends primarily on four factors:

[0062] 1. the nature and source of the event,

[0063] 2. the susceptibility of the load to the event,

[0064] 3. the impact of the event on the process or activity, and

[0065] 4. the cost sensitivity to the event.

[0066] Accordingly, each customer system, operation, or load can respond differently to a given electrical perturbation. For example, a voltage sag event can significantly affect one customer's operation, while the same voltage sag can have little or no noticeable effect on another customer's operation. Voltage sags can also have different effects on one portion of a customer's electrical system than on another portion of the same electrical system.

[0067] Referring to Figure 1 , an example electrical system according to embodiments 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 (e.g., power monitoring parameters) associated with the loads. In embodiments, the loads 111, 112, 113, 114, 115 and the IEDs 121, 122, 123, 124 can be installed in one or more buildings or other physical locations, or they can be installed on one or more processes and / or loads within a building. The building can correspond to, for example, a commercial, industrial, or utility building.

[0068] As shown in Figure 1 , the IEDs 121, 122, 123, 124 are each coupled to one or more of the loads 111, 112, 113, 114, 115 (which, in some embodiments, can be located "upstream" or "downstream" of the IEDs). The loads 111, 112, 113, 114, 115 can include, for example, machines or equipment associated with a particular application (e.g., an industrial application), multiple applications, and / or processes. For example, the machines can include electrical or electronic devices. The machines can also include controllers and / or auxiliary devices associated with the devices.

[0069] In embodiments, the IEDs 121, 122, 123, 124 can 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 can also be embedded within 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 utility feeds, including surge protection device (SPD) 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, temporary interruptions, and oscillatory transients, as well as fan failures, temperature, arc faults, phase-to-phase faults, winding shorts, fuses, and harmonic distortion, which are some example parameters that can be associated with the loads 111, 112, 113, 114, 115. The IEDs 111, 112, 113, 114, 115 can also monitor equipment, such as generators, including input / output (I / O), protection relays, battery chargers, and sensors (e.g., water, air, gas, steam, liquid level, accelerometers, flow, pressure, etc.).

[0070] According to another aspect, the IEDs 121, 122, 123, 124 can detect overvoltage and undervoltage conditions, as well as other parameters, such as temperature, including ambient temperature. According to another aspect, the IEDs 121, 122, 123, 124 can provide indications of monitored parameters and detected conditions that can be used to control loads 111, 112, 113, 114, 115 and other devices in an electrical system in which the loads 111, 112, 113, 114 and the IEDs 121, 122, 123, 124 are installed. Various other monitoring and / or control functions can be performed by the IEDs 121, 122, 123, 124, and the aspects and embodiments disclosed herein are not limited to IEDs 121, 122, 123, 124 operating according to the above examples.

[0071] It should be appreciated that the IEDs 121, 122, 123, 124 can take various forms and can each have an associated complexity (or set of functional capabilities and / or features). For example, the IED 121 can correspond to a "base" IED, the IED 122 can correspond to an "intermediate" IED, and the IED 123 can correspond to an "advanced" IED. In such embodiments, the intermediate IED 122 can have more functionality (e.g., energy measurement features and / or capabilities) than the base IED 121, and the advanced IED 123 can have more functionality and / or features than the intermediate IED 122. For example, in an embodiment, the IED 121 (e.g., an IED with base capabilities and / or features) can be capable of monitoring instantaneous voltage, current energy, demand, power factor, average, maximum, instantaneous power, and / or long duration rms variation, while the IED 123 (e.g., an IED with advanced capabilities) can be capable of monitoring additional parameters at a higher sampling rate, such as voltage transients, voltage fluctuations, frequency slew rate, harmonic power flow, and discrete harmonic components, etc. It should be appreciated that this example is for illustrative purposes only, and as such, in some embodiments, an IED with base capabilities can be capable of monitoring one or more of the above energy measurement parameters indicated as being associated with an IED with advanced capabilities. It should also be appreciated that in some embodiments, the IEDs 121, 122, 123, 124 each have independent functionality.

[0072] In the illustrated example embodiment, the IEDs 121, 122, 123, 124 are communicatively coupled to the central processing unit 140 via the "cloud" 150. In some embodiments, the IEDs 121, 122, 123, 124 can be directly communicatively coupled to the cloud 150, as with the IED 121 in the illustrated embodiment. In other embodiments, the IEDs 121, 122, 123, 124 can be indirectly communicatively coupled to the cloud 150, e.g., through an intermediary device such as the cloud-connected hub 130 (or gateway), as with the IEDs 122, 123, 124 in the illustrated embodiment. The cloud-connected hub 130 (or gateway) can, for example, provide the IEDs 122, 123, 124 with access to the cloud 150 and the central processing unit 140.

[0073] As used herein, the terms "cloud" and "cloud computing" are intended to refer to computing resources connected to the Internet via a communication network, which can be a wired or wireless network, or a combination of both, or otherwise accessible to the IEDs 121, 122, 123, 124. The computing resources comprising the cloud 150 can be centralized in a single location, distributed across multiple locations, or a combination of both. A cloud computing system can divide computing tasks among multiple racks, blades, processors, cores, controllers, nodes, or other computing units according to a particular cloud system architecture or programming. Similarly, a cloud computing system can store instructions and computing information in a centralized memory or storage device, or can distribute such information among multiple storage devices or memory components. A cloud system can store multiple copies of instructions and computing information in redundant storage units, such as a RAID array.

[0074] The central processing unit 140 can be an example of a cloud computing system or a computing system connected to the cloud. In embodiments, the central processing unit 140 can be a server located within a building in which the loads 111, 112, 113, 114, 115 and the IEDs 121, 122, 123, 124 are installed, or can be a remote cloud-based service. In some embodiments, the central processing unit 140 can include computing functional components similar to those of the IEDs 121, 122, 123, 124, but can typically be provided with a greater number and / or more powerful versions of components, such as processors, memories, storage devices, interconnection means, etc., involved in data processing. The central processing unit 140 can 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 involve the execution of one or more software functions, algorithms, instructions, applications, and parameters, which are stored on one or more memory sources communicatively coupled to the central processing unit 140. In certain embodiments, the term "function," "algorithm," "instruction," "application," or "parameter" can also refer to a hierarchy of functions, algorithms, instructions, applications, or parameters operating in parallel and / or in series, respectively. The hierarchy can include a tree-based hierarchy, such as a binary tree, a tree with one or more child nodes descending from each parent or node, or a combination thereof, where each node represents a particular function, algorithm, instruction, application, or parameter.

