Reducing search area for event disturbance location in electrical power delivery systems

Abstract

A waveform measurement unit (WMU) data analysis system locates a source of an electrical disturbance event in an electrical power delivery system. The system receives monitoring data including sampled waveform signals and event-driven status reports, determines an initial search region based on a first electrical disturbance attribute, and reduces the search region by eliminating circuit branches inconsistent with a second electrical disturbance attribute. A geographic information system (GIS)-based map is then generated to present the refined search region and guide a user to the source of the electrical disturbance event.

Description

Docket No. 503461.70081REDUCING SEARCH AREA FOR EVENT DISTURBANCE LOCATION IN ELECTRICAL POWER DELIVERY SYSTEMSRELATED APPLICATION

[0001] This application claims priority benefit of U.S. Provisional Patent Application No.63 / 777,617, filed March 25, 2025, which is hereby incorporated by reference in its entirety.TECHNICAL FIELD

[0002] This disclosure relates generally to electrical power delivery system diagnostics and, more particularly, to electric disturbance detection and localization in electrical grids.BACKGROUND INFORMATION

[0003] In the IEEE Power & Energy Magazine, September / October 2023 edition (Vol. 21, No. 5), an article starting on page 68 by Hamed Mohsenian-Rad, Ph.D., and Wilsun Xu, Ph.D., titled “Synchro-Waveforms,” describes a power system measurement technology approach. This technology, referred to as a waveform measurement unit (WMU), obtains time-synchronized waveform measurements (i.e., synchro-waveforms) from different locations within a power system. WMUs can capture inconspicuous disturbances that are often overlooked by other types of time-synchronized sensors, such as phasor measurement units (PMUs). WMUs also monitor system dynamics at much higher frequencies, as well as much lower frequencies, than the fundamental components of voltage and current that are commonly monitored by PMUs.

[0004] Another paper by V. Barrera Nunez et al., titled “Feature Analysis and Classification Methodology for Overhead Distribution Fault Events,” presented in the IEEE / PES Transmission and Distribution Committee on July 25-29, 2010, describes the use of waveform data from power quality monitors (PQM) installed in substations. The paper presents an analysis of unique features and a classification method for identifying the root cause of overhead distribution fault events, which are multi-cycle events. In particular, it focuses on faults caused by animal contacts, tree contacts, and lightning-induced disturbances.

[0005] Additionally, there are a number of patents describing the calculation of fault locations. FIG. 1, for example, shows an example set of electrical schematics and waveform diagrams arranged in a sequence for representing a SLG (single line-to-ground) incipient fault location estimation. The estimation technique is described in U.S. Patent No.8,941,387 of Kim. The ’387 patent describes a time-domain analysis for determining netDocket No. 503461.70081fault electrical parameters, including inductive reactance. Conventional fault-location practices, however, often involve extensive line patrols and testing, which can prolong restoration times and risk secondary faults.SUMMARY OF THE DISCLOSURE

[0006] This disclosure describes techniques for significantly reducing the search area in which to locate electrical disturbance events, including temporary and sustained faults, as well as power quality (PQ) anomalies, in electrical power distribution systems. By correlating event metadata with known system topology, protection device data, and waveform-derived characteristics, the system iteratively eliminates branches that could not have been the source of the event. This process significantly reduces the search area for field crews, increases the accuracy of outage response and predictive maintenance, and refines fault location estimation.

[0007] The disclosed techniques analyze both continuously sampled waveform data (which captures early-stage fault signatures) and event-driven reports from protective devices (such as reclosers or fault circuit indicators). In some embodiments, WMU data is event-triggered, meaning waveform signals are only captured when a detection threshold, anomaly detection, or external triggering condition occurs.

[0008] When a grid disturbance event occurs, various sensors and protection devices report event-related data, which can be used to refine fault location. The system determines an initial search region, consisting of circuit branches that could have contributed to the event, based on a first electrical disturbance attribute such as phase, reactance, or device response.

[0009] To further refine the fault location, the system iteratively eliminates circuit branches that do not exhibit a second electrical disturbance attribute. The refinement process incorporates alarm data from devices such as fault circuit indicators (FCIs), fault passage indicators (FPIs), advanced fault sensors (AFSs), advanced metering infrastructure (AMI), reclosers, and other protection devices, as well as knowledge of protection device types, fault phases, and overhead (OH) versus underground (UG) line characteristics. For instance, if a PQ disturbance includes a voltage sag and current swell, the system excludes branches where measured reactance values would not have produced such behavior.Similarly, if a recloser detected the fault downstream but a fault circuit indicator (FCI) upstream did not, the search region is constrained to sections between those devices.

[0010] Although many examples herein relate to outages, temporary faults, or sustained faults, the same branch-elimination process may also be applied to non-outage PQ disturbances when waveform-derived metadata and device-generated event data areDocket No. 503461.70081sufficient to constrain a source region within the electrical power delivery system. For example, a voltage sag, current swell, transient, or other PQ disturbance that does not result in a sustained outage may still be characterized by phase information, reactance-related location information, alarm information, protection-device information, or overhead-versus-underground indicators. In such cases, the system may use those attributes in the same manner described herein to eliminate circuit branches that are inconsistent with the detected disturbance, thereby reducing the search area for the source of the non-outage PQ disturbance.

[0011] The GIS-based mapping system visually presents the refined fault search region, distinguishing eliminated branches to assist field crews in locating and repairing the fault efficiently. The GIS-based maps are generated by integrating distribution network models from Synergi Electric or CYME Power Engineering Software with open-source geospatial data (such as OpenStreetMap). These network models provide GPS-based location data for substations, feeders, transformers, protection devices, and other components, as well as reactance values and electrical connectivity details. The GIS-based system dynamically updates to reflect refined fault locations, allowing users to see excluded branches in realtime.

[0012] Additionally, tolerance factors may be applied at any stage to adjust the refined search region based on uncertainty in disturbance location estimation. Because the disclosed techniques integrate multiple data sources with intelligent analysis, they enable utilities to pinpoint fault areas more accurately by focusing on relevant circuit branches rather than scanning the entire system.

