AI algorithm voting system for complex electrical events and issues

Abstract

The invention relates to an AI algorithm voting system for complex electrical events and issues. Fault or disturbance location detection in an electrical system. Energy-related data in the electrical system is captured using at least one intelligent electronic device (IED), and the energy-related data is analyzed to identify a fault or disturbance event in the electrical system. The outputs of the plurality of fault or disturbance location detection algorithms include independently confirmed locations of faults or disturbances, and weighted votes are applied to each of the outputs. The output is compiled to determine and provide an indication of the location. Compiling the plurality of outputs enhances certainty and indicates a higher confidence of the determined location.

Description

Technical Field

[0001] The present disclosure relates generally to electrical / power systems and, more particularly, to systems and methods for monitoring and evaluating energy-related data in electrical systems. Background Art

[0002] Developed and introduced in the early 2000s, the Disturbance Direction Detection (DDD) feature can determine whether a voltage disturbance is occurring upstream or downstream of an intelligent electronic device (IED) / meter. While this feature remains an important market differentiator, improvements are expected.

[0003] For example, analyzing waveform captures derived from waveform capture data is an effective method for troubleshooting potentially harmful electrical events (e.g., unexpected voltage sags or swells on an electrical system). However, diagnosing electrical events from waveform captures typically requires a human operator, which can be difficult or even impossible without years of experience, knowledge of IED configurations, and / or background in electrical systems. IEDs (such as power metering devices) are typically configured to monitor and record a time series of data samples and may be equipped with waveform capture technology to generate waveform captures in response to electrical events. However, each waveform capture must be manually analyzed by an experienced professional to interpret and / or diagnose various aspects associated with the electrical event. Additionally, manual review of the data (e.g., even by an expert) can be a slow, inefficient, and (sometimes) expensive means of troubleshooting electrical events.

[0004] Commonly assigned US Patent No. 7,138,924, the entire disclosure of which is incorporated herein by reference, discloses a method and system for disturbance direction detection in a power monitoring system. Summary of the Invention

[0005] Aspects of the present disclosure provide a more consistent, thorough, and accurate method to help electrical customers locate the source / origin of disturbances and / or faults within or external to their electrical systems. In particular, aspects herein bring important, unique, and long-awaited improvements to DDD for mid-range / high-end meters, software, cloud-based applications, and protection devices.

[0006] In one aspect, a method for improving fault or disturbance location detection includes capturing energy-related data in an electrical system using at least one IED, analyzing the energy-related data to identify fault or disturbance events in the electrical system, and, in response to identifying the at least one fault or disturbance event, selecting and applying a plurality of fault or disturbance location detection algorithms to the energy-related data to independently confirm the location of the at least one fault or disturbance. Furthermore, the method includes compiling outputs of the plurality of fault or disturbance location detection algorithms. The outputs include the independently confirmed locations of the at least one fault or disturbance, and applying a weighted vote to each of the outputs. The method also includes determining and providing an indication of the location of the at least one fault or disturbance based on the analysis of the compiled outputs.

[0007] In another aspect, a method for detecting the location of a disturbance event in an electrical system includes acquiring, by at least one IED of the electrical system, energy-related signals associated with the electrical system and processing the energy-related data to identify the occurrence of at least one disturbance event in the electrical system. In response to identifying the occurrence of the at least one disturbance event, the method applies each of a plurality of disturbance event location detection algorithms to the energy-related data. Each of the applied disturbance event location detection algorithms generates an output representing an independently identified candidate location of the at least one disturbance event relative to the IED. The method further includes combining the outputs of the disturbance event location detection algorithms to determine, based on an analysis of the combined outputs, a location or origin of the at least one disturbance event from the candidate locations.

[0008] In yet another embodiment, a system for detecting the location of a disturbance event in an electrical system includes at least one IED communicatively coupled to the electrical system and configured to acquire energy-related signals associated with the electrical system. The system also includes a processor that receives and responds to the energy-related signals acquired by the at least one IED, and a memory storing processor-executable instructions. When executed, the instructions configure the processor to process energy-related data to identify the occurrence of at least one disturbance event in the electrical system, and in response to identifying the occurrence of the at least one disturbance event, apply each of a plurality of disturbance event location detection algorithms to the energy-related data. Each of the applied disturbance event location detection algorithms generates an output representing an independently confirmed candidate location of the at least one disturbance event relative to the IED. When executed, the instructions further configure the processor to combine the outputs of the disturbance event location detection algorithms to determine the location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs.

[0009] Other objects and features of the invention will be in part apparent and in part pointed out herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 An exemplary electrical system according to an embodiment of the present disclosure is illustrated.

[0011] Figure 2 Another exemplary electrical system according to an embodiment of the present disclosure is illustrated.

[0012] Figure 3 is a flow chart illustrating an exemplary process for analyzing waveform capture data according to an embodiment of the present disclosure.

[0013] Figure 4A Illustrated are example transient waveform captures of three-phase voltages and currents during a phase-to-phase voltage sag event according to an embodiment of the present disclosure.

[0014] Figure 4B The pre-processing according to the embodiment of the present disclosure is illustrated. Figure 4A Example of a transient waveform capture of .

[0015] Figure 4C The embodiment according to the present disclosure is shown Figure 4B An example of the corresponding root mean square (rms) value of each phase captured by the instantaneous waveform.

[0016] Figure 4D The embodiment according to the present disclosure is shown Figure 4B Example of derived three-phase and total active power values ​​during a phase-to-phase voltage sag event.

[0017] Figure 5A Illustrated are example transient waveform captures of three-phase voltage and current during a single-phase voltage sag event according to an embodiment of the present disclosure.

[0018] Figure 5B The embodiment according to the present disclosure is shown Figure 5A An example of the corresponding root mean square (rms) value of each phase captured by the instantaneous waveform.

[0019] Figure 5C The embodiment according to the present disclosure is shown Figure 5A Example of derived three-phase and total active power values ​​during a single-phase voltage sag event.

[0020] Figure 6A and Figure 6B Illustrated are two exemplary digital waveform captures taken from the same analog event signal at two different sampling rates in accordance with an embodiment of the present disclosure.

[0021] Figure 7 An exemplary electrical power management system (EPMS) that identifies the location of a fault condition within a system according to an embodiment of the present disclosure is illustrated.

[0022] Corresponding reference numerals indicate corresponding parts throughout the drawings. DETAILED DESCRIPTION

[0023] The features and other details of the concepts, systems, and techniques for which protection is sought herein will now be described in more detail. It will be understood that any specific embodiments described herein are shown by way of illustration and not as limitations of the disclosure and concepts described herein. Features of the subject matter described herein may be employed in various embodiments without departing from the scope of the concepts for which protection is sought.

[0024] Aspects of the present disclosure include multiple algorithms to independently determine the location of a disturbance event (e.g., a fault or disturbance), such as upstream or downstream of an IED or within a system (when two or more IEDs are used). Once the fault / disturbance data has been processed independently using each algorithm, the outputs are combined (mathematically, statistically, or through some other analytical method) and used to provide the most accurate or valid conclusion.

[0025] For example, the outputs may be weighted according to a particular application, market segment, facility, and / or risk. For example, a weighted vote may be used to determine a final result (ie, an indication of the location of at least one fault or disturbance based on the analysis).

[0026] Empirical data is used to determine that each result may vary for various reasons; however, a comprehensive approach using a combination of algorithms provides customers with a much better level of confidence.

[0027] For example, a voltage sag is a short-duration decrease in the root mean square (rms) voltage, typically caused by a disturbance event such as a short circuit or fault, the energization of a transformer, or the starting of an electric motor. Voltage sags are often associated with voltage swells, especially when the cause is a ground fault. Of the seven power quality categories defined by the Institute of Electrical and Electronics Engineers (IEEE) Standard 1159-2019, short-duration rms variations are the most disruptive and have the greatest general economic impact on energy consumers. A study by the Electric Power Research Institute (EPRI) estimates that electricity customers experience an average of 56 voltage sags per year. A 2008 study concluded that the industrial sector's electrical power facilities are not reliable and resilient enough for current and future operational needs, resulting in losses exceeding €150 billion. This would be equivalent to €180 billion today. As industry becomes increasingly dependent on equipment that is sensitive to voltage sags, the impact of these events becomes more significant.

[0028] Analyzing waveform captures derived from waveform capture data is an effective method for troubleshooting potentially harmful electrical events (e.g., unexpected voltage sags or swells on an electrical system). A human operator is typically required to diagnose electrical events from waveform captures, which can be difficult or even impossible without years of experience. As discussed above, intelligent electronic devices (IEDs), such as power metering devices, are typically configured to monitor and record a time series of data samples and may be equipped with waveform capture technology to generate waveform captures in response to electrical events. However, each waveform capture must be manually analyzed by an experienced professional to diagnose the electrical event. Manual operator review of the data is a slow and inefficient means of troubleshooting electrical events.

[0029] Voltage sags are typically characterized by the minimum RMS voltage detected during the event and the duration that the RMS voltage remains below the voltage sag threshold. Similarly, voltage swells are characterized by their maximum RMS voltage. The impact of voltage sags on end-use equipment is typically estimated by assessing where the voltage sag will fall within the tolerance curve defined by the Information Technology Industry Council (ITIC) or SEMI F-47 voltage sag immunity standards. Tolerance curves are useful tools for summary reporting, but they are general recommendations for specific applications at explicit voltage levels. They do not accurately predict how a specific system or piece of equipment will respond to a voltage sag event, what impact the event will have on the electrical system, or how and where to economically mitigate the problem.

