Method and system for fault detection and prediction in electric grids
Patent Information
- Application Number
- AU2025227160
- Authority / Receiving Office
- AU · AU
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-02-26
- Filing Date
- 2025-02-26
- Publication Date
- 2026-08-27
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Optionally, the method involves analyzing both historical and real-time data of detected events to identify patterns of recurring anomalies (i.e., referring to events or anomalies that happen repeatedly or consistently over time within the electric grid). This analysis is performed over predefined time intervals and across specific geographic regions within the electric grid. By identifying these patterns, the method facilitates proactive maintenance and improves fault prediction accuracy. Pattern detection may comprise analyzing temporal patterns of signal anomalies across predefined time intervals, examining frequency domain characteristics to identify fault-related harmonics, and evaluating spatial patterns of events across geographic regions to correlate anomalies with specific grid components. This comprehensive analysis enables more accurate identification of potential fault locations and better understanding of emerging issues within the electric grid. Exemplary machine learning models used for the classification may include neural networks, Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), and so on. The neural networks model complex nonlinear relationships between features and event types, offering high accuracy in classification tasks. The SVMs are effective for high-dimensional data and find hyperplanes that best separate different event classes in the feature space. The random forests are an ensemble learning method that may use multiple decision trees to improve classification accuracy and may be robust against overfitting. The k-NN may classify samples based on the majority class among their k-nearest neighbors in the feature space. The classification allows preemptive identification and repairing of faults or defects prior to the faults / defects leading to failure of components of the electric grid. For example, by classifying different types of a certain event, it may be possible to determine whether an issue in a component is due to aging, insulation, environmental factors, or mechanical defects, and apply appropriate remediation measures to overcome the issue. For example, an event such as partial discharge may be classified as corona discharge, surface partial discharge, floating partial discharge, internal partial discharge, void partial discharge, or a phase-synchronous sparking. One of the event types is lightning, which, when detected as hitting directly or at close proximity to an electrical grid, is a precursor event to broken insulation, transformers, AMR meters, and lightning arrestors. Lightning events can be located by integrating data from lightning data services, or detected as traveling waves on the grid lines, or geolocated by lightning sensors or traveling wave sensors on the grid. The method involves analyzing patterns and temporal characteristics of detected phenomena to determine both fault severity and fault locations. By evaluating the timing, frequency, and duration of events, the method provides insights into the underlying nature and potential impact of faults. This analysis enables more accurate assessment of fault severity and identification of critical areas within the electric grid that require attention. Optionally, the one or more components, where the one or more events are detected, may include one or more of substations in the transmission network, substations in the distribution network, transmission lines in the transmission network, distribution lines in the distribution network, equipment in the transmission network, or equipment in the distribution network. The transmission lines and the distribution lines may include one or more overhead lines built of bare conductors, covered conductors, insulated conductors, or coaxial cables, and underground cables, which can be for example oil-filled, XLPE (Cross-Linked Polyethylene) or GIL (Gas-Insulated Lines) cables, and busbars. Optionally, the one or more events are detected using a set of sensors. The set of sensors may include one or more of each of a magnetic field detection sensor, an electric field detection sensor, an electromagnetic field detection sensor, an accelerometer, a temperature sensor, a wind detection sensor, a lightning sensor, a humidity sensor, a strain gauge sensor, a vibration sensor, an image sensor, a motion sensor, a personal device, an acoustic sensor, an ultrasound sensor, an infrared sensor, an ultraviolet sensor, or a traveling wave sensor, protection or switch device measurement or action data, load or generation connect / disconnect / start / stop data and so on. Each of the signals, which are propagating through the distribution network or the transmission network of the electric grid, may be associated with a first phase, a second phase, a third phase, a neutral conductor, or a ground conductor. Each sensor of the set of sensors may be configured to simultaneously monitor the signals associated with the first phase, the second phase, the third phase, the neutral conductor, or the ground conductor, for detecting the one or more events. The set of sensors may measure the parameters from the signals and transmit the measurements and the signals to a server that includes a processor. The processor may be included in a server. The processor may be implemented as one of, but not limited to, a microprocessor, a microcontroller, or a controller. In an example, the processor may be implemented as an application-specific integrated circuit (ASIC) chip, or a reduced instruction set computer (RISC) chip. The processor may be operable to detect the one or more events based on the measurements and processing of the signals. The set of sensors may be configured to monitor changes in signal propagation and fault-related anomalies over time. By continuously measuring parameters such as amplitude, phase angle, and frequency, the sensors can identify trends or patterns that indicate the development of potential faults. This temporal analysis allows for early detection of issues before they escalate into major faults. After the detection of the one or more events, the one or more locations, in the distribution network or the transmission network, where the one or more events are detected are determined. The determination may be based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, grid operation actions, grid component actions, source or load connections or disconnections, measurement of a test signal injected into the grid, or location-specific sensor measurements. The pattern may include a temporal or frequency pattern. The temporal pattern may be determined from time-series data. The time-series data is obtained based on counts of detections or signal strength of each of the one or