[0075] In embodiments, 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 useful in analyzing the measurement data received from the IEDs 121, 122, 123, 124. In embodiments, the cloud-connected devices or databases 160 can correspond to devices or databases associated with one or more external data sources. Further, in embodiments, the cloud-connected devices or databases 160 can correspond to user devices from which a user can provide user input data. The user can use the user devices to view information about the IEDs 121, 122, 123, 124 (e.g., IED configuration, model, type, etc.) and data collected by the IEDs 121, 122, 123, 124 (e.g., energy usage statistics). Further, in embodiments, the user can use the user devices to configure the IEDs 121, 122, 123, 124.

[0076] In embodiments, by virtue of 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 of the IEDs 121, 122, 123, 124 and on the additional data sources described above, when appropriate. This analysis can be used to dynamically control one or more parameters, processes, conditions, or devices (e.g., loads) associated with the electrical system.

[0077] In embodiments, the parameters, processes, conditions, or devices are dynamically controlled by a control system associated with the electrical system. In embodiments, the control system can correspond to or include one of the following: one or more of the IEDs 121, 122, 123, 124 in the electrical system, the central processing unit 140, and / or other devices within or external to the electrical system.

[0078] Referring to Figure 2 may be adapted to Figure 1 The example IED 200 shown for the electrical system, for example, includes a controller 210, a memory device 215, a storage 225, and an interface 230. The IED 200 also includes an input-output (I / O) port 235, a sensor 240, a communication module 245, and an interconnect member 220 for communicatively coupling the two or more IED components 210-245.

[0079] For example, memory device 215 may include volatile memory, such as DRAM or SRAM. Memory device 215 may store programs and data collected during the operation of IED 200. For example, in an embodiment, IED 200 is configured to monitor or measure one or more loads in an electrical system (e.g., Figure 1 The memory device 215 can store one or more electrical parameters associated with 111 shown in the figure, and the memory device 215 can store the monitored electrical parameters.

[0080] Storage device 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 controller 210 or information to be processed by a program are stored. Controller 210 may control data transfer between storage device system 225 and memory device 215 according to known computing and data transfer mechanisms. In embodiments, electrical parameters monitored or measured by IED 200 may be stored in storage device system 225.

[0081] I / O port 235 can be used to transfer loads (e.g., ... Figure 1 As shown in Figure 111), I / O port 235 is coupled to IED 200, and sensor 240 can be used to monitor or measure electrical parameters associated with a load. I / O port 235 can also be used to couple external devices, 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), to IED 200. External devices can be local or remote devices, such as gateways (or multiple gateways). I / O port 235 can be further coupled to one or more user input / output components, such as buttons, displays, acoustic devices, etc., to provide alarms (e.g., for displaying visual alarms, such as text and / or steady or flashing lights, or for providing audio alarms, such as beeps or long beeps) and / or to allow user interaction with IED 200.

[0082] Communication module 245 can be configured to couple IED 200 to one or more external communication networks or devices. These networks can be private networks within a building where IED 200 is installed, or public networks such as the Internet. In embodiments, communication module 245 can also be configured to couple IED 200 to a hub (e.g., [missing information]) of a connection cloud associated with an electrical system including IED 200. Figure 1 130 as shown) or a central processing unit connected to the cloud (e.g., Figure 1 (140 shown).

[0083] The IED controller 210 may include one or more processors configured to perform specified functions of the IED 200. The processors may be commercially available processors, such as the well-known Pentium processors offered by Intel. TM Core TM Or Atom TM A multi-level processor. Many other processors are also available, including programmable logic controllers. The IED controller 210 can execute an operating system to define the computing platform on which applications associated with the IED 200 can run.

[0084] In an embodiment, electrical parameters monitored or measured by IED 200 can be received as IED input data at the input of controller 210, and controller 210 can process the measured electrical parameters to generate IED output data or signals at its output. In an embodiment, the IED output data or signals can correspond to the output of IED 200. For example, IED output data or signals can be provided at I / O port 235. In an embodiment, the IED output data or signals can be received by a central processing unit connected to the cloud, for example, for further processing (e.g., to identify power quality events, as briefly discussed above), and / or can be received by a device (e.g., a load) to which the IED is coupled (e.g., for controlling one or more parameters associated with the device, as discussed further below). In one example, IED 200 may include an interface 230 for displaying a visualization indicating IED output data or signals. In an embodiment, interface 230 can correspond to a graphical user interface (GUI).

[0085] The components of IED 200 can be coupled together by interconnecting members 220, which may include one or more buses, wiring, or other electrical connection devices. Interconnecting members 220 enable communication (e.g., data, instructions, etc.) to be exchanged between system components of IED 200.

[0086] It should be understood that, according to various aspects of this disclosure, IED 200 is only one of many potential configurations of an IED. For example, an IED according to embodiments of this disclosure may include more (or fewer) components than IED 200. Additionally, in embodiments, one or more components of IED 200 may be combined. For example, in embodiments, memory 215 and storage device 225 may be combined.

[0087] Now back Figure 1 In order to accurately describe non-periodic events, such as those in electrical systems (such as...) Figure 3-4CIt is important to measure energy-related waveforms associated with events in order to identify and mitigate voltage sags (e.g., in an electrical system as shown in FIG. 1). Two attributes commonly used to characterize voltage sags and transients are the magnitude (deviation from normal) and the duration (length of time) of the event. Both parameters contribute to defining and, thus, identifying and mitigating these types of power quality issues.