[0013] Through this targeted approach, utilities can rapidly diagnose and repair sustained and temporary faults, reducing system downtime and improving overall grid reliability. These techniques ultimately minimize outage durations, enhance predictive maintenance, and improve restoration efficiency. Additionally, because the disclosed system leverages existing data sources (rather than requiring specialized equipment), it provides a cost-effective, scalable approach to disturbance detection and localization.

[0014] In some embodiments, a waveform measurement unit (WMU) data analysis system locates a source of an electrical disturbance event in an electrical power delivery system by receiving monitoring data that includes WMU data and event monitoring data indicative of the electrical disturbance event. The WMU data may include sampled waveform signals, and the event monitoring data may include a discrete, event-driven status report triggered in response to the electrical disturbance event, where the electrical disturbance event isDocket No. 503461.70081characterized by a set of electrical disturbance attributes. The system may determine, based on a first electrical disturbance attribute of the set of electrical disturbance attributes, an initial set of circuit branches representing an initial search region for the source of the electrical disturbance event, and may reduce a size of the initial search region by eliminating any circuit branch that is inconsistent with a second electrical disturbance attribute from the set of electrical disturbance attributes, thereby establishing a refined search region. A geographic information system (GlS)-based map may then be generated to present circuit branches of the refined search region and guide a user to the location of the source of the electrical disturbance event. In some embodiments, the reducing step is iteratively performed for additional electrical disturbance attributes to progressively refine the search region. In some embodiments, the event monitoring data includes alarm data from a fault detection device, and circuit branches may be eliminated based on inconsistency with such alarm data, including by giving greater weight to a positive alarm indication than to an absence of alarm indication. In some embodiments, circuit branches may also be eliminated based on protection-device information, detected disturbed phase information, overhead-versus-underground information, or a tolerance factor associated with event-disruption location uncertainty. In some embodiments, the first electrical disturbance attribute comprises a reactance-based location value derived from the sampled waveform signals, and the initial set of circuit branches is identified from a circuit model as having modeled reactance values consistent with the reactance-based location value. In some embodiments, the GIS-based map displays eliminated circuit branches as visually distinct from circuit branches of the refined search region, optionally using color-coded segments corresponding to exclusion criteria. In some embodiments, the electrical disturbance event is a sustained fault, a temporary fault, or a precursor event, and the system may also identify a set of temporary and sustained electrical disturbances that share a common set of electrical disturbance attributes, including both waveform-based parameters and operational or alarmbased parameters.

[0015] In some embodiments, the disclosed subject matter also includes a data analysis system having a processor and a non-transitory computer-readable medium storing a circuit model of the electrical power delivery system and instructions that, when executed by the processor, cause the system to perform the branch-elimination and GIS-mapping operations described herein. The circuit model may include a circuit connectivity model identifying branch connectivity, modeled reactance values, and locations of protection devices in the electrical power delivery system. In some embodiments, the instructions further cause the system to render eliminated circuit branches differently from circuit branches of the refinedDocket No. 503461.70081search region in the GIS-based map, and the WMU data analysis system may be implemented in a cloud computing environment or at an edge location associated with a substation. In further embodiments, a non-transitory computer-readable medium stores instructions that, when executed by a data analysis system, cause the data analysis system to receive the monitoring data, determine the initial search region, reduce the initial search region by eliminating inconsistent circuit branches, and generate the GIS-based map, optionally including application of a tolerance factor and iterative elimination using additional electrical disturbance attributes. In still further embodiments, the system may include a user interface configured to display the GIS-based map, the initial search region, and the refined search region, with circuit branches eliminated based on alarm data, phase information, or overhead-versus-underground classification visually distinguished on the GIS-based map, and with locations of substations, protection devices, and fault detection devices displayed to assist a user.

[0016] Additional aspects and advantages will become apparent from the following detailed description of various embodiments, taken in conjunction with the accompanying drawings.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0017] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0018] FIG. l is a set of electrical schematic and waveform diagrams arranged in a sequence for representing a SLG (single line-to-ground) incipient electrical disturbance event location estimation technique in accordance with the prior art.

[0019] FIG. 2 is a block diagram of a WMU network architecture in accordance with one embodiment.

[0020] FIG. 3 is a block diagram of an electrical power delivery system, according to one embodiment.

[0021] FIG. 4 are waveform samples showing examples of potentially matching power quality events.

[0022] FIG. 5 is a screen capture of an example user interface for reviewing power quality events in accordance with one embodiment.

[0023] FIG. 6 is another screen capture of the user interface of FIG. 5 showing a detailed view of a power quality event in accordance with one embodimentDocket No. 503461.70081

[0024] FIG. 7 is a flow diagram of a process for locating electrical disturbances in an electrical power delivery system in accordance with one embodiment.

[0025] FIG. 8 is a set of sampled waveform signals that exhibit multiple temporary electrical disturbances and a sustained electrical disturbance in accordance with one embodiment.

[0026] FIG. 9-FIG. 14 are each screenshots of geographic information system (GlS)-based maps showing an example analysis of the data of FIG. 8 for locating electrical disturbances in an electrical power delivery system in accordance with one embodiment.DETAILED DESCRIPTION OF EMBODIMENTS

[0027] This disclosure describes techniques for monitoring, analyzing, and responding to grid disturbances in electrical power delivery systems. These disturbances include faults, which may be either temporary or sustained, as well as other power quality (PQ) events that indicate developing issues in the system.

[0028] A temporary fault is a disturbance that triggers a protective mechanism, such as a recloser operation, but subsequently clears on its own. Because the underlying cause, such as a momentary line contact or minor insulation breakdown, is transient, the network can self-recover and continue operating. In contrast, a sustained fault occurs when the protective mechanism remains open until the fault is physically removed or repaired, leading to an outage or service disruption. Identifying and addressing temporary faults before they develop into sustained faults is helpful for maintaining system reliability.

[0029] Some temporary faults occur as precursors to more serious faults and outages. These incipient faults or precursor events do not immediately cause service interruptions but can degrade system conditions over time. Examples of incipient faults include early-stage insulation breakdown in cables or intermittent contact caused by vegetation. Detecting and mitigating these precursors is an important part of predictive maintenance, helping prevent minor issues from escalating into major failures.