[0030] Reference Figure 1 An example electrical system 100 according to an embodiment of the present disclosure includes one or more intelligent electronic devices (IEDs) 102 capable of sampling, sensing, or monitoring one or more parameters (e.g., power monitoring parameters) associated with one or more loads 106 (sometimes also referred to herein as "devices" or "apparatus"). Although indicated by the same reference numerals, it is understood that, depending on the specific design and characteristics of the electrical system 100, the IEDs 102 may be different from one another (e.g., the IEDs may have different features and capabilities, etc.), and the loads 106 may be different from one another (e.g., motors, lighting fixtures, computer servers, etc.). In an embodiment, the loads 106 and IEDs 102 may be installed in one or more buildings or other physical locations, or they may be installed on one or more processes and / or loads within a building. The building may correspond to, for example, a commercial, industrial, or institutional building.

[0031] like Figure 1As shown in FIG, each IED 102 is coupled to one or more loads 106, which, in some embodiments, may be located "upstream" or "downstream" of the IED. For example, loads 106 include machinery or devices associated with a particular application (e.g., an industrial application), multiple applications, and / or process(es). For example, machinery may include electrical or electronic equipment. Machinery may also include controllers and / or auxiliary equipment associated with the equipment. According to aspects of the present disclosure, loads 106 include a mix of single-phase and three-phase loads (e.g., motors).

[0032] In embodiments, the IEDs 102 can monitor and (in some embodiments) analyze parameters (e.g., energy-related parameters) associated with the loads 106 to which they are coupled. For example, the IEDs 102 (e.g., metering devices) capture energy-related waveforms in the electrical system 100. As used herein, an IED is a computing electronic device optimized to perform a specific function or set of functions. Examples of IEDs 102 include smart meters, power quality meters, microprocessor relays, digital fault recorders, and other metering devices. IEDs 102 can also be embedded in variable speed drives (VSDs), uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical device. Additionally, IEDs 102 can be used to perform measurement / monitoring and control functions in a wide variety of facilities. These facilities can include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing co-location centers, data centers, distribution networks, or any other structure, process, or load that uses electrical energy. For example, if IED 102 is an electrical power monitoring device, it may be coupled to (or installed in) a power transmission or distribution system and configured to sense / measure and store data (e.g., waveform data, log data, I / O data, etc.) as electrical parameters representing operational characteristics of the power distribution system (e.g., voltage, current, waveform distortion, power, etc.). For example, a user may analyze these parameters and characteristics to assess potential performance, reliability, and / or power quality-related issues. One or more of IEDs 102 may include at least a controller (which, in some IEDs, may be configured to run one or more applications simultaneously, serially, or both simultaneously and serially), firmware, memory, communication interfaces, and connectors that connect the IED to external systems, devices, and / or components at any voltage level, configuration, and / or type (e.g., AC, DC). At least some aspects of the monitoring and control functionality of IED 102 may be implemented in a computer program accessible to the IED.

[0033] In some embodiments, the term "IED," as used herein, may refer to a hierarchy of IEDs operating in parallel and / or in series. For example, an IED may correspond to a hierarchy of energy meters, power meters, and / or other types of resource meters. The hierarchy may include a tree-based hierarchy, such as a binary tree, a tree having one or more child nodes descending from each parent node or multiple parent nodes, or a combination thereof, wherein each node represents a specific IED. In some cases, the hierarchy of IEDs may share data or hardware resources and may execute shared software. It is understood that a hierarchy may be non-spatial (such as a billing hierarchy), wherein the IEDs grouped together may not be physically related.

[0034] According to another aspect, the IED 102 can detect overvoltage and undervoltage conditions (e.g., transient overvoltages) and other parameters, such as temperature (including ambient temperature). According to another aspect, the IED 102 can provide an indication of the monitored parameters and detected conditions, wherein the monitored parameters and detected conditions can be used to control the load 106 and other equipment in the electrical system 100 in which the load 106 and the IED 102 are installed. A variety of other monitoring and / or control functions can be performed by the IED 102, and the aspects and embodiments disclosed herein are not limited to the IED 102 operating as described in the examples mentioned above.

[0035] It is understood that the IEDs 102 can take various forms and can each have an associated complexity (or set of functional capabilities and / or features). For example, one IED 102 is a "basic" IED, while another IED 102 is a "medium" IED, and yet another IED 102 is an "advanced" IED. In such embodiments, the medium IED can have more functionality (e.g., energy measurement features and / or capabilities) than the basic IED, and the advanced IED can have more functionality and / or features than both the medium and basic IEDs. For example, in embodiments, the IED 102 (e.g., an IED with basic capabilities and / or features) can be capable of monitoring instantaneous voltage, current energy, demand, power factor, average value, maximum value, instantaneous power, and / or long-duration RMS variation, and / or the IED 102 (e.g., an IED with advanced capabilities) can be capable of monitoring additional parameters such as voltage transients, voltage fluctuations, frequency slew rates, harmonic power flows, and discrete harmonic components (all at higher sampling rates), etc. It is understood that this example is for illustrative purposes only, and that, likewise, in some embodiments, an IED with basic capabilities may be capable of monitoring one or more of the above energy measurement parameters indicated as being associated with an IED with advanced capabilities. It is also understood that, in some embodiments, the IEDs 102 each have independent functionality.

[0036] exist Figure 1 In the example embodiment of FIG. 1 , the IED 102 is communicatively coupled to a central processing unit (CPU) 140 (and associated memory) via a data communications network, shown as a “cloud” 150. In some embodiments, the IED 102 may be directly communicatively coupled to the cloud 150. In other embodiments, the IED 102 may be indirectly communicatively coupled to the cloud 150 via, for example, an intermediary device, such as a connected hub 130 (or gateway), that provides the IED 102 with access to the cloud 150 and the CPU 140. Additional cloud-connected devices and / or databases are indicated by reference numeral 160.

[0037] Commonly assigned U.S. Patent Application Publication No. 2023 / 0152833, the entire disclosure of which is incorporated herein by reference, discloses a cloud-connected electrical system that may utilize aspects of the present disclosure.

[0038] Metering data from IEDs 102 is one of the most important sources of information available for troubleshooting problems that arise in electrical systems, such as system 100. Today's meters provide ample feedback for detecting, isolating, and resolving ongoing problems. However, too much data can lead to information overload, overwhelming even the most knowledgeable users. As electrical metering incorporates more complex functions, meter manufacturers are forced to provide more straightforward solutions for typical users. Many meters available on the market today are capable of detecting disturbances that occur on the systems they monitor. Metered data commonly used to characterize events can include:

[0039] the time of the event,

[0040] Triggering of alarms,

[0041] · Alarm disconnection,

[0042] Worst-case value,

[0043] The stage at which the incident occurred, and / or

[0044] Waveform capture of events.

[0045] This information, which is very important in determining the cause, severity, and source of an event, has been provided by many instruments for many years. In some cases, an experienced user can evaluate the data and determine the source of the problem; however, a typical user may not have this expertise.

[0046] The source of the disturbance can be identified as "downstream" or "downlink" (toward the electrical load that utilizes the electrical power) or "upstream" or "uplink" (toward the source of the electrical power itself). For networks, transmission systems, or any other non-radial feed systems, the supply of power may be ambiguous due to multiple sources or electrical paths. In those cases, "forward" and "reverse" can be used to describe the relative direction of the disturbance source.

[0047] To determine the root cause of a disturbance, a consultant or experienced engineer can be hired to analyze and interpret the data captured by the meter; however, obtaining the relevant information and arriving at the correct solution can be a slow (and expensive) process. It is easier (and cheaper) to troubleshoot power quality issues as they occur, rather than days or weeks later.

[0048] Benefits of this feature may include:

[0049] Quickly and accurately determine the source of voltage dips / swells,

[0050] Locate the source of voltage swells that occur in the presence of voltage sags,

[0051] Minimize recurring events by quickly locating the source of disturbances, and

[0052] Improve production reliability and uptime.

[0053] Figure 2 Another example electrical system 200 is illustrated in which aspects of the present disclosure may be used to automatically assess voltage imbalance. One or more embodiments may optionally use location information of one or more IEDs 202 within the electrical system 200 to provide spatial context. For example, Figure 2 The illustrated radially fed system 200 includes two step-down transformers 204 and has a plurality of IEDs 202 monitoring a mix of single-phase and three-phase downstream loads 206. It is to be understood that Figure 2 The electrical system 200 is only one embodiment of many potential embodiments for teaching the concepts described herein.

[0054] Figure 3An example process 300 for implementing various aspects of the present disclosure is illustrated, which allows for analysis of waveform capture data. Process 300 begins at 302 and proceeds to 304 to assess whether the algorithm, IED, and / or system are appropriately / correctly configured for executing the application. Configuration may occur or require configuration in at least one of the IED (e.g., meter, etc.), edge software, cloud-based application, gateway, PLC, or any other relevant or necessary aspect of the EPMS (Electrical Power Management System). Some aspects that may require configuration include: device type, signal sampling rate, load type, customer type, process type, alarm settings, logging interval, load metadata (e.g., motor nameplate data, etc.), EPMS metadata, process, location, time context (e.g., timestamps, synchronization, etc.), features, etc. (this list is not exhaustive). In the event that one or more aspects of the algorithm, IED, and / or system are not sufficiently configured, an indication may be provided to the end user to address the issue, or alternatively, various aspects of the present disclosure may automatically configure or resolve the issue itself. One or more embodiments may optionally re-evaluate the configuration to ensure that the application no longer has any configuration issues.

[0055] Proceeding to 306, an IED (such as a conventional power metering device) is typically capable of acquiring a univariate, bivariate, or multivariate time series of electrical signal data samples (such as current and / or voltage data samples) within a time window. If the measured current and / or voltage signal exceeds or drops below a predetermined threshold, some IEDs will automatically store the waveform data and generate a waveform capture that provides a graphical representation of how the signal (e.g., current and / or voltage signal) fluctuates or changes over time. Depending on the duration of the event or the memory limitations of the IED capturing the signal, the duration of the graphical representation of the captured signal can range from a sub-cycle to multiple (or even many) cycles.