[0088] The systems and methods disclosed herein classify measurements related to energy-related waveforms into a plurality of categories, such as: (1) voltage sags due to upstream electrical system disturbances, (2) voltage sags due to downstream electrical system faults (i.e., electrical system short circuits), (3) voltage sags due to downstream line transformer and / or motor magnetization, and (4) voltage sags due to other downstream line load disturbances that do not include fault or surge characteristics. More specifically, in some embodiments, the disclosed systems and methods classify each sample of a waveform into a plurality of categories and classify a complete waveform measurement into one or more of the plurality of categories. As noted above in the SUMMARY section of the disclosure, the energy-related waveforms can include at least one of: a voltage waveform; a current waveform; a power waveform; a derivative of a voltage, current, and / or power waveform; an integral of a voltage, current, and / or power waveform; and any (or substantially any) other energy-related waveform derived from a voltage and / or current signature.

[0089] For example, the disclosed systems and methods can be embedded (or otherwise implemented) in Schneider Electric’s ION meters and Schneider Electric’s Microprocessor Relays. This would allow the monitoring equipment to output signals or alarms related to the detection of faults, surges, or other downstream line load disturbances. This would complement other analytical features related to voltage sags, including Schneider Electric’s algorithms related to loss of electrical load due to voltage sags, which are described in U.S. Patent Application Serial No. 16 / 233,231, entitled “Systems and Methods for Managing Voltage Event Alarms in Electrical Systems,” (the application assigned to the same assignee as the present disclosure). It should be appreciated that the disclosed systems and methods can also be applicable to many other meters, microprocessor relays, and similar equipment.

[0090] The disclosed systems and methods can also be incorporated into edge software to classify / vary voltage sags into a plurality of categories, such as those mentioned above. These categories can result in new email notifications or alarms not available in current monitoring instruments or software. The measurements including energy-related waveforms can be automatically filtered or grouped according to voltage sag cause type.

[0091] The disclosed systems and methods can further be embedded in cloud-based software, such as Schneider Electric’s EcoStruxure Power Monitoring Expert, which is a cloud-based software for monitoring and analyzing electrical power systems. The disclosed systems and methods can be used to classify voltage sags into a plurality of categories, such as those mentioned above. These categories can result in new email notifications or alarms not available in current monitoring instruments or software. The measurements including energy-related waveforms can be automatically filtered or grouped according to voltage sag cause type. TMelectrical consultant). One example application is to have the electrical consultant track recurring transient faults and provide a warning when the same transient fault is detected more than once in a short period of time without a subsequent permanent fault.

[0092] According to embodiments of the present disclosure, the disclosed systems and methods operate on (at least partially) recorded energy-related waveforms by any of the aforementioned devices or instruments (e.g., electrical power meters or microprocessor relays). In some embodiments, the energy-related waveforms are triggered using a voltage sag detection algorithm, for example, which can already be available (or can become available) within the device or instrument.

[0093] In some embodiments, the measurements include at least one full cycle of pre-sag energy-related samples, which allows the systems and methods to establish initial conditions before a voltage sag occurs. The disclosed systems and methods can process instantaneous voltage and current amounts of the waveforms, but can also calculate rms amounts, fundamental phase amounts, and amounts for at least one harmonic and / or inter-harmonic frequency. These measured and derived amounts can be used together to classify each waveform sample of a voltage sag into one of a plurality of categories, such as: an upstream line voltage sag, a downstream line fault, a downstream line surge, or other downstream line disturbance. Since electrical system disturbances can evolve through these categories, the disclosed systems and methods can allow a complete measurement to have more than one category. For example, a voltage sag measurement can start as a transformer or motor surge, but can end as a fault event.

[0094] The output of the inventive algorithm will be the voltage sag category (and, in some cases, other categories) that infers the source of the disturbance, such as: an upstream line voltage sag, a downstream line fault, a downstream line surge, or other downstream line load disturbance. These categories can be used, for example, for meter notifications, edge software alerts, and voltage sag event list filtering or grouping, as well as for cloud software analysis of trends in voltage sag sources.

[0095] Reference is made to Figure 4 several flowcharts (or flow diagrams) are shown to illustrate various methods (here, methods 300, 400) of the present disclosure for automatically categorizing disturbances in electrical systems. Rectangular elements (represented by element 405 in Figure 4 may be referred to herein as "processing blocks"), can represent computer software and / or IED algorithmic instructions or groups of instructions. Diamond elements (represented by element 410 in Figures 4-4CElements 455 in the middle (as represented by representative element 455) such as can be referred to herein as "decision boxes," represent computer software and / or IED algorithmic instructions or groups of instructions that affect execution of computer software and / or IED algorithmic instructions represented by processing boxes. Processing boxes and decision boxes (as well as other boxes shown) can represent steps performed by functionally equivalent circuits such as digital signal processor circuits or application specific integrated circuits (ASICs).

[0096] The flowcharts do not depict any particular programming language's syntax or semantics. Rather, the flowcharts illustrate the functional information one of ordinary skill in the art requires to fabricate circuits or generate computer software to perform the processing required of the particular apparatus. It should be noted that many routine program elements such as initialization of loops and variables and the use of temporary variables are not shown. It will be apparent to those of ordinary skill in the art that the particular sequence of steps described is merely illustrative and that variations can be made in the sequence and in the steps themselves. Thus, unless otherwise stated, the order of the steps described below is not limiting; this means that, when possible, blocks can be executed in any convenient or desirable order, including simultaneously, that blocks that are in sequence can be executed at the same time, and vice versa. It will also be understood that, in some embodiments, features from various flowcharts described below can be combined. Thus, unless otherwise stated, features from one of the flowcharts described below can be combined with features of other flowcharts described below, e.g., to capture various advantages and aspects of systems and methods associated with automatically classifying disturbances in electrical systems sought to be protected by the present disclosure. It will also be understood that, in some embodiments, features from various flowcharts described below can be separated. For example, although Figure 3 The flowcharts shown illustrate as having many boxes, but in some embodiments, the illustrated methods shown by these flowcharts can include fewer boxes or steps.