[0030] Beyond faults, other PQ events represent grid disturbances that can affect power system stability and performance. These include voltage sags / swells, transients, harmonics, and frequency variations. While these PQ events may not always cause outages, they can be indicative of underlying problems in the network. In many cases, faults exhibit characteristic power quality signatures, such as current swells and voltage sags, making PQ analysis another tool for fault detection and classification. For example, a voltage sag may result from a short but severe fault that quickly clears, yet it still signals a vulnerableDocket No. 503461.70081segment of the network. Similarly, a transient voltage spike may indicate a momentary fault that did not escalate into a sustained outage but still represents an operational concern.

[0031] As used herein, the term electrical disturbance attributes refers to one or more characteristics associated with an electrical disturbance event that may be used to identify, classify, group, localize, or exclude possible source regions for the event. Electrical disturbance attributes may include, for example, waveform-derived electrical parameters (such as phase, reactance, current magnitude, voltage magnitude, duration, point on wave, or energy), device-generated event reports (such as alarms, non-alarms, triggered-device indications, or status reports), circuit-model attributes (such as circuit topology, branch connectivity, modeled reactance, conductor configuration, or substation-relative position), device-type attributes (such as presence or expected operation of a fuse, recloser, automated line switch, or other protection device), line-placement attributes (such as whether a segment is overhead or underground), and inferred equipment-type attributes (such as predicted damaged equipment type, likely faulted component category, or likely cause classification). In some embodiments, a given electrical disturbance event may be characterized by a set of electrical disturbance attributes that includes one or more attributes from different categories.

[0032] FIG. 2 shows a WMU network architecture 200 for monitoring and analyzing electrical disturbances within a power system, such as an electrical power delivery system 300 (FIG. 3). According to some embodiments, WMU network architecture 200 integrates subcomponents provided by a power transmission and distribution (T&D) company with infrastructure and software available from the applicant for this disclosure, Toumetis, Inc. of Boise, Idaho. This integrated system allows for comprehensive monitoring and analysis of the power grid, aiming to quickly and efficiently detect and respond to electrical disturbances.

[0033] The main components shown in the example of FIG. 2 are a substation 202 and a WMU data analysis system 204, which are communicatively coupled via an internet connection 206. Internet connection 206 is available at substation 202 using cellular connection equipment 208 (e.g., LTE or 5G), fiber, or other communication devices. In some embodiments, WMU data analysis system 204 is a remotely located server (e.g., AWS server) such that the analysis is performed via cloud computing. In other embodiments, WMU data analysis system 204 may be located at an edge, such as within substation 202.

[0034] In this example, substation 202 serves as the physical location where power system monitoring occurs. It contains equipment for controlling and monitoring the electricity flowDocket No. 503461.70081in the power grid. Substation 202 also includes an internal LAN 210, which is communicatively coupled to cellular connection equipment 208 via wireless or ethernet connection 212 with a router 214.

[0035] LAN 210 interconnects all the monitoring and computing devices located in LAN 210. For instance, a substation server 216 includes local PC 218 having an optional storage interface 220 for optional removable storage 222 and a wireless or wired router 214 connected via router interface 224 to local PC 218. Local PC 218 is configured with software processes 226 including a process 228 to get FTP data from devices and a process 230 push data to storage 232 in connection with cloud storage and processing system 234.

[0036] Cloud storage and processing system 234 is configured to read instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium), including storage 232, and perform any one or more of the methods discussed herein (or portions thereof), such as discussed for process 700 (FIG. 7), or any portions of these embodiments. Additional details are described later with reference to FIG.7-FIG. 13.

[0037] Router 214 also connects to multiple WMU devices 236 via server connection 238. Multiple WMU devices 236 include monitoring device 240, which samples synchrowaveform data and provides it via an FTP interface 242 for process 228.

[0038] FIG. 3 illustrates an electrical power delivery system 300 monitored by WMU synchro-waveform event analysis 302. This monitoring and analysis capability uses continuously sampled voltage and current waveforms to detect, classify, and locate electrical disturbances in electrical power delivery system 300, facilitating prompt responses to both temporary and sustained outages. For example, the system may classify various electrical disturbance types, such as a tee connector fault, load break elbow fault, cable-to-ground fault, capacitor fault, or transformer fault, but other electrical disturbance scenarios are possible as well.

[0039] Electrical power delivery system 300 includes a power generation plant 304 (e.g., a hydroelectric dam, wind farm, or thermal power plant), a step-up transmission substation 306, high-voltage transmission lines 308, a step-down transmission substation 310, a stepdown distribution substation 312, distribution network poles 314, and end users 316 (such as residential or commercial customers).

[0040] Within this system, WMU synchro-waveform event analysis 302 supports electrical disturbance location estimation 318 by receiving and processing time-domain waveforms measured throughout the grid. Specifically, FIG. 3 shows a time-domain waveform 320Docket No. 503461.70081exhibiting a temporary electrical disturbance event 322. The sampled voltage and current measurements capture an event lasting less than a full cycle, which appears as a distinct spike or transient in the overlapping phase waveforms. By applying an analysis to this measurement data, the system produces electrical disturbance location estimation 318. Here, the location of the event is determined to lie on distribution line 324 between the step-down distribution substation 312 and the distribution network poles 314 or feeder lines 326.

[0041] Time-domain waveform 320 includes three overlapping waveforms, corresponding to multiple phase views of the electrical system. The transient voltage spikes on all three waveforms mark the precise, sub-cycle interval at which temporary electrical disturbance event 322 occurred. Additionally, techniques described in International Patent Application No. PCT / US2025 / 030681, titled “Phasor-Based Fault Location Estimation in Electrical Power Delivery System,” and incorporated by reference herein in its entirety, can be used in conjunction with the present disclosure. These techniques support multi-cycle or sub-cycle event disturbance analysis, measuring waveform behavior at sub-cycle intervals to deliver high-resolution event disturbance detection and location estimation.

[0042] By integrating both continuous waveform measurements and discrete event disturbance monitoring inputs (e.g., event-driven alarms), the WMU synchro-waveform event analysis 302 can further pinpoint the source of an outage or transient event within electrical power delivery system 300 with greater speed and accuracy, thereby reducing system downtime and improving reliability.