[0056] Waveform captures are typically generated by IEDs in response to unexpected electrical events that cause changes in voltage and / or current signals in the electrical system. The consequences of unexpected electrical events can be benign, but they often result in complications related to power quality, including equipment damage, equipment failure, and safety concerns. Waveform capture data can be analyzed to diagnose electrical events, enabling more timely resolution or mitigation of power quality-related issues.

[0057] The first step in process 300 is to capture electrical signal waveform data from or associated with the electrical system being monitored, evaluated, or analyzed. The electrical signal waveform data can include directly measured data (e.g., voltage, current, etc.), derived data (e.g., power, energy, etc.), and / or other external data (e.g., I / O, time, etc.). Additionally, metadata (e.g., location, load type, customer type, load characteristics, IED capabilities and limitations, etc.) can be attached to the captured electrical waveform data and optionally used as part of the analysis. The waveform data consists of a series of incremental, time-series, high-speed samples of the electrical signal that, when plotted on a time graph, represent the waveform of the measured / captured electrical signal.

[0058] Figure 4A shows a transient waveform capture (WFC) of an electrical event, that is, the raw voltage and current signals captured by an IED. As described in more detail below, Figure 4B Indicates Figure 4A The signal is the signal after it has been preprocessed. Figure 4C represents the RMS value of the instantaneous electrical signal (i.e., derived from the instantaneous WFC), and Figure 4D Examples of derived three-phase active power values ​​and total active power value are illustrated. Figure 5A and Figure 5B illustrate example waveform capture samples and corresponding derived RMS values ​​of the waveform capture samples, respectively, and Figure 5C 1 illustrates an example of derived three-phase active power values ​​and a total active power value according to an embodiment of the present disclosure. It is important to note that it is not necessary to plot the waveform data as a graph; analysis of the waveform data can be (and often is) performed using one or more phases and / or parameters of the signal data originally sampled from the electrical event.

[0059] At 308, process 300 filters events prior to analysis. The digital representation of an analog electrical signal is subject to the limitations of the digital-to-analog conversion process, including all intermediate hardware, algorithms, and other associated or related constraints. Any intentional, unintentional, and / or natural filtering between the original analog signal and the final digital waveform capture data will (to some extent) degrade the quality of the subsequent digital data, potentially affecting the authenticity of any subsequent analysis results.

[0060] For example, samples from an analog electrical signal are used to create a representative digital signal, and the sampling rate of an IED is inversely proportional to the inter-sample interval (i.e., the gap between samples). The sampling rate is measured in Hertz (e.g., Hz, kilohertz, megahertz, etc.) or in samples per cycle, and the inter-sample interval is measured in time (e.g., seconds, milliseconds, microseconds, etc.). Because the inter-sample interval is the inverse of the sampling rate in Hertz, reducing the sampling rate results in a longer inter-sample interval. Reducing the sampling rate of an IED also has the unfortunate effect of ignoring (i.e., not measuring) more data between each sample. For example, Figure 6A and Figure 6B Two exemplary digital waveform captures taken from the same analog event signal at two different sampling rates (eg, 512 samples / cycle and 16 samples / cycle, respectively) are shown, illustrating how a lower sampling rate can lose important details in the subsequent digital data. Figure 6A shows a digital representation of an analog signal sampled at 512 samples / cycle with the 9th harmonic component present, and Figure 6B Shown is a digital representation of the same analog signal sampled at 16 samples / cycle, with the 9th harmonic component present. As can be seen, even though the input analog signal is identical, the two digital waveform captures appear different. This effect is known as signal aliasing, and this example illustrates how a lower sampling rate (i.e., 16 samples / cycle in this case) can lose important details when subsequently converted to digital data. In addition to the effects of aliasing shown in this example, other examples of limitations in converting analog signals to digital signals can include other IED hardware limitations (e.g., front-end filtering, etc.), instrument transformer limitations (e.g., insufficient bandwidth, etc.), power transformer limitations (e.g., filtering, configuration, etc.), and so on.

[0061] Process 300 performs one or more analyses at 310, 312, 314, 316, 318, 320, and 322 to identify candidate locations for the detected disturbance event. Preferably, these analyses are performed simultaneously, but they can also be run sequentially or in parallel, in a different order, etc. The location detection algorithm can be run, for example, in series, and the results processed together after the algorithm runs. In an embodiment, aspects of the present disclosure include "short-circuiting" the processing if there is a high degree of confidence in the results, especially if the current analysis is based on previous data.

[0062] Referring to step 310, the "Disturbance Power and Energy" method evaluates the difference between the total three-phase instantaneous power and the steady-state three-phase instantaneous power before the event, which is defined as "Disturbance Power" (DP). The integral of the disturbance power with respect to time represents "Disturbance Energy" (DE), which is the impact of the disturbance event on the energy flow measured by the IED. Because the energy that can flow toward the disturbance source will increase, the location of the disturbance source in the IED can be determined by using the direction of the disturbance energy flow (i.e., upstream or downstream of the IED) as an indicator.

[0063] At 312, the "actual current magnitude" method uses the current magnitude and the phase angles of the voltage and current to calculate the actual current flow at the IED located on the electrical circuit experiencing the disturbance event. The direction of the disturbance event relative to the IED's location on the circuit (i.e., upstream or downstream) is determined by the direction of the current flow. If the actual current flow is positive, the disturbance event is considered downstream of the IED; if the actual current flow is negative, the disturbance event is considered upstream of the IED.

[0064] At 314, process 300 includes a "phase impedance" method for evaluating: 1) the relationship between the measured pre-event / pre-sag impedance magnitude and the measured event / sag impedance magnitude, and 2) the event / sag impedance phase angle. If the measured event / sag impedance magnitude is less than the pre-event / pre-sag impedance magnitude and the event / sag impedance phase angle is greater than zero, then the event / sag source is located downstream of the IED that measured the event. If neither of these conditions holds true, then the event / sag source is located upstream of the IED that measured the event.

[0065] At 316, the "negative sequence impedance" method uses the negative sequence voltage and current components to derive a negative sequence impedance, which is used to determine the event / sag source location (i.e., downstream or upstream of the IED measuring the event). The derived negative sequence impedance is compared to a fixed impedance threshold to determine whether the event / sag is downstream or upstream of the IED measuring the event. The threshold can be optimized using heuristic algorithms or AI-based algorithms (such as machine learning).

[0066] Proceeding to step 318, the "negative sequence torque" method uses the negative sequence voltage and current components to derive the negative sequence torque, which is then used to determine the event / sag source location (i.e., downstream or upstream of the IED measuring the event). A maximum torque angle (MTA) setting is used as part of this calculation, which can be optimized using heuristics or AI-based algorithms (such as machine learning).

[0067] The "positive sequence impedance" method at 320 derives the residual positive sequence voltage by determining the difference between the measured pre-event / pre-sag positive sequence voltage and the measured event / sag positive sequence voltage. Similarly, the residual positive sequence current is determined from the difference between the measured pre-event / pre-sag positive sequence current and the measured event / sag positive sequence current. The residual positive sequence impedance is calculated by dividing the residual positive sequence voltage by the residual positive sequence current. The direction of the disturbance event is then determined by assessing which quadrant the residual positive sequence impedance lies in: for example, a downstream fault is in the third quadrant (that is, between 180 degrees and 270 degrees), and the source or origin of the upstream event is in the first quadrant (that is, between 0 degrees and 90 degrees).

[0068] At 322, process 300 executes another method. There are other useful technical approaches that may also be considered. For example, various analytical methods (such as machine learning (ML)) can be successfully employed to determine whether the source of a sag / swell event (e.g., a fault, a load outage, etc.) is located upstream or downstream of the point where the IED is installed or the sag / swell event is measured / metered. Machine learning focuses on using statistically based algorithms to formalize and / or extend "case-specific" data attributes / properties to newly captured data to facilitate the execution of analytical tasks without explicit instructions. The specific cases mentioned here are used to "teach" or "train" the ML algorithm, thereby constructing an ML model for considering and analyzing new cases accordingly. It is generally necessary to build a valid (i.e., sufficient relevant data to produce successful and / or statistically valid results in most cases) database or a library of labeled (i.e., previous, confirmed, estimated, and / or verified) waveform capture data to create a successful ML model, and thus a successful ML application.

[0069] The ML approach can be improved by incorporating feedback loops into the process. The end user (or through some other means) can continue to "tag" the waveform capture data as new situations are analyzed. Over time, the deliberate and accurate assessment of dips and spikes brought about by external sources (i.e., manually, by some other algorithm, etc.) will continue to update and / or improve the ML algorithm / model as additional events are incorporated into the ML model.

[0070] While many electrical utilities use similar equipment (e.g., reclosers, fuses, lightning arresters, etc.), energy consumers (i.e., energy customers or end users) may employ a range of load types depending on their business requirements and processes. Unique customer types (e.g., healthcare, data centers, office buildings, etc.) often require specific equipment (e.g., healthcare may use imaging equipment, etc.) or may use equipment in a specific way that produces a unique energy signature. Because specific electrical equipment is often used within a specific customer type, some waveform event characteristics may also be more typical for a specific customer type (especially downstream events).

[0071] For example, petrochemical customers often use large motors in their oil and gas operations to move a wide variety of fluids (e.g., oil, gas, water, slurries, etc.) via a range of pump types (e.g., centrifugal, positive displacement, diaphragm, conveying, etc.). When large motors start up, they can generate inrush currents as high as 6 to 10 times their rated full-load amperage, which can cause significant voltage drops in the system (depending on the system impedance). These voltage drops can potentially affect other equipment connected to the same system and, if the voltage dip is deep enough, can trigger a waveform capture (WFC) at an IED. If a WFC is triggered, it is determined to be downstream of the IED that captured the WFC, as that is where the motor is located (i.e., the load is at the end of the circuit) and, therefore, the source of the event. In this case, background knowledge of the customer type and event characteristics (i.e., large inrush currents, etc.) can be leveraged to provide a higher degree of confidence that the source of the voltage event is downstream of the IED. This understanding can be further improved by incorporating known waveform characteristics associated with specific customers and / or load types to improve the ML approach.