[0097] Referring to Figure 1 , a flowchart illustrates an example method 300 for automatically classifying disturbances in electrical systems. Method 300 can be implemented, for example, on a processor of at least one IED (e.g., 121 shown in Figure 3 , implemented and / or implemented away from the at least one IED, e.g., in at least one of: a cloud-based system, on-site software, a gateway, and other front-end systems.

[0098] As Figures 4-4CAs shown, the method 300 begins at block 305, where energy-related waveforms are captured by at least one IED in an electrical system. The at least one IED can be installed or positioned, for example, at a respective one of a plurality of metering points in the electrical system. The energy-related waveforms can include at least one of: a voltage waveform; a current waveform; a power waveform; a derivative of a voltage, current, and / or power waveform; an integral of a voltage, current, and / or power waveform; and any (or substantially any) other energy-related waveform derived from a voltage and / or current signature. The voltage and / or current waveforms can be, for example, single-phase or three-phase voltage and current waveforms. These waveforms can be sampled at various rates, for example, at a rate of 100 Hz or higher.

[0099] At block 310, the electrical measurement data in or derived from the energy-related waveforms is processed to identify a disturbance (e.g., an electrical disturbance) in the electrical system. According to some embodiments of the present disclosure, the disturbance is identified based on one or more features of the energy-related waveforms satisfying at least one criterion indicative of a disturbance. For example, a disturbance can be identified as a result of a duration of a detected electrical event satisfying a disturbance classification criterion. The disturbance classification criterion can be established, for example, by IEEE Standard 1159-2019 or other standard or manner that can define a disturbance classification criterion.

[0100] At block 315, each sample of the energy-related waveforms associated with the disturbance identified from block 310 is analyzed and classified into one of a plurality of disturbance categories. The disturbance categories can include, for example, (a) a voltage sag due to an upstream line electrical system disturbance, (b) a voltage sag due to a downstream line electrical system fault, (c) a voltage sag due to a downstream line transformer and / or motor magnetization, and (d) a voltage sag due to other downstream line disturbances. Definitions of these example types of disturbance categories have been discussed earlier in the DETAILED DESCRIPTION section of the present disclosure. It should be understood that the definitions provided are exemplary definitions and can be more or less elaborated as understood by one of ordinary skill in the art. It should also be understood that these disturbance categories are only a few of many potential disturbance categories into which each sample of the energy-related waveforms can be classified, as understood by one of skill or ordinary skill in the art.

[0101] At block 320, a disturbance class is determined for the energy-related waveforms (i.e., the complete energy-related waveforms) associated with the identified disturbance based on the class of each sample of the energy-related waveforms at block 315. The disturbance class can be, for example, selected from one of the disturbance categories described above (and / or other disturbance categories). As noted above, a disturbance can evolve, and the systems and methods disclosed herein allow for a complete measurement to have more than one class. For example, as noted above, a voltage sag measurement can begin as a transformer and / or motor swell, but can end as a fault event.

[0102] At block 325, which is optional in some embodiments, one or more actions can be taken / performed based on the disturbance category of the energy-related waveform. For example, in one embodiment, the action can include triggering one or more alarms in response to certain types of disturbances in the electrical system. In some embodiments, one or more actions can be taken in response to triggering the alarms. For example, the disturbances can be reported to, e.g., a system user or operator. Additionally or alternatively, at least one component in the electrical system can be operated or controlled in response to the disturbances (e.g., to prevent or reduce damage to electrical system devices). As one example, at least one component (e.g., a load in the electrical system) can be operated or controlled to change a functional or operational state.

[0103] After block 325, in some embodiments, the method can end. In other embodiments, the method can return to block 305 and repeat again (e.g., for capturing additional energy-related waveforms, and identifying and categorizing additional disturbances in the electrical system).

[0104] It should be appreciated that, in some embodiments, the method 300 can include one or more additional blocks. For example, in embodiments where the action taken at block 325 includes triggering one or more alarms in response to certain types of disturbances in the electrical system, the method can further include prioritizing the alarms based on the importance / criticality of the electrical node or location from which the disturbance originated. Determination of the electrical node or location from which the disturbance originated can be based, e.g., at least in part, on the location of the IED used to capture the energy-related waveform associated with the disturbance. For example, the IED location information (i.e., where within the facility or structure) can be obtained or extracted from metadata associated with the energy-related waveform captured at block 305. According to some embodiments of the present disclosure, the metadata can also include IED hierarchy information (e.g., how the IEDs are related to each other within the monitoring system, etc.) as well as other data indicative of the contextual environment in which the energy-related waveform was captured.

[0105] Additional aspects and advantages of the method 300 and other aspects of the application disclosed herein can be had from the following detailed description when viewed in connection with the drawings, in which: Figures 4-4CThe discussion of method 400 is further understood. According to some embodiments of this disclosure, method 400 corresponds to an example implementation of method 300. For example, according to some embodiments of this disclosure, one or more of blocks 420-615 of method 400 may correspond to an example step performed at block 315 of method 300. Additionally, one or more of blocks 620-680 of method 400 may correspond to an example step performed at block 320 of method 300. For example, as described above, at block 320 of method 300, a disturbance category is determined for the energy-related waveform (i.e., the complete energy-related waveform), wherein the disturbance category is selected from a plurality of possible disturbance categories. As described above, the disturbance category may include, for example, (a) a voltage dip due to an upline electrical system disturbance, (b) a voltage dip due to a downline electrical system fault, (c) a voltage dip due to downline transformer and / or motor magnetization, and (d) a voltage dip due to other downline disturbances. According to some embodiments of this disclosure, if the determination made at block 680 of method 400 is true, the disturbance category of the energy-related waveform can be generally determined to be a voltage dip (or dips) caused by an uplink electrical system disturbance (or multiple disturbances). Additionally, according to some embodiments of this disclosure, if the determination made at any of blocks 620, 630, 640, and 650 of method 400 is true, the disturbance category of the energy-related waveform can be generally determined to be a voltage dip (or dips) caused by a downlink electrical system fault (or multiple faults). Furthermore, according to some embodiments of this disclosure, if the determination made at block 660 of method 400 is true, the disturbance category of the energy-related waveform can be generally determined to be a voltage dip (or dips) caused by downlink transformer and / or motor magnetization. Additionally, according to some embodiments of this disclosure, if the determination made at block 670 of method 400 is true, the disturbance category of the energy-related waveform can be generally determined to be a voltage dip (or multiple dips) caused by other downline disturbances.