[0043] FIG. 4 illustrates how multiple transient or temporary events can be grouped together with a sustained event disturbance based on a common set of event disturbance attributes, including waveform characteristics, location estimates, phases, and timing. Such grouping techniques are described in greater detail in International Patent Application No. PCT / US2025 / 042658, titled “Grouping Electrical Disturbance Events.” As shown, waveform samples 402 exhibit various PQ events, each of which may represent a momentary event disturbance.

[0044] In this example, a first event 404 occurs on a first phase, while a second event 406 and a third event 408 occur on a second phase. For the system to conclude that these separate events share the same underlying cause (or belong to the same group) they would typically be detected on the same circuit, involve the same single-phase or multi-phase configuration, align closely in location estimates (e.g., as derived through WMU-based waveform analysis described in the ’681 application), and occur within a specified time interval prior to an outage. Other optional criteria may also be considered, such as device orDocket No. 503461.70081alarm data that confirm or refute whether multiple events stem from the same event disturbance source.

[0045] When a sustained outage occurs, utility processes capture metadata concerning the event disturbance, including the physical location of damage, the device types involved, and possible causes (e.g., vegetation contact or equipment failure). Some precursor or transient events result in system interruptions that are momentary (five minutes or less, as specified in IEEE 1366) but do not provide clear cause information. If these short-duration outages repeatedly clear without physical intervention, the underlying cause may remain unresolved, potentially leading to larger or more frequent sustained outages later.

[0046] By grouping transient events with the subsequent sustained outage under a common set of attributes, the system can link metadata otherwise available only for the outage with the prior “unlabeled” precursors. This linkage expands the historical record and helps classify newly observed transient events according to the same attributes (such as phase, circuit location, timing, or alarm data). Engineers can then use these enriched event labels to train and refine physics-based, rule-based, or machine-learning models aimed at predicting, locating, and preventing more serious outages.

[0047] To monitor and view power quality events and automated inferences, FIG. 5 and FIG. 6 show a set of screenshots for a power event monitoring software application, referred to as Cascadence. Cascadence uses machine learning to derive analytic insights and predictions from PQ meters, other data sources, outage management system (OMS) or (Advanced) distribution management system (DMS / ADMS), and weather data from across the grid. Those predictions inform operations that specific equipment is failing so proactive maintenance / replacement can be carried out and customer outage time is reduced and satisfaction improved. Cascadence feeds that data into its machine learning algorithms (see, e.g., FIG. 15 of the ’681 application) in order to automatically perform the following tasks: event type classification (i.e., correlate power quality events to device types), and physicsbased incipient event disturbance location (i.e., determine the position of the failing equipment).

[0048] In this example, the Cascadence application presents to a user an event overview timeline user interface 500 (FIG. 5). Event overview timeline user interface 500 is accessible via an overview tab 502 and shows several timelines on which events are depicted. The timelines include a precursor timeline 504 and an outage timeline 506 for each day. An individual day can be selected and shown in a detailed view 508. A time slot on the timelines is selectable to view in greater detail particular events occurring in thatDocket No. 503461.70081time slot. In this example, outages 510 are selected so that specific information about an outage event 512 can be viewed.

[0049] FIG. 6 shows original signal user interface 600 for viewing the WMU data of a particular March 28, 2024 precursor event 602. In this example, event 602 is selected to show its original three-phase voltage signal waveforms 604, an original three-phase current signal waveforms 606, a reactance to electrical disturbance 608 (i.e., calculated as described in the ’681 application), and inferences 610 that were determined using machine learning or Al processes. For instance, inferences 610 include an event persistence type 612 (shown as a precursor event) and an electrical disturbance type 614 that is shown as a line-to-ground (LG) event.

[0050] Event persistence type 612 includes precursor events, outages, and operational issues (e.g., schedule maintenance). This term conveys the various ongoing or transient states of events in the context of a power system, i.e., whether the events are persistent (ongoing or long-lasting in the case of planned outages) or transient (temporary or shortlived), which can be crucial for understanding and managing their impact on the power system.

[0051] Electrical disturbance type 614 includes single line-to-ground (LG) electrical disturbances, line-to-line (LL) electrical disturbances, line-to-line-to-ground (LLG) electrical disturbances, and three-line electrical disturbances (LLL).

[0052] In some embodiments, events can also be auto-labeled based on additional inferences. For example, labelling 616 includes a cause 618, a device 620, and line placement 622 (OH or UG).

[0053] Causes 618 indicate the cause of the event and can include any of the following: cap switch, cap switch re-strike, cap switch stuck contact, customer problem, deenergized for safety, dig in, equipment failed / damage, faulty meter, fire, foreign object in lines, improper installation, lightning, loose connection, meter power supply, non-standard construction, other, overloaded normal conditions, planned outage, power electronics switching, restoration switching event, severe weather, snapshot, switch (load off), switch (load on), switching error, switching event, sympathetic event, timing pulse, transformer inrush current, transmission event, tree / limb preventable, tree / limb unpreventable, under investigation, unknown, and vehicle contact.

[0054] Device 620 indicates an electrical component that is determined to be involved with the event. Example electrical components include: conductor (OH), connector (OH), cross arm, customer equipment, cut out, disconnect, disconnect switch, down guy, elbow (L.B.),Docket No. 503461.70081elbow (N.L.B.), electronic sectionalizer, feed through, fuse, fuse cabinet, fuse switch, ground bank, handhole, hotline clamp, insulator / pin, jumper, meter, miscellaneous substation, other equipment, pole, polymer arrester, pothead, probes, RA switch, RC-off (distribution only), recloser, regulator, relay, splice, step down transformer, switch, and T-connector.

[0055] In the example of FIG. 6, event 602 is captured by a PQ meter (or equivalent device) and classified by Cascadence and its underlying machine learning algorithms: event type precursor, electrical disturbance type LG, phases A and then the location algorithm provides a cumulative reactance to the electrical disturbance of 10.104 ohms.