[0072] It is important to note that each analytical method has its own limitations, which should be considered when ultimately determining the source location or origin of a disturbance event (i.e., upstream or downstream of an IED). For example, any one or more analytical techniques may produce uncertain, "borderline" results. In these cases, the confidence level of that one or more analytical techniques will be lower than that of techniques with more certain results. Alternatively, a general consensus among multiple analytical techniques should strengthen certainty and indicate a higher confidence level in the results.

[0073] Due to the nature of each discrete method / technique, its constraints may be unique and subject to various conditions. Reasons for increased uncertainty in discrete methods / techniques can include strict configuration requirements, limitations due to fault type, misapplication, limitations in the scope of the method / technique, instrument transformers used with specific IEDs, and so on. For example, the disturbance power (DP) and disturbance energy (DE) in the disturbance power and energy method (i.e., step 310) are not always consistent (or may be inconsistent between two or more IEDs), thereby introducing uncertainty into the results. Alternatively, the phase impedance method (i.e., step 314) is more ideally suited for single-line-to-ground faults, while other methods may be better suited for, for example, symmetrical three-phase faults.

[0074] Additionally, constraints associated with the data used to create the WFC measurements can also create uncertainty in the results. The lower sampling rate of the IED performing the A / D conversion can lose or attenuate relevant information (or even introduce misleading artifacts), ultimately producing substandard results. Even the instrument transformers (i.e., VTs and / or CTs) associated with the IED can filter out important data, thereby reducing the fidelity of the results. The range of the A / D converter can affect the results; if the full-scale range of the A / D converter used for current is less than the magnitude of the current experienced during the fault, the current waveform can appear "flat" rather than sinusoidal (sometimes referred to as "flat-topped"). In another example, a saturated instrument transformer can produce something that looks like a shark's dorsal fin—curved on one side and flat on the other.

[0075] Alternatively, if the algorithm is based on small current changes from one sample to the next, perhaps in the milliamp range, a measurement optimized to handle peaks of hundreds of amps may not be able to accurately discern that small change. If voltage and current samples are not recorded simultaneously due to A / D limitations, impedance and power calculations will be affected.

[0076] Referring now to step 324 in process 300, given the existence of so many methods / techniques for evaluating WFC, the limitations and possible impacts within these techniques, and the constraints associated with converting analog data into digital representations, it is important to evaluate WFC in a manner that takes these possible influencing factors into account. Because each method / technique (if applicable) provides its own independent conclusion regarding the location (i.e., upstream or downstream) of the source or origin of the disturbance event relative to the associated IED performing the analysis, it is important to evaluate all methods and provide the "best" (i.e., most likely) result selected from these candidate locations.

[0077] Just as there are many discrete methods / techniques for assessing / evaluating the source location or origin (downstream or upstream) of a disturbance event, there are also multiple ways to combine the results from these different discrete methods / techniques into a "most likely" result. Here is a short list of exemplary possible methods and corresponding descriptions:

[0078] Simple aggregate voting;

[0079] Confidence-weighted voting;

[0080] Algorithmically weighted voting;

[0081] Algorithms and confidence weighting;

[0082] Highest confidence level;

[0083] Optimal algorithms;

[0084] Utilize partial weighting of ML; and

[0085] Dynamic weighting of ML.

[0086] In one embodiment, a simple aggregate voting method aggregates the results generated from each discrete correlation method / technique through a simple voting process. In this method, the algorithm from each correlation method independently determines the source location or origin of the disturbance event (ignoring confidence). These determined results are then evaluated and quantified (i.e., downstream vs. upstream), and the most frequently occurring result (e.g., statistically, the highest modal score) is determined as the final result / answer. If the majority of the results from the discrete algorithms are considered uncertain, the final result / answer can be given as "uncertain / undetermined".

[0087] According to one embodiment, where confidence levels are calculated (qualitatively or quantitatively) for discrete correlation methods / techniques, a confidence-weighted voting method can use the confidence levels to provide "weights" to the discrete correlation methods / techniques accordingly. Once the weights have been applied to the discrete correlation methods / techniques, the results from the discrete correlation methods / techniques can be aggregated into a single "most likely" result (i.e., downstream or upstream) using some applicable statistical method. The applicable statistical method for aggregating weighted results can be performed using any valid or relevant statistical method that would be understood by one of ordinary skill in the art.

[0088] The algorithmic weighted voting method according to one or more embodiments can weight discrete, related method / technique results based on at least one of the specific algorithm being used and / or the application associated with the event type being analyzed (e.g., process, customer type, load type, etc.). As previously mentioned, some algorithms are more effective in certain situations, and this method takes these conditions into account. The weighting associated with the method / technique can be learned or prescribed over time based on historical results.

[0089] In one embodiment, the algorithmic and confidence weighting method combines the two methods mentioned above to produce an aggregate confidence that includes both confidence-weighted votes (i.e., the confidence of the results of each method / technique) and algorithm-weighted votes (i.e., the confidence associated with the application of each method / technique). Again, different statistical or mathematical procedures can be used to combine the two confidences. For example, a simple multiplication of each respective confidence can be used (e.g., [confidence of the result × confidence of the algorithm = final confidence]). Other procedures for calculating the final result and confidence can also be used.

[0090] The highest confidence method according to one embodiment simply selects the result with the highest confidence from the results provided by the evaluated methods / techniques.In the event that two or more evaluated methods / techniques with high respective confidences contradict each other, additional steps will have to be taken.

[0091] As previously mentioned, some methods / techniques are more suitable for certain applications, use cases, or event types. According to one or more embodiments, the best algorithm approach simply selects and recommends the results (if possible) from a determination of the discrete method / technique that is most suitable for that event type or characteristic (taking into account all relevant associations, limitations, and possible impacts).

[0092] In one embodiment, process 300 employs partial weighting using ML. As described with respect to step 322, ML is a viable method for analyzing and determining the source of the event disturbance location. However, if there are certain considerations or uncertainties associated with the method (e.g., insufficient training, etc.), the end user may not want to rely solely on the results from ML. In this case, the ML model can be used to provide only partial input (weighting) in determining the final result (i.e., downstream or upstream). The level of input (weighting) can be adjusted accordingly if necessary.

[0093] As ML models are enhanced, enriched, and improved, their outcomes should also improve. In one embodiment, a dynamic weighting approach to ML modifies / adapts the weights assigned to ML models over time to reflect and / or accommodate these improvements. These improvements and weights can be statistically calculated, temporarily adjusted, and / or manually changed / updated based on appropriate feedback.

[0094] Now refer to Figure 7 , aspects of the present disclosure provide for systematic evaluation and determination. Using multiple IEDs to provide redundant analysis of the same event can also be used to provide a higher confidence level in the results. For example, Figure 7 The following diagram illustrates a simple radially fed electrical system hierarchy using three IEDs: a parent IED (M1), two child IEDs (M2, M3), and two grandchild IEDs (M4, M5). Each IED measures relevant electrical signals (e.g., voltage, current, etc.) at its discrete installation point within the electrical system, and each IED (M1, M2, M3, M4, M5) employs an event evaluation algorithm to determine the source location or origin of the disturbance event relative to the electrical system. Assume that at some point in time, a disturbance event (e.g., a fault) occurs on the electrical system, causing a downstream load disturbance. After the algorithm of each discrete IED processes the electrical signal data associated with the disturbance event, each discrete IED provides a result indicating whether the source location or origin of the disturbance event is downstream or upstream of the corresponding IED. Within the context of the electrical system hierarchy, the results can then be analyzed to determine the source location or origin of the disturbance event. In this case, the parent IED indicates a downstream source location, while the two child IEDs and two grandchild IEDs indicate an upstream source location. Therefore, the hierarchical context indicates that the source location or origin of the disturbance event is likely to be located somewhere between the parent IED installation point and the two child installation points.

[0095] Aspects of the exemplary system allow for a partially redundant analysis of the source location or origin of a disturbance event (although not in every case). For example, all IEDs electrically connected to feeder 1 (Fdr1) and below unanimously indicate that the source location of the disturbance event is upstream (i.e., M2, M4, M5). The agreement of multiple IEDs increases the confidence in this conclusion. The parent IED (M1) and the child IED (M3) also provide corresponding indications about the source location of the disturbance event; however, neither includes one or more additional IEDs to further confirm the conclusion. This does not imply that the overall conclusion is incorrect, only that the confidence in the conclusion may not be as convincing. As more IEDs capable of assessing the source location or origin of a disturbance event are applied to the entire electrical system, the overall conclusion about the source location or origin of the disturbance event can generally be strengthened. To expand on this concept, the enhanced confidence utilized by the hierarchical relationship of two or more IEDs can also be utilized to aggregate individual confidence levels at discrete IEDs.

[0096] Those skilled in the art will recognize that there may be other statistically relevant methods that can be used to assess the source location or origin of a disturbance event. The goal / objective of the analysis described herein is to 1) provide more accurate and more definitive results, and 2) reflect an accurate confidence level associated with these results. Where applicable, by evaluating both the outputs of multiple algorithms and the hierarchical relationships between discrete IEDs, the most reasonable results and reasonable confidence levels can be provided.

[0097] Refer again Figure 3 , process 300 stores the information at 326. The stored information includes, but is not limited to, data, results, confidence levels, and / or any other information associated with the present disclosure, and may be stored in part or in whole in one or more IEDs, on-site or off-site software systems, cloud-based systems, gateways, or some combination thereof for future reference / benefit. Waveform capture data, pre-event data, post-event data, event parameters and / or characteristics (and other information associated with the present disclosure) may be stored (e.g., locally, remotely, and / or in the cloud). The stored information may be used to improve the algorithm (in part or in whole) or the methods / techniques used accordingly by the algorithm. For example, the stored data, metadata, or other information may be used to improve the ML model. Similarly, the application may utilize the updated customer type and / or load type to provide at least one of better results or confidence calculations.