[0106] Additional aspects of the above and other embodiments can be further understood from the following discussion.

[0107] Now refer to Figure 1 Several flowcharts illustrate example method 400 for automatically classifying disturbances in an electrical system. Similar to method 300, method 400 can, for example, in at least one IED (e.g., Figure 4 The implementation is carried out on and / or away from at least one IED implementation on the processor of 121 shown, for example, in at least one of the following: cloud-based systems, field software, gateways and other front-end systems.

[0108] Before further discussing the method 400, for convenience, certain abbreviations used in the description of this method and shown in the corresponding figures are collected here.

[0109] -“RMS” refers to root mean square;

[0110] -“CbC” refers to cycle-by-cycle;

[0111] -“SLG” refers to single line to ground;

[0112] -“LL” refers to line-to-line;

[0113] -“LLG” refers to line-to-line-to-ground; and

[0114] - refers to three-phase.

[0115] As shown in FIG. 4, the method 400 begins at block 405 where an electrical disturbance occurs in an electrical system (also sometimes referred to herein as an electrical system). Figure 4C At block 410, one or more IEDs record energy measurements in the electrical system. The energy measurements include energy-related waveforms (as shown in block 415). For this reason, the energy-related measurements are also sometimes referred to as energy-related waveform measurements, energy-related waveforms, or simply waveform measurements. The energy-related waveform measurements / energy-related waveforms / multiple waveforms can, for example, include voltage and / or current measurements (as shown in block 415). The energy-related waveform measurements / energy-related waveforms / multiple waveforms can additionally or alternatively include power waveforms, derivatives of voltage, current, and / or power waveforms, integrals of voltage, current, and / or power waveforms, etc.

[0116] In some embodiments, at block 410, the one or more IEDs use an internal algorithm designed to detect voltage sags and / or another type of disturbance using an algorithm that compares waveform points or root mean square (rms) values. The triggering algorithm will cause energy-related waveforms to be recorded, typically for a duration between one cycle and 120 cycles. One cycle is defined as the time period of one rise and fall of a sinusoidal voltage waveform supplied to an electrical node. As is well known, power systems in North America oscillate at 60 times per second, or 60 Hz.

[0117]

[0118] ​At block 420, the software module in conjunction with the present application will automatically process the waveform measurements. The processing can, for example, run on at least one of the IEDs, on the edge server software, or on a cloud server. In some embodiments, the measurements can be downloaded from the remote IEDs to a local server and / or uploaded to a cloud server before being processed.

[0119] At block 425, the waveforms from one or more measurements can be combined in chronological order by the software module in conjunction with the present application by preserving the time difference between measurements. For example, temporal clustering can be used to group measurements that occur close in time together. The grouping logic can be based on the start time of the measurements being close in time (e.g., less than a one second difference) or the end of one measurement being close in time to the start of the next measurement (e.g., less than a one millisecond difference). The measurements that are temporally clustered will be referred to as a "temporally clustered event".

[0120] At block 430, if different channels of the waveform measurements are sampled at different rates, the software module in conjunction with the present application will downsample to the slowest sampling rate. Optionally, the algorithm allows for waveform measurements to be recorded at a relatively fast rate (e.g., 2048 samples per cycle) to a slower rate (e.g., 64 points per cycle) to reduce computation time.

[0121] At block 435, if the monitoring instrument is accompanied by an estimate of the voltage source impedance (as indicated by block 440), certain channels missing from the measurements can be estimated by the software module.

[0122] At block 445, the software module in conjunction with the present application will use the Fourier transform calculated over the first cycle of the measurements to derive the voltage and current phase.

[0123] At block 450, the software module in conjunction with the present application will compare the phase voltage derived for the first cycle of the measurements to a user-defined nominal voltage (as indicated by block 455). The nominal voltage can also be automatically derived using an algorithm that estimates a "floating" nominal voltage based on slow changes in the voltage (that is, changes in the phase voltage magnitude over a period of ten minutes). If the software module determines that the derived nominal voltage on one of the phase voltages is between approximately 90% and approximately 110% of the nominal voltage, then the cycle will be labeled "nominal". If the first cycle of a temporally clustered event is determined to be a voltage sag or a voltage swell, the software module in conjunction with the algorithm will stop processing the measurements (as indicated by block 460). In some embodiments, the 90% and 110% nominal voltage threshold can be overwritten by a threshold provided by the user. Further, in some embodiments, the nominal voltage threshold can be automatically determined by the software module.

[0124] At block 465, the software module in conjunction with the present application compares the phase current derived for the first cycle of the measurement to a user-defined minimum level of load current, such as 50 amps (as indicated by block 470). If the phase current of any phase of the first measurement is below the minimum level of load current, the software module in conjunction with the algorithm will stop processing the measurement (as indicated by block 475).

[0125] At block 480, the first cycle of the current waveform of each phase is subtracted (i.e., removed) from each successive cycle by matching the sample points sampled N cycles after the first cycle. N is calculated by using a cycle counter that is initialized to zero at the first cycle of the first waveform in time order and incremented with each cycle as each cycle of each phase of the temporally aggregated measurement is processed.

[0126] At block 485, the second harmonic current is calculated for each phase using a single cycle window Fourier transform, sliding through the waveform samples of the temporally aggregated event cycle by cycle. The single cycle derivation window can slide one cycle at a time or in time increments as short as the sampling rate of the waveform samples themselves. The maximum second harmonic current magnitude across all phases is recorded.

[0127] At block 490, the voltage and current are processed using a single cycle window, sliding through the waveform samples of the temporally aggregated event cycle by cycle. The single cycle derivation window can slide one cycle at a time or in time increments as short as the sampling rate of the waveform samples themselves. Example calculations include: time axis symmetry ratio of the current waveform, fundamental frequency voltage phase, fundamental frequency current phase, current total harmonic distortion (THD), positive sequence symmetrical component, and second harmonic current (as indicated by blocks 495, 500, 505, and 510).