[0056] FIG. 7 depicts a process 700 for refining an electrical disturbance location estimate. Process 700 begins by using a location algorithm to produce an initial search area 702. This step serves as the starting point based on reactance to the electrical disturbance. The subsequent steps apply additional information in an aggregate refinement stage 704 to exclude certain branches from the calculated search area on the map. Aggregate refinement stage 704 includes a plurality of sub-checks 706-714, each of which contributes to reducing the initial search area based on a different category of event metadata or circuit-model information. In general, to resolve conflicts or ambiguities, the checks with the highest level of confidence are applied first, and their order or optionality depends on the known data quality. In the illustrated embodiment, sub-check 706 corresponds to alarm-data refinement, sub-check 708 corresponds to protection-device refinement, sub-check 710 corresponds to phase-based refinement, sub-check 712 corresponds to overhead / underground refinement, and sub-check 714 corresponds to damaged-equipment refinement.

[0057] At any stage in the following checks, tolerance factors may be applied to account for potential inaccuracies in the location algorithm, and a circuit connectivity model (i.e., a graph-based representation of the network) is used to focus on relevant branches within the search region. The overall approach of process 700 reflects a principle of applying the most confident checks first, then sequentially narrowing the search area as new information becomes available.

[0058] Next, alarm data 706 from devices such as FCIs, FPIs, AFSs, or AMI smart meters are integrated to further refine the electrical disturbance area. Although these alarms can exhibit ambiguities (some devices may have been offline during the event, and some are more sensitive than others) alarm data 706 receives relatively high precedence because these devices typically occupy strategic positions in the circuit for sectionalizing purposes. Given these potential ambiguities, a positive alarm (i.e., confirmation that a device did detect anDocket No. 503461.70081electrical disturbance) is prioritized over a negative alarm (i.e., lack of an alarm does not necessarily mean the electrical disturbance never passed that device).

[0059] Next, protection devices 708 are considered so as to narrow the search area by leveraging knowledge of protective equipment types (e.g., automated line switches, fuses, or the like) and their operational waveforms. For instance, when the electrical disturbance is determined to be attributed to a fuse, circuit branches lacking this device may be excluded from the search area. This step has a lower priority because of uncertainties about whether a specific device actually operated and concerns regarding the consistency of device operations.

[0060] More generally, the protection-device information used in this refinement step may indicate a device type associated with the electrical disturbance event, such as a fuse, recloser, automated line switch, sectionalizer, circuit breaker, or another protection device represented in the circuit model. In such embodiments, circuit branches that do not include protection devices of the indicated type, or that are otherwise inconsistent with an expected response of that device type to the electrical disturbance event, may be excluded from the search area. For example, a branch may be excluded when the branch lacks the indicated protection device type, lies beyond a boundary established by that device type, or would be expected to exhibit a different device response than the response reflected in the event monitoring data.

[0061] Process 700 then entails an electrical disturbances phase analysis 710, the result of which excludes portions of the circuit that do not carry the identified electrically disturbed phase or have a different set of conductors. Although phase-identification errors can occur due to inaccuracies in the circuit model or the waveform monitor’s phase mapping, this check usually has high precedence because the electrically disturbed phase is generally evident from the waveform. In cases where phase information has proven incorrect, the system revisits the circuit model once an outage location is found.

[0062] Next, overhead / underground (OH / UG) line differentiation 712 is used to further restrict the search area based on waveform characteristics. The classification model used here determines whether an electrical disturbance likely originated in an underground or overhead section. In general, there are multiple types of data that are provided to a model so as to generate a probability that a fault is overhead or underground. Multi-phase events typically lend greater confidence to an overhead origin (e.g., where a tree knocks down multiple lines), whereas single-phase to ground events reverse that confidence and suggest an underground fault. Furthermore, to classify an overhead fault, Toumetis, Inc. hasDocket No. 503461.70081developed a vegetation contact model described in a forthcoming International Patent Application titled, “Analyzing Pre-Fault WMU Current Data to Detect Vegetation Causing Faults in Electrical Power Delivery System” (Attorney Docket No. 503461.70051).Similarly, to classify an underground fault, Toumetis, Inc. has developed a connector model described in U.S. Patent Application No. 63 / 823,358 titled, “Identifying Fault Types in Electrical Power Delivery Systems Using Shapelet-Based Classification” (Attorney Docket No. 503461.70070). Another type of data used for classification is what percentage of overhead vs. underground lines in the circuit model are within the reactance-to-fault calculation, which is used to estimate a likelihood of the source of the disturbance being at an UG or OH segment. For instance, if 90% of the lines are underground, then there is a 90% probability the fault type is UG.

[0063] Finally, process 700 then compares damaged equipment identification 714, wherein waveform analysis can suggest a specific failed component. As the accuracy of classification models improve with additional data, they may appear higher in the sequence. Models used for identifying damaged equipment include those described in the aforementioned connector model patent application for specifying a failing component type. U.S. Patent Application No. 63 / 934,295 titled, “Protection Device Operation Validation, Coordination, and Reconfiguration for Electrical Power Delivery System” (Attorney Docket No. 503461.70060) describes an ability to detect failing protection devices is titled. Other models include stuck capacitors and switches, or other damaged equipment.

[0064] FIG. 8 shows waveform data of PQ events for a circuit identified as “Avocado 810061.” In this example, the system classified the electrical disturbance as a single-phase (A-phase) LG disturbance, based on waveform characteristics gathered over nine days. Each event reflects a corrected reactance value of approximately 7.52 ohms. Specifically, a WMU data analysis system instance recorded nine temporary electrical disturbance events (sometimes called “precursors”), each with a “base reactance” of about 9.3 Q. An average “reactance adjustment” of -1.78 Q is applied to these base values to align the estimates with other sources (i.e., circuit models), resulting in a final “corrected” cumulative reactance of approximately 7.52 Q. Vegetation on OH lines is indicated as the likely cause, consistent with the classification of “OH Vegetation.” The outage cause was attributed to vines or grass contacting equipment, and the interruption (“Ticket Type: TX”) lasted until the clearing device (with a reactance of 7.68 Q) isolated the electrical disturbance. Example clearing devices include a fuse, recloser, circuit breaker, or some other protective component. In typical distribution systems, any protective device (e.g., fuse, recloser, orDocket No. 503461.70081circuit breaker) that operates to interrupt current flow and isolate an electrical disturbance is often referred to as a “clearing device.”