[0098] Proceeding to 328, process 300 feeds back into the optional event configuration step to evaluate the results of the discrete method / technique at one or more IEDs, software systems, cloud-based applications, gateways, etc., thereby initializing, improving, or optimizing one or more settings or configurations. If relevant data is measured and available (e.g., from a storage device, etc.), changes to the settings or configurations can be made at any time, and analysis can be performed post-hoc. For example, assume that waveform capture data from a disturbance event has been measured and saved from multiple IEDs, and the source location or origin of the disturbance event has been determined at each discrete location. At a later time, metadata is specified that provides a hierarchical relationship between each discrete IED. This subsequent metadata (i.e., knowledge of the hierarchical relationship) can be used to provide more accurate and certain results and increase the confidence level associated with the results (as described above in the "System Evaluation and Determination Description"). Alternatively, post-hoc changes to the IED sampling rate configuration cannot be used to improve the previous evaluation or results of the discrete method / technique because the uncaptured / unmeasured / unsampled data is unrecoverable (i.e., lost).

[0099] In all cases, improvements to the event configuration (ie, in the IED, software, cloud-based application, gateway, etc.) are useful to optimize features and improve data and / or subsequent results immediately after making the changed / updated configuration.

[0100] The programmed configuration (i.e., in a device, gateway, software, and / or cloud-based application) implementing the features of the present disclosure preferably includes any parameters to be evaluated (e.g., voltage, current, active power, apparent power, reactive power, etc., or combinations thereof). The configuration data to be analyzed may also include a periodic data sampling rate (i.e., acquisition rate, logging interval, etc.) with an associated time stamp. For example, the acquisition of data may be time-based (e.g., periodic), process-based (e.g., when certain devices or processes are operating), arbitrary (e.g., random or non-periodic), and / or some combination thereof. In one embodiment, the sampling / measurement rate of data may be anywhere from one cycle to a year or longer (e.g., several cycles, seconds, minutes, hours, days, weeks, months, seasons, years, etc.), but is preferably measured in minutes, hours, or days. It is understood that measurement activity may last up to a year, but the preferred sampling rate is typically at least daily. For example, the system is often configured to capture current values ​​every 5 minutes, apparent power every 15 minutes, or minimum, average, and maximum RMS voltage every 10 minutes. Sampling / measurement of data may also occur at specified times within the process when a particular device is operating.

[0101] Figure 3 The process 300 provides a result / answer at 330. In response to the occurrence of the disturbance event, recommendations for mitigating the disturbance event can be provided to the end user. The recommendations can be provided to the end user via at least one of a text, an email, a report, an alarm, an audible communication, a communication on an interface of a screen / display, or any other form of interaction or communication. For example, the recommendations can suggest that the end user install one or more sag / sag mitigation technologies to mitigate the disturbance event. For example, the sag / sag mitigation technology can include at least one of the following: for example, a dynamic voltage restorer (DVR), an uninterruptible power supply (USP), a constant voltage transformer (CVT), a step-down starter, a static switch, a system design enhancement. In one embodiment, the process 300 takes at least one action at 330 to resolve the at least one disturbance event. It is understood that the example recommendations and actions listed above are just some of many possible recommendations and actions, which will be clear to one of ordinary skill in the art.

[0102] After completing at least one disturbance event assessment, various types of data, results, and recommendations may be shared with the end user. Some of the data / results / information / recommendations that may be included are:

[0103] Voltage sag / drop amplitude

[0104] Various statistical analyses

[0105] Voltage dip / sag type

[0106] Confidence

[0107] Voltage sag / drop direction

[0108] Fault current

[0109] Voltage dip / sag location

[0110] Duration

[0111] Impact of voltage sag / drop

[0112] Historical analysis and trends

[0113] Phases affected by voltage dips / sags

[0114] Tracking and recovery time

[0115] Causes of voltage sag

[0116] ·recommend

[0117] Configuration recommendations

[0118] Mitigation recommendations

[0119] Relief position

[0120] Again, it is important to note that the above list provides only some of the possible types of outputs associated with disturbance events and is not an exhaustive list, as will be understood by one of ordinary skill in the art.

[0121] Process 300 ends at 332. In some embodiments, process 300 may return to step 302 and repeat again, for example, automatically or in response to user input and / or control signals.

[0122] Likewise, it is important to note that limitations always exist when converting analog signals to representative digital signals. The analog-to-digital conversion process is lossy and (depending on the characteristics of the signal) may introduce errors, limitations, or artifacts in one or more of the algorithms and / or evaluations mentioned above. These errors or limitations may be introduced externally by the associated instrument transformer or other intermediate components, or internally by signal processing issues within the IED's front end, within the IED (e.g., undersampling, aliasing, anti-aliasing applications, etc.), or elsewhere. Aspects of the present disclosure attempt to limit the impact of some of these issues so that the quality of the digital representation of the signal is adequate and the validity of the results is unquestionable.

[0123] For example, high-speed electrical events often contain high-frequency spectral content. If the instrument transformer cannot transmit this content to the IED, or if the IED's sampling rate is insufficient to adequately sample the high-frequency components of the electrical signal, the digital representation of the analog signal may be unsatisfactory. In such cases, the evaluation of the digital signal can produce inaccurate results that can lead to misinterpretations, flawed decisions, and unintended consequences.

[0124] For this reason, the quality of the digital signal representation is an important factor in evaluating an event. Bandwidth limitations of instrument transformers and / or IEDs should be considered and addressed. For example, unintentional instrument transformer filtering or insufficient IED sampling rates can directly impact the quality of the digital signal and the conclusions drawn. Due to the potential degradation of digital signal quality and frequency content at high speeds, one or more embodiments may impose limitations on the brevity / briefness of electrical events that can be analyzed / evaluated. Data and / or results from a given IED (or IEDs using a given CT or VT / PT set) may be suppressed accordingly based on their specific sampling rate, aliasing properties, lack of anti-aliasing, bandwidth, or other characteristics. Additionally, event characteristics may be used to determine the quality and / or validity of evaluations originating from any one or more IEDs.

[0125] Because every electrical system, event, IED characteristics and features, instrument transformers, etc., and load is unique, there are subtle limitations or specific constraints associated with how the system or algorithm responds to different events such as those discussed above. For example, one algorithm or IED may perform better than another under certain conditions. Evaluating events or problems from different locations at different points within the electrical system is useful for many reasons, including: 1) helping to locate the source or origin of the problem (i.e., upstream or downstream of the IED), 2) helping to quantify the severity of the problem, and 3) providing redundant evaluation of the event or problem from different "viewpoints."

[0126] This third reason is important because it increases the confidence level in the results and / or determinations of one or more of the algorithms discussed above. For example, consider a first IED evaluating a voltage sag event, which indicates that the source or origin of the disturbance event is downstream of the IED. Additionally, a second IED, located higher in the electrical system hierarchy, may evaluate the same voltage sag event and also indicate that the source or origin of the disturbance event is downstream of the second IED. Having both the first and second IEDs produce similar results provides a more convincing conclusion that the source or origin of the voltage sag is indeed downstream of the first IED.

[0127] Determining a "confidence level" can be performed in many ways. For example, a simple statistical approach could be to evaluate the results from each individual IED and combine them accordingly. In the example above, the two independent IEDs agree that the source or origin of the voltage sag event is downstream of the first IED, which is lower in the electrical hierarchy (based on where the data was measured / captured within the electrical hierarchy). The two results from the first IED and the second IED agree with each other, so the confidence level in their conclusions is higher than relying solely on the results from a single IED. For example, the first IED can also be weighted more heavily than the second IED. In one implementation, the algorithm, evaluation, or data used in the first IED can be deemed by a professional in the field to be more efficient, more applicable, or of higher quality. Thus, during configuration / setup or during its analysis / evaluation, the expert can give the first IED a heavier weight. In another implementation, the first IED is considered to be twice as accurate as the second IED (possibly because it is a higher quality and more capable IED). In the event that the results from the two systems conflict (i.e., a first IED indicates that the source of the voltage sag is downstream while a second IED indicates that the source of the voltage sag is upstream), the conclusion of the first IED will be used because it has a higher probability of being correct than the result from the second IED. There are many statistical and mathematical methods and techniques for combining results from different systems that can be understood or considered by those of ordinary skill in the art.

[0128] Example 1: Phase-to-phase fault

[0129] Refer again Figures 4A to 4D , in this example, several waveforms (measured and derived) are provided to illustrate various aspects of the present disclosure. Figure 4A Both the measured instantaneous three-phase voltage (upper waveform) and the measured instantaneous three-phase current (lower waveform) are illustrated, respectively. Figure 4B The diagram shows Figure 4A The pre-processed instantaneous three-phase voltage and pre-processed instantaneous three-phase current waveforms are provided in . Figure 4C The diagram shows Figure 4B The RMS value of each phase is derived from its corresponding instantaneous waveform shown in . Figure 4D The diagram shows Figure 4B The active power value derived from the corresponding pre-processed instantaneous waveform shown in . Figure 4D Also shown are the derived total active power values, which are Figure 4D It should be noted here that although the examples provided herein describe a three-phase system, aspects of the present disclosure may be applied to any system having one or more phases (e.g., single-phase, two-phase, three-phase, etc.).

[0130] Figure 4A The voltage waveforms shown in FIG. 1 indicate that an exemplary event causes two phases (ie, phase V a and V b ) experiences a voltage sag, and the third phase voltage (i.e., V c ) remains relatively constant in amplitude and phase. In addition, Figure 4A The current waveform shown in FIG indicates the current associated with the affected phase voltage (ie, I a and I b ) also experiences significant deviations from its steady-state / pre-event characteristics. In the illustrated embodiment, the current I a and I b The amplitude of the third phase current (i.e., I c ) remains relatively constant in amplitude and phase.