[0128] At blocks 515, 520, 525, and 530, the software in conjunction with the present application determines a true / false flag for each analyzed single-cycle window as to whether all of the following criteria are met: current waveform asymmetry, voltage phase magnitude between about 80% of nominal voltage and about 110% of nominal voltage, positive sequence voltage between about 80% of nominal voltage and about 120% of nominal voltage, and second harmonic current greater than a user-defined percentage of the maximum second harmonic current value of the phase of the temporal aggregation waveform event (e.g., between about 20% and about 35%). For voltage phase magnitude between about 80% of nominal voltage and about 110% of nominal voltage, fundamental voltage outside of this range would indicate that we have a fault event rather than a swell. Also, for positive sequence voltage between about 80% of nominal voltage and about 120% of nominal voltage, positive sequence voltage outside of this range would indicate that we have a fault event rather than a swell. Also, for second harmonic current greater than a user-defined percentage of the maximum second harmonic current value of the phase of the temporal aggregation waveform event, as the swell current signal decays exponentially, there will be some residual second harmonic current measured. Once the second harmonic current falls below the user-defined percentage (e.g., between about 20% and about 35%, as described above) of the maximum seen, it is difficult to use the second harmonic as an indicator of the presence of a swell. Thus, once below, for example, 35% of the peak, the flag is disabled. It should be understood that any of the "user-defined thresholds" (or cutoff thresholds) described above and below can be automatic thresholds in accordance with embodiments of the present disclosure.

[0129] At block 535, the real power is calculated by averaging the product of voltage and current over one cycle. That is, P = 1 / T Integral[t=0 to T] (p(t) • dt. This equation is known. Additionally, at block 540, the change in load is calculated by subtracting the real power measured at the end of the temporal aggregation event from the real power measured at the beginning. If the normalized difference in real power is determined to be positive and greater than a user-defined threshold (e.g., 5%), then a load increase flag will be set at block 545.

[0130] At block 550, the voltage and current symmetrical components are calculated on a cycle-by-cycle basis. In particular, in one embodiment, the symmetrical component transform 4 The Fourier transform of the coupled single-cycle windows, sliding through the waveform samples of the temporal aggregation event, calculates the voltage and current symmetrical components for the fundamental frequency. The single-cycle derived windows can be slid one cycle at a time or in time increments as short as the sampling rate of the waveform samples themselves.

[0131] At blocks 555 and 560, the voltage and current symmetrical components are analyzed cycle by cycle. In one embodiment, if the positive sequence voltage is less than about 90% to about 95% of the first cycle voltage (as indicated by block 555, where it is determined whether the positive sequence voltage is less than about 95% of the first cycle voltage), then the current symmetrical components are analyzed to determine whether the current cycle displays a single line to ground (SLG) fault (as indicated by block 570), whether the current cycle displays a line to line to ground (LLG) fault (as indicated by block 590), whether the current cycle displays a line to line (LL) fault (as indicated by block 600), or whether the current cycle displays a three phase fault (as indicated by block 610). This analysis will result in a cycle by cycle (CbC) fault flag (as indicated by blocks 575, 595, 605, 615) with true / false values. In addition, if the current cycle does not display an SLG fault (e.g., at blocks 570, 580), then a CbC swell flag is generated at block 585. If the positive sequence voltage is less than about 95% of the first cycle voltage, then a CbC SLG voltage dip flag can be generated (e.g., at block 565). According to some embodiments of the disclosure, the ratio between the current increase and the voltage decrease can be used to develop a confidence factor as to the fault condition (i.e., the disturbance category). For example, an increase in current without a corresponding decrease in voltage would indicate that there is no fault. According to some embodiments of the disclosure, the confidence factor is a measure of confidence in correctly identifying an electrical disturbance (e.g., a fault condition).

[0132] In one example implementation, the confidence factor is determined by weighting the output of the characteristics that match the conditions required for a positive swell characterization (i.e., the conditions checked at blocks 515, 520, 525, and 530) such that certain conditions have a greater impact on the cycle by cycle swell check calculated at block 585. In this example implementation, for example, the true / false flag at block 585 can be converted to a percentage score ranging from 0 to 100. For example, as discussed further below, the confidence factor can also be determined at block 660, where the requirement for a specified number of consecutive cycle by cycle values displaying a positive swell characteristic of 100% can be modified to a certain percentage of consecutive cycles.

[0133] ​In some embodiments, in response to a confidence factor representing a disturbance satisfying a threshold (e.g., a user-defined threshold), a disturbance category (or identification of an electrical disturbance) can be determined as defined in the boxes above and below. According to some embodiments of this disclosure, different elements or portions of an electrical system may have different confidence factor threshold levels, such as based on the element's functional type. Additionally or alternatively, a global average confidence threshold may also be used. In some embodiments, if a confidence factor does not satisfy a threshold, the system or systems implementing the method thereon may indicate that no electrical disturbance has occurred, or determine that further information is needed to characterize the disturbance. If further information is determined to be needed, additional information (e.g., energy-related waveforms, images, videos, or other sensor information or requests for verification of information extracted from images or videos) can be captured and analyzed.

[0134] Now, returning to a more detailed discussion of perturbation characterization, following the box above, the software incorporating this algorithm will characterize SLG faults, LLG faults, faults, LL faults, and... All fault periodic flags are analyzed together, which is consistent for each phase. If temporal aggregated events have one or more periodic flags set to true... If a fault flag (as defined in box 620) is used, the temporal clustering event is marked as a three-phase fault (as indicated in box 625). In one example implementation, several further characteristics of the temporal clustering event can be analyzed before marking it as a three-phase fault in box 625 to distinguish it from three-phase faults and three-phase load startups that may appear nearly identical in some instances. For example, if the last fault in the temporal clustering event is a three-phase fault, it can be further determined whether: (a) the last cycle of the last fault measured was a three-phase fault, (b) the voltage is below the user-specified voltage (e.g., 1000 volts) (or if the load is identified as primarily a three-phase load), and (c) there are no other faults associated with the event (e.g., in...). Figure 7 As determined in the subsequent box shown below (as further described below). According to some embodiments of this disclosure, in response to (a), (b) and (c) being true, it can be determined that the final three-phase fault is actually a load initiation event, and the temporal aggregation event can be labeled as a load initiation event.