[0065] Events shown in FIG. 8 were grouped by their matching phase, electrical disturbance type, location estimate (based on cumulative reactance), and electrical disturbance current magnitude. By grouping events, the system checks whether an outage waveform matches the earlier “precursor” events identified through machine learning analyses. This includes verifying that key waveform attributes (such as duration, point on wave, and overall energy) closely resemble those of the predicted electrical disturbance signature.

[0066] As noted in FIG. 7, the system also cross-references any alarm or non-alarm indications from FCIs to ensure these do not contradict the predicted location. If no discrepancies arise between the predicted and observed data, the system confirms that the electrical disturbance occurred in the anticipated area. In the example of FIG. 8, during the outage, no recloser operations were recorded, suggesting the electrical disturbance was cleared through other protective means or device configurations. No combination of FCI alarms contradicted the prediction, and waveform properties (such as duration, point on wave, and total electrical disturbance energy) indicated a high correlation between precursor signals and the actual outage signature. Accordingly, a direct comparison of the predicted electrical disturbance characteristics with actual field data validated that both the location and phase were consistent with the system’s forecast.

[0067] In instances where the system leverages algorithms first trained in one utility’s environment and then applied to another’s data, it monitors for consistency in how waveform patterns, classification labels, and field observations align, thereby validating the portability and reliability of the machine learning-driven predictions. Thus, FIG. 8 also highlights how features developed through machine-learning algorithms originally trained in one utility environment can be adapted to another, confirming the cross-utility portability of these predictive methods.

[0068] Ultimately, FIG. 8 underscores the method’s ability to match precursors with real-world outages by leveraging aligned waveforms, confirmed device data, and robust machine learning-driven classification. In other embodiments, external environmental data (e.g., weather, geographic markers) may also be integrated to refine the location estimate.

[0069] FIG. 9-FIG. 14 show GIS-based maps of the circuit in the Avocado 810061, mentioned above. Specifically, FIG. 9-FIG. 13 shows how branches of the circuit areDocket No. 503461.70081dropped to narrow down the location of the electrical disturbance. FIG. 14 then shows another resulting report.

[0070] In some embodiments, the GIS-based maps are generated by integrating distribution network models from Synergi Electric or CYME Power Engineering Software with open-source geospatial data, such as OpenStreetMap. These network models contain GPS-based location data for substations, feeders, transformers, protection devices, and other components, as well as reactance values and electrical connectivity details. To generate the maps, the system first extracts the network topology and reactance data from Synergi or CYME and overlays this structured network model onto an open-source GIS base map. The GIS base map provides real-world geographical context, including roads, rivers, terrain, and urban infrastructure, which aids field crews in efficiently locating electrically disturbed sections.

[0071] Map icons provide a quick visual reference to operational statuses and network components, reflecting how the system integrates both physical and event-driven data for more effective event disturbance-locating. For instance, in this example, the mapping interface incorporates icons and various line styles to illustrate the physical locations and operational statuses within the power distribution network. A first pin icon signifies the location of the electrical disturbance. The substation, acting as the circuit’s primary power source, is denoted with a second pin icon, providing a key reference point for measuring distances or reactances. A first device icon style represent reclosers or FCIs that did not trigger during the event, indicating that the event disturbance likely did not occur downstream of those particular devices. In contrast, a second device icon style (FIG. 10) mark reclosers or FCIs that did register or alarm, confirming that the event disturbance passed through those points. Box-shaped icons represent fuses. Dot-shaped icons represent automated line switches (ALS), which is an automated switch capable of sectionalizing that portion of the feeder and helping isolate electrically disturbed segments more quickly.

[0072] The maps also annotate the segments with solid or dashed lines. Solid lines indicate overhead (OH) segments of the circuit, which may be more susceptible to external factors like vegetation or weather, while dashed lines represent underground (UG) sections, where different reactance profiles or attenuation factors may apply. A first set of line segments outlines the calculated search area based on reactance, often bounded by a tolerance margin to account for measurement uncertainties; this helps crews prioritize specific branches or sections within the highlighted zone for a faster and more precise electrical disturbance location process. A second set of line segments indicates sections outside of the search area. A third set of line segments represents sections that were originally within the search areaDocket No. 503461.70081but were eliminated through refinement. Similarly, a fourth set of line segments represents sections that were eliminated because they do not carry the electrically disturbed phase. The different sets of line segments are visually distinguished in the figures according to their respective statuses.

[0073] With reference to FIG. 9, for purposes of the experiment, alarms from FCIs and AFSs are assumed to be generated and transmitted correctly regardless of how long the electrical disturbance lasts. In other words, an AFS either triggers an alarm or it is known that an electrical disturbance has passed by it based on the waveform it captures.Furthermore, an electrical disturbance location tolerance of ±0.5 miles is used. To narrow the search area, information from electrical components (e.g., FCIs and AFSs) is analyzed. As indicated previously with reference to FIG. 7, information such as a known protection device type is considered, the electrically disturbed phase is identified, and the system determines whether the line is OH or UG. As a hypothetical approach, the location tolerance could also be reduced further.

[0074] FIG. 9 presents an initial or “raw” location estimate labeled as “Xl cmltv,” indicating a cumulative reactance of the electrical disturbance-location algorithm before any additional data refinement. Reactance is typically modeled in individual segments, so cumulative reactance is the total from each segment all the way to the substation or other known point. According to this estimate, the system projects that the electrical disturbance could lie within a fourteen-mile expanse, which corresponds to approximately twenty-four percent of the circuit’s total length. The electrical disturbance location is represented by the highlighted line segments in FIG. 9, which are within ±0.5 miles of the estimated electrical disturbance location (estimated based on cumulative reactance).

[0075] The red lines need not be continuous; multiple branches in the model may have a reactance that matches the calculated cumulative reactance. For instance, a first location 902 that includes UG sections is on a branch that is spaced apart from a second location 904 that include OH lines.