[0131] Preprocessing at least one of the three-phase voltage signal and the current signal may be beneficial or even necessary to improve data quality and / or signal quality to provide optimal results and / or conclusions. For example, the following is possible:

[0132] The phase conductors are interchanged with each other,

[0133] Phase conductors are connected incorrectly,

[0134] The polarity of one or more conductors (voltage and / or current) is to be reversed at the IED,

[0135] Applying incorrect transducer ratios in the IED,

[0136] ·etc.

[0137] In most cases, addressing and / or correcting any one or more of these issues before performing an assessment will minimize errors, provide better analysis, and improve the accuracy of determining the location or origin of the disturbance event.

[0138] For example, Figure 4B The three-phase voltage and current waveforms shown in Figure 4A The derived results of the voltage and current waveforms are shown in . Figure 4A As indicated at 402, the phase A current during the event (ie, I a ) are flattened or "clipped" at their peaks, which is caused by limitations in the IED analog-to-digital conversion process. Figure 4A The affected waveform or waveforms shown in FIG are pre-processed (i.e., prior to fault analysis) to reduce the effects of clipping (i.e., "de-clip"), as indicated at 404, which improves the results and reliability of the fault analysis algorithm. This pre-processing provides more accurate waveform data, resulting in better representation in the waveform graph.

[0139] Figure 4C The RMS values ​​of the voltage and current for this event are shown in a slightly more quantifiable format. Figure 4B For example, this graph indicates that V b drops to nearly 72% of the nominal value, and V a In this case, if the nominal voltage is 480 volts, then V a and V b The voltage drops to approximately 374 V and 346 V, respectively. This drop in voltage can easily cause connected electrical equipment, including critical equipment, to lose power due to insufficient voltage levels. Figure 4C The rms current levels shown in FIG also indicate significant deviations from their nominal steady-state levels. In this case, the graph indicates I a Increased by approximately 500% and I b An increase of approximately 425%. Figure 4C For the values ​​shown, the duration of the event is approximately 11 seconds.

[0140] Figure 4D The three-phase active power values ​​and the total active power value derived during a voltage sag event are illustrated. As shown, the active power value of phase A and the active power value of phase B (i.e., P a and P b ) shows significant deviation during the voltage sag event, while the active power value of phase C (i.e., P c ) remains relatively constant. In fact, P a and P b are roughly mirror images of each other, indicating that the event is a phase A and a phase B (i.e., P a and P b ) between the phases.

[0141] Refer again Figure 3 In step 310, the disturbance power and energy algorithm evaluates the difference between the total three-phase power value during the event and the total three-phase power value before the event (i.e., before the event) to determine the disturbance power (DP). To help establish the calculation, it is important to note (as will be clear to one of ordinary skill in the art) that the total power value (i.e., P total ) is given by the sum of the active phase power values ​​(i.e., P total =P a +P b +P c ). For example, in Figure 4D When the time on the graph shown is 2 cycles, P a Roughly equal to 15MW, P b is roughly equal to 3MW, and P cRoughly equal to 5MW. Because the total active power value (P total ) is equal to the sum of the individual phases at that point in time (i.e., time = 2 cycles), so the total active power value is approximately equal to 15MW + 3MW + 5MW, or about 23MW. It is important to further note that the term "approximately" is used here because these values ​​are only visually estimated in this example. To understand, P a 、P b and P c The value of the sum of the values ​​and the result (i.e., P total ) can be determined precisely.

[0142] A simple assessment of the source location or origin of the disturbance for this event using the disturbance power and energy algorithm would be to calculate the total active power value (P total event ) Evaluate the total active power value (P total pre-event ), as discussed in the above example. Assuming the IED is configured to indicate positive energy flow from source to load, Figure 4D The total active power value during the event shown in total event ) increases to the total active power value before the event (P total pre-event ) has a magnitude almost double that of the IED (approximately 13.5 MW); that is, from approximately 23 MW to approximately 13.5 MW. This indicates that the source or origin of the disturbance is downstream of the IED from which the data was measured. Another algorithm or algorithms may also be used to determine the source or location of the disturbance event, and the voting aspects of the present disclosure may be performed accordingly.

[0143] Example 2: Phase-to-ground fault

[0144] Refer again Figures 5A to 5C In this example, a second type of disturbance event is similarly analyzed, and again, several waveforms (measured and derived) are provided to illustrate aspects of the invention. Figure 5A Both the measured instantaneous three-phase voltage (upper waveform) and the instantaneous three-phase current (lower waveform) are illustrated, respectively. Figure 5B The diagram shows Figure 5A The RMS values ​​of each phase are derived from their corresponding instantaneous waveforms shown in . Figure 5C The diagram shows Figure 5A The active power values ​​of the three phases are derived from the corresponding instantaneous waveforms shown in . Figure 5C Also illustrated is a fourth set of values ​​(ie, derived total active power values), which are Figure 5C The sample-by-sample sum of the active power values ​​of each of the three phases shown in FIG.

[0145] Figure 5AThe voltage waveforms shown in FIG. 1 indicate that this event causes two voltage phases (i.e., phase V a and V c ) undergoes a sudden increase in amplitude, while the third voltage phase (i.e., V b ) shows a significant voltage dip. In addition, Figure 5A The current waveform shown in FIG indicates the current associated with the "sudden rise" phase voltage (ie, I a and I c ) remains somewhat constant relative to its steady-state / pre-event characteristics; however, during the event, the third phase current (i.e., I b ) more than doubles in magnitude.

[0146] Figure 5B The RMS values ​​of the voltage and current for this event are shown in a format that is slightly easier to quantify and analyze. Figure 5A For example, the graph indicates V b drops to approximately 35% of the nominal value, V a suddenly rises to nearly 115% of the nominal value, and V c In this case, if the nominal voltage is 480 volts, then V a It suddenly rises to about 552 volts, V b drops to approximately 168 volts, and V c These variations (sags and swells) in phase voltage can easily cause connected electrical equipment (including critical equipment) to lose power due to brief (ie, on the order of cycles) overvoltage and undervoltage conditions. Figure 5B The rms current levels shown in FIG also indicate significant deviations from their nominal steady-state levels. In this case, the graph indicates I a Increased by about 170%, I b Increased by approximately 510%, and I c An increase of approximately 260%. Figure 5B For the values ​​shown, the duration of the event is approximately 17 seconds.

[0147] same, Figure 5C The three-phase active power values ​​and the derived total active power value are illustrated during a voltage sag event. As shown, all three phases (i.e., P a 、P b and P c ) showed a significant increase in their power flow values, among which P c The power amplitude increased by almost 300% from its pre-event value.

[0148] Refer again Figure 3In step 310, the disturbance power and energy algorithm evaluates the difference between the total three-phase power value during the event and the total three-phase power value before the event (i.e., before the event) to determine the disturbance power (DP). A simple assessment of the location or origin of the disturbance source for the event using the disturbance power and energy algorithm would be to calculate the total active power value (DP) relative to the total active power value (DP). total event ) Evaluate the total active power value (P total pre-event ). Similarly, assuming the IED is configured to indicate positive energy flow from source to load, Figure 5C The total active power value during the event shown in total event ) increases to the total active power value before the event (P total pre-event ) has a magnitude more than double that of the IED (approximately 950 kW to approximately 1.9 MW). This indicates that the source or origin of the disturbance is also downstream of the IED measuring the data. One or more additional algorithms may then be used to determine the source or location of the disturbance event, and the voting aspects of the present disclosure may be performed accordingly.

[0149] In one embodiment, a method for improving fault or disturbance location detection includes capturing energy-related data in an electrical system using at least one IED, analyzing the energy-related data to identify fault or disturbance events in the electrical system, and, in response to identifying the at least one fault or disturbance event, selecting and simultaneously applying multiple fault or disturbance location detection algorithms to the energy-related data to independently confirm the location of the at least one fault or disturbance. Furthermore, the method of this embodiment includes compiling outputs of the multiple fault or disturbance location detection algorithms. The outputs include the independently confirmed locations of the at least one fault or disturbance, and weighted voting is applied to each of the outputs. The method also includes determining and providing an indication of the location of the at least one fault or disturbance based on the analysis of the compiled outputs.

[0150] Various aspects of the present disclosure may optionally utilize one or more digital or analog I / O signals to function more optimally. For example, one or more embodiments may optionally utilize digital status input signals from at least one single-phase or three-phase load (e.g., a polyphase induction motor) to simplify processing and / or enhance its analysis, evaluation, results, and / or recommendations. Alternatively, one or more embodiments may utilize analog I / O signals from the load (e.g., a polyphase induction motor) to incorporate measured temperature (i.e., from a thermocouple) into its analysis, evaluation, results, and / or recommendations. The I / O signals may be generated or utilized, as necessary, by at least one of an IED, a gateway, a software system, a cloud-based system, or other application. The I / O data may be used to indicate whether loads that may or may not generate a disturbance event are energized or de-energized. If the locations of these loads are known and the disturbance event is directly related to their energization or de-energization, this information may be used to help determine whether the source or origin of the disturbance event is upstream or downstream of one or more IEDs.

[0151] It is understood that inputs are data received by a processor and / or IED, and outputs are data sent by a processor and / or IED. Inputs and outputs can be digital or analog. Digital and analog signals can be discrete variables (e.g., two states such as high / low, one / zero, on / off. If digital, this can be a value. If analog, the presence of voltage / current can be treated as an equivalent signal by the system / IED) or continuous variables (e.g., continuous variables such as spatial position, temperature, pressure voltage, etc.). They can be digital signals (e.g., measurements in an IED from a sensor that generates digital information / values) and / or analog signals (e.g., measurements in an IED from a sensor that produces analog information / values). These digital and / or analog signals can include any processing steps within the IED (e.g., deriving active power (kW), power factor, amplitude, relative phase angle, and all derived calculations).