[0135] Otherwise, if the temporal aggregation event has one or more per-cycle LLG fault flags set to true (as determined at block 630), the temporal aggregation event is flagged as an LLG fault (as indicated by block 635). Otherwise, if the temporal aggregation event has one or more per-cycle LL fault flags set to true (as determined at block 640), the temporal aggregation event is flagged as an LL fault (as indicated by block 645). Otherwise, if the temporal aggregation event has one or more per-cycle SLG fault flags set to true (as determined at block 650), the temporal aggregation event is flagged as an SLG fault (as indicated by block 655). Three-phase fault, LLG fault, LL fault, and SLG fault are example types of events that can be due to a voltage sag (or voltage sags) caused by a downline electrical system fault, as defined above. For example, as discussed further below, Figure 8 and Figure 5 An example voltage sag due to a downline electrical system fault is shown.

[0136] If none of the above flags (i.e., three-phase fault, LLG fault, LL fault, and SLG fault) are true, the method proceeds to block 660. At block 660, if the temporal aggregation event has a per-cycle swell flag set to true, and the swell flag is true for at least four to six cycles or another user-defined number of cycles (as determined at block 660), the temporal aggregation event is flagged as a swell event (as indicated by block 665). A swell event is one example type of event that can be due to a voltage sag (or voltage sags) caused by downline transformer and / or motor magnetization, as defined above. For example, as discussed further below, Figure 6 and Figure 10 An example voltage sag due to downline transformer and / or motor magnetization is shown.

[0137] If it is determined that the temporal aggregation event does not have a per-cycle swell flag set to true, and the swell flag is not true for at least four to six cycles or another user-defined number of cycles (i.e., the determination made at block 660 is false), the method proceeds to block 670. At block 670, if by comparing the last cycle of the temporal aggregation event to the first cycle of the temporal aggregation event, if the event shows an increase in real power greater than a user-defined amount of kilowatts (as determined at block 670), the temporal aggregation event is flagged as a load start event (as indicated by block 675). A load start event is one example type of event that can be due to a voltage sag (or voltage sags) caused by other downline disturbances, as defined above. For example, as discussed further below, Figure 11 and Figure 9 An example voltage sag due to other downline disturbances is shown.

[0138] If it is determined that the event does not show an actual increase in power greater than a user-defined amount (i.e., the determination made at block 670 is false), the method proceeds to block 680. At block 680, the temporal aggregation event is labeled as a voltage sag due to an upstream electrical system disturbance, or simply as an upstream voltage sag (as indicated by block 680). For example, as discussed further below, Figures 5-11 An example voltage sag due to an upstream electrical system disturbance is shown.

[0139] After block 680, in some embodiments, the method can end. In other embodiments, the method can go back to block 405 (or another block) and repeat again (e.g., for capturing additional energy-related waveforms, and identifying and classifying additional disturbances in the electrical system).

[0140] It will be appreciated that, in some embodiments, the method 400 can include one or more additional blocks, e.g., similar to those described above in connection with the method 300.

[0141] Referring to Figure 5 , several example waveforms (and samples) are shown. For example, Figure 6 Example voltage and current waveforms recorded during a transformer surge are shown. In addition, Figure 6 Example current waveforms recorded during a motor surge are shown. As shown, Figure 5 The motor surge current shown exhibits similar asymmetric characteristics as Figure 7 the transformer surge shown.

[0142] Figure 8 Example voltage and current waveforms recorded during a single-phase fault event are shown. In addition, Figure 9 Example voltage and current waveforms recorded during a transformer inrush followed by a single-phase fault are shown. Figure 10 Example voltage and current waveforms recorded during an upstream voltage sag are shown. In addition, Figure 11 Example voltage and current waveforms recorded during a load start are shown. Figures 5-11 Example voltage and current root mean square (rms) samples recorded during a load start are shown.

[0143] According to some embodiments of the present disclosure, ​ The waveforms (and samples) shown in

[0144] As described above and as will be appreciated by those of ordinary skill in the art, the embodiments disclosed herein can be configured as a system, a method, or a combination thereof. Accordingly, embodiments of the present disclosure can include various means, including hardware, software, firmware, or any combination thereof.

[0145] It should be appreciated that the concepts, systems, circuits, and techniques sought to be protected herein are not limited to use in the example applications described herein (e.g., the power monitoring system applications), but can be useful in substantially any application in which it is desirable to categorize disturbances in an electrical system. While particular embodiments and applications of the present disclosure have been illustrated and described, it is to be understood that the embodiments of the present disclosure are not limited to the precise construction and compositions disclosed herein and that various modifications, changes and variations can be apparent from the foregoing description to those skilled in the art without departing from the spirit and scope of the present disclosure as defined in the appended claims.

[0146] Having described preferred embodiments for explaining various concepts, structures, and techniques of the present patent subject matter, it will now become apparent to one of ordinary skill in the art that other embodiments can be used in conjunction with the concepts, structures, and techniques described herein. In addition, elements of different embodiments described herein can be combined to form further embodiments not specifically set forth above.

[0147] Accordingly, it is therefore claimed that the scope of the present patent should not be limited to the described embodiments but should only be limited by the spirit and scope of the appended claims.

Claims

1. A method for automatically classifying disturbances in an electrical system, comprising: At least one energy-related waveform is captured using at least one intelligent electronic device (IED) in the electrical system; Process electrical measurement data from or derived from the at least one energy-related waveform to identify disturbances in the electrical system; In response to the identification of a disturbance in the electrical system, each sample of at least one energy-related waveform associated with the identified disturbance is analyzed and classified into one of a plurality of disturbance categories, the disturbance categories including: (a) voltage sag due to upline electrical disturbances, (b) voltage sag due to downline electrical system faults, (c) voltage sag due to downline transformer and / or motor magnetization, and (d) voltage sag due to other downline disturbances; Based on the type of each sample of the at least one energy-related waveform, determine the perturbation category of at least one energy-related waveform associated with the identified perturbation, the perturbation category being selected from one of the perturbation categories; as well as Based on the disturbance category of the energy-related waveform, at least one action is taken.