more events (such as PD that is indicative of deteriorating insulation) over a certain timeperiod. A higher count or signal strength per timespan or increasing or alternating trend may be precursor of a future fault. The frequency pattern may be determined from an analysis of frequency of signals associated with the events (such as PD signals associated with a PD event). In an embodiment, the analysis may be a Fourier Transform on the signals associated with the events, which may facilitate identification of characteristic harmonics associated with specific types of faults (such as those caused by arcing). The determination of the one or more locations where the events are detected may be further based on weather conditions correlated to event occurrences, traveling wave analysis for identifying fault locations by analyzing wave propagation characteristics caused by precursor events, or location-specific sensor measurements. Temporal and geographic correlation of events may be used to identify specific components of the electric grid where faults are likely to occur. By analyzing the time-series patterns of event detection along with the geolocations of those events, specific grid components contributing to or affected by potential faults can be pinpointed. This combined approach enables a more accurate identification and prioritization of maintenance activities. In an embodiment, the location is determined from weather conditions (determined based on measurements obtained from sensors such as wind, temperature, or humidity sensors) of a particular area and the location-specific sensor measurements because one or more sensors of the set of sensors located in an area may measure a parameter of a signal associated with an event. If the location of the one or more sensors are known, and if weather in the area matches the weather conditions, the event is located. Further, sensors such as accelerometers may detect vibrations and motion. The detected vibrations and motion are indicative of mechanical issues, abnormalities, structural anomalies, and so on, in components (such as substations, transformers, transmission lines, and so on) of the electric grid. Furthermore, the traveling wave analysis may be performed on a traveling wave, which is generated at a particular location, where a component (such as a transmission line) of the electric grid is situated, due to sudden change in voltage or current. The change in the voltage or current may be an event, which can be detected by multiple traveling wave sensors. The locations of the traveling wave sensors and a timedifference between arrival of the traveling wave at the locations may be used to determine the location of the event. Traveling wave analysis may comprise detecting reflected waves generated at fault locations to determine their distance from the sensors. The analysis includes examining wave attenuation, polarity, and propagation time to classify the severity and type of fault. This approach enables a more accurate assessment of fault characteristics, leading to improved fault localization and faster response times. Traveling wave analysis may involve detecting wave propagation characteristics caused by one or more events, determining time differences in wave arrival at multiple sensors, and correlating these time differences with the locations of the detected events within the electric grid. This approach allows for accurate localization of faults, enabling faster response times and more precise maintenance actions. The results of pattern detection and traveling wave analysis may be combined to refine fault location predictions. This combined approach allows for a more precise assessment of fault locations and provides a confidence score for each prediction, enabling operators to prioritize critical issues with higher accuracy. Optionally, the one or more events are phenomena associated with probable occurrence of the one or more faults at the one or more determined locations. The phenomena may include one or more of PD, intermittent earthing, high-impedance earthing, lightning strike, circuitbreaker tripping, fuse blowing, abnormal current harmonics, abnormal voltage harmonics, sparking (due to a poor electrical contact), physical discontinuity of one or more conductors (i.e., breaking of the one or more conductors) in the electric grid, and leaking insulation. The PD may be classified as a corona discharge, a surface PD, a floating PD, an internal PD, a void PD, or a phase-synchronous sparking. The type of PD may be classified based on a temporal analysis of the PD signals. Based on the temporal analysis, a temporal pattern may be identified. The temporal pattern is identified on an oscillograph or a phase-resolved PD chart. PD-like signals may be emitted by semiconductor devices, such as power supplies, inverters, and converters (as these semiconductor devices have known signal patterns), which should be labeled as noise, using, e.g. the temporal information that the signal amplitude of such noise typically stays at the same level for a long period of time, hours, days, or weeks, as compared to real PD signals the amplitude of which fluctuate much more. Single phase semiconductor noise typically has a maximum 1 or 2 times per mains frequency phase cycle. Additionally, some high-voltage AC / DC and DC / AC converters may generate noise pulses at 6 or 12 times per mains frequency cycle due to thyristor or IGBT switching actions, which information makes these easy to remove from the PD distance detection. Also, partial discharge (PD) signals do not travel very far along the grid, unless the origin is a badly conducting component, such as a switch or a joint. But in those cases, there typically exists a phase signal deformation at the same specific moment as the spark signal is detected which is easy to notice. Therefore, a method to filter out some semiconductor device noise on high or medium voltage grids is to filter out those signals that travel very long distances. After the detection of the one or more events and the determination of the one or more locations, the alert is generated. The alert includes the detected one or more events, the determined one or more locations, and one or more components of the electric grid in which the one or more events are detected. The alert corresponds to predictions of occurrences of the one or more faults. Optionally, the alert is a visual alert or a textual alert. The visual alert may include a map of a real-world region, signal strength, and / or a risk index associated with each event of the one or more events detected in the real-world region, and / or an urgency index associated with each event. The map includes one or more pointers, or heatmaps indicative of one or more geolocations corresponding to the one or more locations where the one or more events have been detected (i.e., where the one or more components are situated), and a topology map of the electric grid. The alert may include a topology map of the electric grid that displays