[0076] Notably, FIG. 9 shows a broad area that results from the base calculation of reactance and waveform parameters in isolation, prior to integrating auxiliary data sources such as FCI alarms, protective device information, or phase-specific details. The system’s first-pass estimation often yields a more extensive region of uncertainty, laying the groundwork for subsequent narrowing steps that leverage additional inputs.

[0077] FIG. 10 illustrates how the incorporation of alarm information from FCIs or AFSs significantly narrows the electrical disturbance-location estimate. Whereas the initialDocket No. 503461.70081“Xl cmltv” calculation produced a broader search area of 14 miles, adding these real-time alarm data points reduces the region to approximately five miles, constituting roughly 8.5% of the total circuit length. For instance, first location 902 is now shown using an eliminated-branch line style because it is on a branch that is downstream from a recloser or FCI 1002 represented by a non-triggered device icon style that did not trigger. Conversely, second location 904 is on a branch that is downstream from a recloser or FCI 1004 represented by a triggered device icon style that did trigger.

[0078] By overlaying which devices triggered alarms (and at what time) onto the baseline reactance-based estimate, the system effectively rules out line segments that remained inactive or did not sense electrical disturbance current. The iterative refinement approach, in which each new data source helps eliminate non-electrically disturbed areas and narrows the search zone, guides field crews with greater precision.

[0079] FIG. 11 shows a further refinement of the electrical disturbance search area, where the system factors in knowledge of circuit segments located “beyond ALS,” indicating regions past a specific automated line switch or similar protection device. By applying this device-based boundary as an additional constraint, the originally narrowed region of five miles is reduced to approximately 3.8 miles, which corresponds to 6.4% of the total circuit. Specifically, a right side line 1102 that was initially included in the search area is shown using an eliminated-branch line style because it is beyond an ALS 1104. The system’s logical checks (beyond mere alarms or reactance measurements) leverage known locations of key distribution assets to exclude unlikely electrically disturbed segments. The ability to incorporate device-specific thresholds or zones (“beyond ALS”) demonstrates a layered, data-driven approach that refines the electrical disturbance location estimate and expedites targeted field inspections.

[0080] FIG. 12 illustrates the additional refinement achieved by incorporating knowledge of the specific electrically disturbed phase into the electrical disturbance-location process. By determining which phase on the distribution circuit experienced the electrical disturbance, the system excludes line sections that do not carry that phase, thereby narrowing the potential location to 2.1 miles, approximately 3.5% of the entire circuit. In particular, second location 904 is shown with a phase-excluded line style that did not include conductors for the electrically disturbed phase. The system leverages detailed electrical characteristics to target the most probable electrically disturbed segments, further reducing the geographic area that field crews need to inspect.Docket No. 503461.70081

[0081] Finally, FIG. 13 demonstrates further refinement, in which the electrical disturbance-location algorithm’s tolerance threshold is further tightened to ±0.23 ohms. This tolerance in ohms is different from the first tolerance that was based on miles. Toumetis collects a dataset of calculated location values versus the actual electrical disturbance location’s ohmic value. Analyzing the distribution of these location errors allows us to determine a statistically-derived tolerance value. This is done for the values in terms of distance / miles as well. Although ohms and miles represent different measurement domains, reactance values can be mapped to physical distances using a circuit model, allowing location estimation in terms of either metric. By applying this stricter margin of error, the previously identified 2.1 -mile region is reduced to approximately 0.7 miles shown as highlighted line segments, representing only 1.2% of the overall circuit length.

[0082] This exercise highlights the method’s potential to achieve very high localization accuracy by calibrating or refining the reactance tolerance, assuming reliable data inputs and modeling. Such fine-grained adjustments could prove valuable in expediting repairs and minimizing service disruptions, as field crews can focus efforts on a considerably smaller section of the line.

[0083] In a final detailed report of FIG. 14, second location 904 has already been determined to include the location of the electrical disturbance. The report shows a predicted electrical disturbance location 1402 and about a half-mile search area visually identified on the map determined as a predicted area of focus for repair efforts.

[0084] In the prediction validation phase of the report (not shown), the system indicates that an electrical disturbance occurred 63 days after the initial precursor events were detected and classified. The algorithm’s classification was deemed “correct,” confirming it had accurately identified both the nature and cause of the anticipated electrical disturbance. When the electrical disturbance ultimately occurred, the measured location error was only 0.063 miles (equivalent to 0.16 ohms), underscoring the high precision of the location estimate. As a result, the overall status was labeled a “Success,” demonstrating that the system’s predictive capability not only forecasted the electrical disturbance well in advance but also achieved a close alignment with the actual outage location.

[0085] In light of this disclosure, skilled persons will appreciate that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. The scope of the present invention should, therefore, be determined only by claims and equivalents.

Claims

Docket No. 503461.70081CLAIMSWhat is claimed is:

1. A method, performed by a waveform measurement unit (WMU) data analysis system, for locating a source of an electrical disturbance event in an electrical power delivery system, the method comprising:receiving, by the WMU data analysis system, monitoring data from the electrical power delivery system, the monitoring data including WMU data and event monitoring data indicative of the electrical disturbance event, in which the WMU data comprises sampled waveform signals and the event monitoring data comprises a discrete, event-driven status report triggered in response to the electrical disturbance event, the electrical disturbance event being characterized by a set of electrical disturbance attributes;determining, based on a first electrical disturbance attribute of the set of electrical disturbance attributes, an initial set of circuit branches representing an initial search region for the source of the outage;reducing a size of the initial search region by eliminating any circuit branch that is inconsistent with a second electrical disturbance attribute from the set of electrical disturbance attributes, thereby establishing a refined search region for the source of the electrical disturbance event; andgenerating a geographic information system (GlS)-based map that presents circuit branches of the refined search region configured to guide a user to the location of the source of the electrical disturbance event.

2. The method of claim 1, in which the reducing step is iteratively performed for additional electrical disturbance attributes from the set to progressively refine the search region.

3. The method of claim 1, in which the event monitoring data includes alarm data from a fault detection device, the alarm data indicating presence or absence of a fault near a location of the fault detection device.

4. The method of claim 3, in which the reducing comprises eliminating circuit branches inconsistent with the alarm data from the fault detection device.