2. The method according to claim 1, wherein, The at least one energy-related waveform includes at least one of the following: a voltage waveform, a current waveform, and other waveforms and / or data derived from the voltage waveform and / or the current waveform.

3. The method according to claim 2, wherein, The voltage waveform and the current waveform are at least one of the following: single-phase and three-phase voltage and current waveforms.

4. The method according to claim 1, wherein, Processing electrical measurement data in or derived from the at least one energy-related waveform to identify disturbances in the electrical system includes: determining voltage and current phase information of the electrical measurement data associated with the disturbance, and analyzing the voltage and current phase information to determine whether the source of the disturbance is electrically upstream or electrically downstream of an electrical node or location electrically coupled to the at least one IED in the electrical system.

5. The method according to claim 1, wherein, Processing electrical measurement data in or derived from the at least one energy-related waveform to identify disturbances in the electrical system includes: grouping the electrical measurement data based on electrical nodes and locations in the electrical system associated with the at least one energy-related waveform, and processing the grouped electrical measurement data to identify disturbances at the electrical nodes or locations.

6. The method according to claim 5, wherein, The electrical measurement data is grouped using temporal aggregation techniques, such that measurements that occur close in time are grouped together.

7. The method according to claim 6, further comprising: Determine at least one of the following: when capturing at least one energy-related waveform associated with the electrical measurement data, adjust the sampling rate of the electrical measurement data to a desired sampling rate, calibrate the electrical measurement data, and group the electrical measurement data.

8. The method according to claim 7, wherein, The sampling rate of the electrical measurement data is adjusted by upsampling, downsampling, and / or resampling the electrical measurement data.

9. The method according to claim 7, wherein, The desired sampling rate is the lowest sampling rate at which the sampling rate is used to capture at least one energy-related waveform associated with the electrical measurement data.

10. The method according to claim 1, wherein, Determining the perturbation category of the at least one energy-related waveform includes: analyzing the category of each sample of the at least one energy-related waveform to formulate a confidence factor for the perturbation category of the at least one energy-related waveform, and determining the perturbation category of the at least one energy-related waveform in response to the confidence factor of the perturbation category satisfying a threshold.

11. The method according to claim 10, wherein, The analysis of each sample of the energy-related waveform includes identifying the type and pattern of the energy-related waveform samples.

12. The method according to claim 1, wherein, Taking one or more actions based on the disturbance category includes triggering one or more alarms based on the disturbance category.

13. The method according to claim 12, wherein, The alarms are prioritized based on the importance / criticality of the electrical node or location from which the disturbance originates.

14. The method according to claim 12, wherein, The alarms are prioritized based on the magnitude of the load measured at the electrical node or location from which the disturbance originates.

15. The method of claim 12, further comprising: In response to triggering the alarm, the disturbance is reported and / or at least one component in the electrical system is operated as a response to the disturbance to prevent or reduce damage to the electrical system apparatus.

16. The method according to claim 1, wherein, The one or more actions are automatically performed by a control system associated with the electrical system, wherein the control system is communicatively coupled to the at least one IED and / or to a cloud-based system, field / edge software, gateway, and other front-end systems associated with the electrical system.

17. The method according to claim 16, wherein, The electrical measurement data captured by or derived from the at least one energy-related waveform by the at least one IED is processed on at least one of the following: the cloud-based system, the field software, the gateway, and the other front-end systems associated with the electrical system, wherein the at least one IED is communicatively coupled to at least one of the following: the cloud-based system, the field software, the gateway, and the other front-end systems on which the electrical measurement data is processed.

18. A system for automatically classifying disturbances in an electrical system, comprising: processor; A memory device, coupled to the processor, wherein the processor and the memory device are configured as follows: Process electrical measurement data from or derived from at least one energy-related waveform captured by at least one intelligent electronic device (IED) in the electrical system to identify disturbances in the electrical system; In response to the identification of a disturbance in the electrical system, each sample of at least one energy-related waveform associated with the identified disturbance is analyzed and classified into one of a plurality of disturbance categories, the disturbance categories including: (a) voltage sag due to upline electrical disturbances, (b) voltage sag due to downline electrical system faults, (c) voltage sag due to downline transformer and / or motor magnetization, and (d) voltage sag due to other downline disturbances; Based on the type of each sample of the at least one energy-related waveform, determine the perturbation category of at least one energy-related waveform associated with the identified perturbation, the perturbation category being selected from one of the perturbation categories; as well as Based on the disturbance category of the energy-related waveform, at least one action is taken.

19. The system according to claim 18, wherein, The disturbance is identified by: grouping the electrical measurement data based on the electrical nodes and locations associated with the at least one energy-related waveform in the electrical system, and processing the grouped electrical measurement data to identify the disturbance at the electrical nodes and locations.

20. The system according to claim 18, wherein, The disturbance category of the at least one energy-related waveform is determined by: analyzing the category of each sample of the at least one energy-related waveform to formulate a confidence factor for the disturbance category of the at least one energy-related waveform, and determining the disturbance category of the at least one energy-related waveform in response to the confidence factor of the disturbance category satisfying a threshold.

21. The system according to claim 18, wherein, The one or more actions include: triggering one or more alarms based on the disturbance category.

22. The system according to claim 21, wherein, In response to triggering the alarm, the disturbance is reported and / or at least one component in the electrical system is operated as a response to the disturbance to prevent or reduce damage to the electrical system apparatus.

23. The system according to claim 18, wherein, The one or more actions include: generating an output signal according to the disturbance category, and providing the output signal to at least one device for further processing.

24. The system according to claim 23, wherein, The at least one device includes at least one of the following: the at least one IED, a control system associated with the electrical system, a cloud-based system, field / edge software, a gateway, and other front-end systems associated with the electrical system.

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