[0152] Processors and / or IEDs can convert / reconvert digital and analog input signals into digital representations for internal processing. Processors and / or IEDs can also be used to convert / reconvert internally processed digital signals into digital and / or analog output signals to provide an indication, action, or other response (such as an input to another processor / IED). Typical uses of digital outputs can include signaling relays to open or close circuit breakers or switches, signaling relays to start or stop motors and / or other equipment, and operating other devices and equipment that can directly interface with digital signals. Digital inputs are often used to determine the operating state / position of equipment (e.g., whether a circuit breaker is open or closed, etc.) or to read input synchronization signals from utility pulse outputs. Analog outputs can be used to provide variable control of valves, motors, heaters, or other loads / processes in energy management systems. Finally, analog inputs can be used to collect variable operating data and / or be used in proportional control schemes.

[0153] Some more examples of utilizing digital and analog I / O data can include (but are not limited to): turbine control, electroplating equipment, fermentation equipment, chemical processing equipment, telecommunications equipment, precision scaling equipment, elevators and moving walkways, compression equipment, wastewater treatment equipment, sorting and disposal equipment, electroplating equipment temperature / pressure data logging, power generation / transmission / distribution, robotics, alarm monitoring and control equipment, to name a few.

[0154] In some embodiments, the methods discussed above (and / or other systems and / or methods discussed herein) may include one or more of the following features, either independently or in combination with other features. For example, in some embodiments, energy-related signals captured by at least one IED may include at least one of the following: a voltage signal, a current signal, input / output (I / O) data, and a derived energy-related value. In some embodiments, the I / O data includes at least one of on / off state, open / closed state, high / low state, temperature, pressure, and volume. Additionally, in some embodiments, the derived energy-related value includes at least one of the following: an additional energy-related value calculated, computed, estimated, derived, formed, interpolated, extrapolated, evaluated, or otherwise determined from at least one of the voltage signal and / or the current signal. In some embodiments, the derived energy-related value includes at least one of: active power, apparent power, reactive power, energy, harmonic distortion, power factor, magnitude / direction of harmonic power, harmonic voltage, harmonic current, interharmonic current, interharmonic voltage, magnitude / direction of interharmonic power, magnitude / direction of subharmonic power, individual phase current, phase angle, impedance, sequential component, total voltage harmonic distortion, total current harmonic distortion, three-phase current, phase voltage, line voltage, and / or other similar / related parameters. In some embodiments, the derived energy-related value includes at least one energy-related characteristic, including magnitude, direction, phase angle, percentage, ratio, level, duration, associated frequency component, impedance, energy-related parameter shape, and / or decay rate. It is understood that the energy-related signal can include (or utilize) substantially any electrical parameter derived from at least one of the voltage signal and the current signal (including the voltage and current themselves), including, for example, load level and mode, as will be understood from the further discussion below.

[0155] In some embodiments, the methods discussed above (and / or other systems and / or methods discussed herein) can be implemented on at least one IED invoked in the methods discussed above (and / or other systems and / or methods discussed herein). Additionally, in some embodiments, the methods discussed above (and / or other systems and / or methods discussed herein) can be implemented partially or completely remotely from at least one IED, for example, in a gateway, field software, edge software, a remote server, or the like (which may be collectively or interchangeably referred to herein as a "head-end" system). It should be understood that "cloud-based software," "edge software," "edge systems," "management systems," "software management systems," and the like may be collectively or interchangeably referred to herein as "head-end software" for the purposes of this application. In some embodiments, at least one IED may be coupled to measure an energy-related signal, receive electrical measurement data derived from or derived from the energy-related signal at an input, and be configured to generate at least one or more outputs. These outputs may be used to identify at least one potential load type associated with at least one identified change / change in the electrical system that is characterized and / or quantified. Examples of the at least one IED may include a utility smart meter, a power quality meter, and / or another (or more) measurement device. For example, the at least one IED may include a circuit breaker, a relay, a power quality correction device, an uninterruptible power supply (UPS), a filter, and / or a variable speed drive (VSD). Additionally, in some embodiments, the at least one IED may include at least one virtual (e.g., residual energy-related signal measurement, calculation, or derivation) meter.

[0156] In some embodiments, at least one IED can continuously or semi-continuously capture and / or record energy-related signals and, in response thereto, update (e.g., evaluate / re-evaluate, prioritize / re-prioritize, track, etc.) changes / alterations identified in the energy-related signals. For example, a change / alteration can be initially identified from energy-related signals captured at a first time and can be updated or revised in response to (e.g., including or comprising) changes / alterations identified from energy-related signals captured at a second time. For example, upon identification of a change / alteration, the change / alteration can be characterized and / or quantified, information related to the characterized and / or quantified identified change / alteration can be appended to time series information associated with the energy-related data, and characteristics and / or quantities associated with the time series information can be evaluated to identify at least one potential load type associated with the characterized and / or quantified identified change / alteration. For example, the appended information can include time series information, metadata, characteristics, and / or other information related to the characterized and / or quantified identified change / alteration.

[0157] As used herein, the terms "uplink" and "downlink" (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 "uplink" and "downlink" are electrical locations relative to an IED that collects data and provides that information. For example, in an electrical system that includes multiple IEDs, one or more IEDs may be located (or installed) at an electrical location that is uplink relative to one or more other IEDs in the electrical system, and the one or more IEDs may be located (or installed) at an electrical location that is downlink relative to one or more additional IEDs in the electrical system. A first IED or load located uplink of an electrical circuit relative to a second IED or load may, for example, be located electrically closer to an input or source of the electrical system (e.g., a generator or utility power source) than the second IED or load. Conversely, a first IED or load located downlink of an electrical circuit relative to a second IED or load may be located electrically closer to the end or terminal of the electrical system than the other IED (and therefore, in this case, closer to a load or group of loads).

[0158] In an embodiment, a first IED or load that is electrically connected in parallel (e.g., on an electrical circuit) with a second IED or load can be considered to be "electrically" upstream of the second IED or load, and vice versa. In an embodiment, an algorithm for determining the direction (i.e., upstream or downstream) of a power quality event is located (or stored) in the IED, cloud, field software, gateway, etc. As an example, the IED can record voltage and current phase information of the electrical event (e.g., by sampling the corresponding signals) and communicate 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), waveform, and / or other electrical characteristics) to determine whether the source / origin of the energy-related transient (or other energy-related event) is upstream or downstream relative to where the IED is electrically coupled to the electrical system (or network).

[0159] In some embodiments, energy-related signals captured by at least one IED, or energy-related data derived therefrom, are processed on at least one of the IED, a cloud-based system, field or edge software, a gateway, and other head-end systems associated with the electrical system. In these embodiments, for example, at least one IED may be communicatively coupled to at least one of the cloud-based system, field or edge software, a gateway, and any other head-end system on which electrical measurement data is processed, analyzed, and / or displayed.

[0160] In some embodiments, data associated with energy-related data is stored (e.g., stored in a memory device of at least one device or system associated with the electrical system) and / or tracked for a predetermined time period. For example, the predetermined time period can be a user-configured time period. In some embodiments, the stored and / or tracked data includes information associated with identifying at least one potential load type. The information associated with identifying at least one potential load type can include, for example, at least one of the following: at least one identified change / alteration, at least one identified change / alteration characterized and / or quantified, time series information, and evaluated characteristics and / or quantities associated with the time series information. In some embodiments, the information associated with identifying at least one potential load type can be saved and / or tracked for future analysis / use. For example, the stored and / or tracked information can be used to generate a library of load types and associated start / run / change / stop characteristics and / or be added to a pre-existing library of load types and associated start / run / change / stop characteristics. In embodiments where there is a pre-existing library of load types and associated start / run / change / stop characteristics, the at least one potential load type identified using the systems and methods described herein can be selected from a plurality of potential load types in the pre-existing library of load types and associated start / run / change / stop characteristics.

[0161] In some embodiments, the above-described system may correspond to a control system (e.g., a control system previously discussed) for monitoring or controlling one or more parameters associated with an electrical system. As previously discussed, in some embodiments, the control system may be an instrument, an IED (e.g., at least one IED responsible for capturing energy-related signals), a programmable logic controller (PLC), head-end software (e.g., an edge software system), a cloud-based control system, a gateway, a system in which data is routed via Ethernet or some other communication system, and the like.

[0162] It is understood that the systems and methods described herein can be responsive to changes in the electrical system in which they are provided and / or implemented. For example, the comparison to at least one identified change / variation to determine whether the at least one identified change / variation satisfies a specified threshold or thresholds can be a dynamic threshold or thresholds that change in response to the change in the electrical system. For example, the change in the electrical system can be detected from energy-related signals captured by at least one IED in the electrical system. In one example embodiment, the change is detected after the system has been manually trained / taught to recognize the change. For example, specific equipment (or processes) operating at a given time can be described to allow the system to learn (i.e., a form of machine learning). In another example implementation, the change is detected by automatically identifying operating patterns using state-of-the-art machine learning algorithms (e.g., using time series clustering or using spectra or any other algorithm that facilitates analysis to identify patterns).

[0163] As will become further understood from the following discussion, the disclosed invention provides the ability to characterize voltage, current, and other derived signals (among other features) to better understand upstream and downstream loads, their operation, and impact on the electrical system. The ability to automatically evaluate energy-related data for correlation, characterization, quantification, identification, and analysis helps end users better understand the operation of their electrical systems. It can also provide more service and solution opportunities to energy-related companies (such as Schneider Electric, the assignee of the present disclosure).