5. The method of claim 1, in which the reducing comprises eliminating circuit branches that lack protection devices of a type indicated by the second electrical disturbance attribute or that are inconsistent with an expected operation of such protection devices.Docket No. 503461.700816. The method of claim 1, in which the reducing includes eliminating circuit branches that do not correspond to a detected disrupted phase.

7. The method of claim 1, in which the reducing includes eliminating underground (UG) circuit branches when the second electrical disturbance attribute indicates that the electrical disturbance event likely originated in an overhead (OH) section.

8. The method of claim 1, in which the reducing includes eliminating overhead (OH) circuit branches when the second electrical disturbance attribute indicates that the electrical disturbance event likely originated in an underground (UG) section.

9. The method of claim 1, in which the generating the GIS-based map includes displaying eliminated circuit branches as visually distinct from those of the refined search region.

10. The method of claim 9, in which the eliminated circuit branches are represented on the GIS-based map using color-coded segments corresponding to exclusion criteria.

11. The method of claim 1, in which the reducing includes adjusting the refined search region based on a tolerance factor associated with event disruption location uncertainty.

12. The method of claim 1, in which the WMU data is event-triggered, capturing sampled waveform signals based on a detection threshold, anomaly detection, or external triggering conditions.

13. The method of claim 1, in which the electrical disturbance event is a sustained fault, a temporary fault, or a precursor event associated with evolving system degradation.

14. The method of claim 1, further comprising identifying, from the monitoring data, a set of temporary and sustained electrical disturbances that share a common set of electrical disturbance attributes.

15. The method of claim 14, in which the common set of electrical disturbance attributes includes both waveform-based parameters and operational or alarm-based parameters.

16. The method of claim 1, in which the first electrical disturbance attribute comprises a reactance-based location value derived from the sampled waveform signals.

17. The method of claim 16, in which determining the initial set of circuit branches includes identifying, from a circuit model of the electrical power delivery system, circuit branches having modeled reactance values consistent with the reactance-based location value.Docket No. 503461.7008118. The method of claim 1, in which the reducing comprises eliminating any circuit branch that is inconsistent with alarm data from a fault detection device relative to a modeled location of the fault detection device.

19. The method of claim 18, in which the alarm data includes a positive alarm indication, and the reducing gives greater weight to the positive alarm indication than to an absence of alarm indication.

20. The method of claim 1, in which the second electrical disturbance attribute comprises a detected disturbed phase, and the reducing comprises eliminating any circuit branch that does not carry the detected disturbed phase.

21. The method of claim 1, in which generating the geographic information system (GIS)-based map comprises overlaying the refined search region on a geographic base map using network topology data from a circuit model of the electrical power delivery system.

22. The method of claim 21, in which the GIS-based map displays eliminated circuit branches differently from circuit branches of the refined search region.

23. The method of claim 1, further comprising applying a tolerance factor to the first electrical disturbance attribute to determine the initial search region.

24. A data analysis system for locating a source of an electrical disturbance event in an electrical power delivery system, the system comprising:a processor; anda non-transitory computer-readable medium storing a circuit model of the electrical power delivery system and instructions that, when executed by the processor, cause the system to:receive monitoring data from the electrical power delivery system, the monitoring data including WMU data and event monitoring data indicative of the electrical disturbance event, in which the WMU data comprises sampled waveform signals and the event monitoring data comprises a discrete, event-driven status report triggered in response to the electrical disturbance event, the electrical disturbance event being characterized by a set of electrical disturbance attributes;determine, based on a first electrical disturbance attribute of the set of electrical disturbance attributes, an initial set of circuit branches representing an initial search region for the source of the electrical disturbance event;1Docket No. 503461.70081reduce a size of the initial search region by eliminating any circuit branch that is inconsistent with a second electrical disturbance attribute from the set of electrical disturbance attributes, thereby establishing a refined search region for the source of the electrical disturbance event; andgenerate a geographic information system (GlS)-based map that presents circuit branches of the refined search region to guide a user to a location of the source of the electrical disturbance event.

25. The system of claim 24, in which the circuit model comprises a circuit connectivity model identifying branch connectivity, modeled reactance values, and locations of protection devices in the electrical power delivery system.

26. The system of claim 24, in which the instructions further cause the system to render eliminated circuit branches differently from circuit branches of the refined search region in the GIS-based map.

27. The system of claim 24, in which the WMU data analysis system is implemented in a cloud computing environment or at an edge location associated with a substation.

28. A non-transitory computer-readable medium storing instructions that, when executed by a data analysis system, cause the data analysis system to:receive monitoring data from an electrical power delivery system, the monitoring data including WMU data and event monitoring data indicative of an electrical disturbance event, in which the WMU data comprises sampled waveform signals and the event monitoring data comprises a discrete, event-driven status report triggered in response to the electrical disturbance event, the electrical disturbance event being characterized by a set of electrical disturbance attributes;determine, based on a first electrical disturbance attribute of the set of electrical disturbance attributes, an initial set of circuit branches representing an initial search region for a source of the electrical disturbance event;reduce a size of the initial search region by eliminating any circuit branch that is inconsistent with a second electrical disturbance attribute from the set of electrical disturbance attributes, thereby establishing a refined search region for the source of the electrical disturbance event; andgenerate a geographic information system (GlS)-based map that presents circuit branches of the refined search region to guide a user to a location of the source of the electrical disturbance event.Docket No. 503461.7008129. The non-transitory computer-readable medium of claim 28, in which the instructions further cause the data analysis system to apply a tolerance factor to the first electrical disturbance attribute to determine the initial search region.

30. The non-transitory computer-readable medium of claim 28, in which the instructions further cause the data analysis system to iteratively eliminate circuit branches using additional electrical disturbance attributes from the set of electrical disturbance attributes.

31. The system of claim 24, further comprising a user interface configured to display the GIS-based map, the initial search region, and the refined search region.

32. The system of claim 31, in which the user interface is configured to visually distinguish circuit branches eliminated based on alarm data, phase information, or overhead-versus-underground classification.

33. The system of claim 31, in which the user interface is configured to display locations of substations, protection devices, and fault detection devices on the GIS-based map.

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