[0164] It is understood that the at least one energy-related waveform capture described in conjunction with the above methods (and other methods and systems discussed below) can be associated with an energy-related signal captured or measured by at least one IED. For example, according to some embodiments of the present disclosure, at least one energy-related waveform capture can be generated from at least one energy-related signal captured or measured by at least one IED. For example, according to IEEE Standard 1057-2017, a waveform is "a representation or indication (e.g., a graph, curve, oscilloscope representation, discrete time series, equation, table of coordinates, or statistics) or visualization of [a] signal." With this definition in mind, at least one energy-related waveform can correspond to a representation or indication or visualization of at least one energy-related signal. It is understood that the above relationship is based on a definition of a waveform by a standards body (in this case, the IEEE), and that other relationships between waveforms and signals are certainly possible, as will be appreciated by one of ordinary skill in the art.

[0165] It is understood that, for example, the energy-related signal or waveform captured or measured by the at least one IED can include (or utilize) substantially any electrical parameter derived from at least one of a voltage signal and a current signal (including the voltage and current themselves). It is also understood that the energy-related signal or waveform can be continuously or semi-continuously / periodically captured / recorded and / or transmitted and / or recorded by the at least one IED. As described above, the at least one captured energy-related waveform can be analyzed (e.g., in real-time, pseudo-real-time, or historically) to determine whether the at least one captured energy-related waveform can be compressed while maintaining relevant properties for characterization, analysis, and / or other purposes.

[0166] In some embodiments, at least one IED that captures energy-related waveforms includes at least one metering device. The at least one metering device may, for example, correspond to at least one metering device in the electrical system for which the energy-related waveforms are captured / monitored.

[0167] It is understood that the terms "processor" and "controller" are sometimes used interchangeably herein. For example, a processor can be used to describe a controller. Additionally, a controller can be used to describe a processor.

[0168] As described in more detail herein, embodiments of the present disclosure may include a special-purpose computer including various computer hardware.

[0169] For illustrative purposes, programs and other executable program components may be shown as separate blocks. However, it is recognized that such programs and components reside at various times in different storage components of the computing device and are executed by the data processor of the device.

[0170] Although described in conjunction with example computing system environment, the embodiments of various aspects of the present invention are operated together with other special computing system environments or configurations. The computing system environment is not intended to imply any limitation on the scope of use of any aspect of the present invention or function. In addition, the computing system environment should not be interpreted as having any dependency or requirement related to any one or combination of the components illustrated in the example operating environment. The example of computing system, environment and / or configuration that can be applicable to various aspects of the present invention includes but is not limited to personal computers, server computers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, mobile phones, network PCs, minicomputers, mainframe computers, the distributed computing environment of any one of the above systems or devices, etc.

[0171] Embodiments of various aspects of the present disclosure may be described in the common context of data and / or processor-executable instructions, such as program modules, which are stored in one or more tangible, non-transitory storage media and executed by one or more processors or other devices. Typically, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform specific tasks or implement specific abstract data types. Various aspects of the present disclosure may also be practiced in a distributed computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed computing environment, program modules may be located in both local storage media and remote storage media, including memory storage devices.

[0172] In operation, a processor, computer, and / or server may execute processor-executable instructions, such as those described herein (eg, software, firmware, and / or hardware), to implement aspects of the present invention.

[0173] Embodiments may be implemented using processor-executable instructions. The processor-executable instructions may be organized into one or more processor-executable components or modules on a tangible processor-readable storage medium. In addition, embodiments may be implemented using any number and organization of such components or modules. For example, aspects of the present disclosure are not limited to the specific processor-executable instructions or specific components or modules illustrated in the figures and described herein. Other embodiments may include different processor-executable instructions or components having more or less functionality than illustrated and described herein.

[0174] Unless otherwise specified, the order in which the operations according to various aspects of the present disclosure are performed or executed as illustrated and described herein is not required. That is, unless otherwise specified, the operations may be performed in any order, and embodiments may include additional or fewer operations than those disclosed herein. For example, it is contemplated that it is within the scope of the present invention to perform or execute a particular operation before, simultaneously with, or after another operation.

[0175] When introducing elements of the present invention or embodiments thereof, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements.

[0176] Not all depicted components may be required as illustrated or described. Furthermore, some implementations and embodiments may include additional components. Variations in the arrangement and types of components may be made without departing from the spirit or scope of the claims as set forth herein. Additional, different, or fewer components may be provided, and components may be combined. Alternatively or additionally, a component may be implemented by several components.

[0177] The foregoing description illustrates embodiments by way of example and not limitation. This description enables those skilled in the art to make and use aspects of the present invention, and describes several embodiments, adaptations, variations, substitutions, and uses of aspects of the present invention, including those currently believed to be the best modes for practicing aspects of the present invention. In addition, it is to be understood that the application of aspects of the present invention is not limited to the details of the construction and arrangement of the components set forth in the following description or illustrated in the drawings. Aspects of the present invention can support other embodiments and be implemented or practiced in various ways. In addition, it will be understood that the words and terms used herein are for descriptive purposes and should not be considered as limiting.

[0178] It will be apparent that modifications and variations are possible without departing from the scope of the invention as defined in the appended claims. As various changes can be made in the above constructions and methods without departing from the scope of the invention, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

[0179] In view of the above, it will be seen that the several advantages of various aspects of the invention are achieved and other advantageous results attained.

[0180] The Abstract and Summary are provided to help the reader quickly ascertain the nature of the technical disclosure. They are submitted with the understanding that they will not be used to interpret or limit the scope or meaning of the claims. The Summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description. The Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the claimed subject matter.

Claims

1. A method for detecting a location of a disturbance event in an electrical system, the method comprising: acquiring, by at least one intelligent electronic device (IED) of an electrical system, an energy-related signal associated with the electrical system; processing the energy-related data to identify an occurrence of at least one disturbance event in the electrical system; applying each of a plurality of disturbance event location detection algorithms to the energy-related data in response to identifying an occurrence of the at least one disturbance event, wherein each of the applied disturbance event location detection algorithms generates an output representing an independently confirmed candidate location of the at least one disturbance event relative to the IED; as well as The outputs of the disturbance event location detection algorithms are combined to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs.

2. The method of claim 1 , further comprising applying a weighted vote to each output of the applied disturbance event location detection algorithm to generate a weighted output before combining the outputs.

3. The method according to claim 2, wherein: Combining the outputs of the disturbance event location detection algorithms includes aggregating weighted outputs of the disturbance event location detection algorithms.

4. The method according to claim 2: in, The weighted voting is implicit or explicit; optionally The weighted voting takes into account at least one of a particular algorithm used, availability of relevant data, application, customer segment, facility, load, or risk associated with the electrical system.

5. The method of claim 2, further comprising executing one or more machine learning algorithms to determine the weights of the weighted votes.

6. The method according to any one of claims 1 to 5: in, The disturbance event location detection algorithm is selected based on at least one of a specific algorithm used, availability of relevant data, application, customer segment, facility, load, or risk associated with the electrical system; optionally Among them, the disturbance event location detection algorithm is selected from the following group: simple aggregate voting; confidence-weighted voting, algorithm-weighted voting, algorithm and confidence weighting, highest confidence, best algorithm, partial weighting using machine learning, and dynamic weighting of machine learning.

7. The method according to any one of claims 1 to 6, wherein Combining the outputs of the disturbance event location detection algorithms to determine the location of the at least one disturbance event from the candidate locations includes providing a measure of confidence in the determined location.

8. The method according to any one of claims 1 to 7, wherein The energy-related data includes at least one voltage waveform capture and a current waveform capture, and further comprising pre-processing the at least one voltage waveform capture and current waveform capture to provide improved waveforms before processing the energy-related data.

9. A system for detecting a location of a disturbance event in an electrical system, the system comprising: at least one intelligent electronic device (IED) communicatively coupled to the electrical system, the IED configured to acquire energy-related signals associated with the electrical system; a processor that receives and responds to the energy-related signal acquired by the at least one IED; and a memory storing processor-executable instructions, wherein the processor-executable instructions, when executed, configure the processor to: processing the energy-related data to identify an occurrence of at least one disturbance event in the electrical system; applying each of a plurality of disturbance event location detection algorithms to the energy-related data in response to identifying an occurrence of the at least one disturbance event, wherein each of the applied disturbance event location detection algorithms generates an output representing an independently confirmed candidate location of the at least one disturbance event relative to the IED; and Outputs of the disturbance event location detection algorithms are combined to determine a location or origin of the at least one disturbance event from the candidate locations based on an analysis of the combined outputs.

10. The system according to claim 9, wherein: The processor-executable instructions, when executed, further configure the processor to: applying a weighted vote to each output of the applied disturbance event location detection algorithm to generate a weighted output before combining the outputs; Optionally The weighted outputs of the disturbance event location detection algorithms are aggregated to combine the outputs.

11. The system according to claim 10: in, The weighted voting is implicit or explicit; optionally The weighted voting takes into account at least one of a particular algorithm used, availability of relevant data, application, customer segment, facility, load, or risk associated with the electrical system.

12. The system according to claim 10, wherein: The processor-executable instructions, when executed, further configure the processor to perform one or more machine learning algorithms to determine the weights of the weighted votes.

13. The system according to any one of claims 9 to 12: in, The disturbance event location detection algorithm is selected based on at least one of a specific algorithm used, availability of relevant data, application, customer segment, facility, load, or risk associated with the electrical system; optionally Among them, the disturbance event location detection algorithm is selected from the following group: simple aggregate voting; confidence-weighted voting, algorithm-weighted voting, algorithm and confidence weighting, highest confidence, best algorithm, partial weighting using machine learning, and dynamic weighting of machine learning.

14. The system according to any one of claims 9 to 13, wherein: The processor-executable instructions, when executed, further configure the processor to provide a measure of confidence in the determined location of the at least one disturbance event.

15. The system according to any one of claims 9 to 14, wherein: The energy-related data includes at least one voltage waveform capture and a current waveform capture, and wherein the processor-executable instructions, when executed, further configure the processor to pre-process the at least one voltage waveform capture and current waveform capture to provide improved waveforms prior to processing the energy-related data.

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