fault locations identified using traveling wave analysis, as well as regions of concern highlighted based on pattern detection. This visual representation allows operators to quickly identify critical fault locations and areas of the grid that require further investigation or maintenance. The alert may dynamically update as additional data is received from event detection, location-specific sensors, traveling wave analysis, or pattern detection. This ensures that users have access to the most current information about fault locations, severity, and associated risk indices, enabling more informed decisionmaking and timely maintenance actions. The visual alert may further reflect patterns of event recurrence, providing users with insights into repeated occurrences of anomalies or faults within specific components or regions of the electric grid. This information helps operators identify persistent issues and prioritize maintenance efforts to prevent recurring faults. The textual alert may include a link (such as a Uniform Resource Locator (URL)) of a webpage that directs to a visual alert. The visual alert may be generated by the processor. The processor may cause the visual alert to be rendered on a user interface (UI) of an application associated with predictive fault detection and maintenance of an electric grid. The application may be installed on a user device that may interact with the server to fetch the visual alert or the textual alert. The visual alert or the textual alert may be rendered on a display of the user device. In an embodiment, a risk index, and an urgency index may be determined for each event of the one or more events. The determination of the risk index or the urgency index of an event of the one or more events is based on locations of the set of sensors (which may be obtained from a topology map of the grid that includes all components of the electric grid), a location of the one or more locations where the event is detected (and where a component, in which the event is detected, is situated) the parameters of a signal (i.e., an event signal associated with the event) propagating through the distribution network or the transmission network of the electric grid, a type to which the event is classified as, and association of the event as a precursor to a known fault occurring in a certain type of component in the electric grid. The risk and urgency indices may be dynamically generated based on the strength, frequency, and location of detected events. Events with higher signal strength, higher frequency of occurrence, or those located in critical areas of the grid are assigned higher risk and urgency scores. This dynamic assessment allows for prioritization of maintenance actions to address the most severe or urgent issues first. The alert may include a dynamically updating risk index and urgency index based on signal patterns, event severity, and historical trends. The alert may include a risk index and an urgency index derived from the strength, frequency, and recurrence of patterns identified by pattern detection, as well as the precision and severity indicated by traveling wave analysis. This combined approach ensures that operators can quickly assess the relative importance of different faults and prioritize actions based on the most critical risks. It further ensures that users and operators receive the most up-to-date information about the relative importance of different faults, allowing them to prioritize maintenance actions more effectively. Optionally, the visual alert is generated based on one or more user inputs. The one or more user inputs are indicative of at least one of a time range, a time interval, a frequency, and a selection of an area on the map. The time range may include a starting timestamp and an ending timestamp. The one or more events are detected at the one or more geolocations within the time range. The time interval is one of a day, a week, or a month within the time range, or start and end times per user entry, i.e., a time range specifically provided by the user. The one or more events are detected at the one or more geolocations within the time interval. The map may indicate, via the heatmap and / or the pointer, the one or more locations where the one or more events were detected. The frequency is one of continuous or sporadic. The one or more geolocations may be determined based on detection of the one or more events within the time range. The one or more events are continuous phenomena or sporadic phenomena, i.e., the one or more events are detected continuously or sporadically. Based on selection of the area on the map, the one or more events, detected within the area of the map, may be rendered on the map. Optionally, the user inputs may refine the temporal and spatial scope of fault analysis. For example, users can specify a time range to analyze events within a specific period or select a geographic area on the map to focus the analysis on events detected within that location. This approach allows for a more targeted and customized fault analysis. Optionally, the visual alert further includes a histogram or a time-graph. The time-graph indicates count of events or signal strength detected at each time-instance of a set of time-instances within the time-range or the time-interval, one or more characteristics associated with each event of the one or more events, the risk index associated with each of the one or more events, and the urgency index associated with each of the one or more events. In an embodiment, the time-graph further indicates events detected at each time-instance of a set of time-instances within the selected area (which may be received as a user input). Optionally, the visual alert further includes a list. Each entry of the list may include a component of the one or more components in which an event of the one or more events is detected, a type of the event detected, a time-instance of occurrence of the event, and a geolocation at which the event is detected. The time-instance may be within the time-range or the time-interval (which may be received as a user input). Optionally, the one or more user inputs are indicative of a type of event or a component of the electric grid. The end-user may select a particular type of event or a certain component. Based on the selection, one or more events of the selected type or one or more events detected in the selected component may be detected. Thereafter, geolocations of the one or more events of the selected type or geolocation of the component are determined. The determined geolocations of the one or more events or the determined geolocation of the component may be rendered on the map. The histogram or time-graph may indicate time-instances during which the one or more events of the selected type were detected or timeinstances during which the one or more events were detected in the component. The time-instances may be within the time-range or the time-period (which may be received as a user input). In an embodiment, the list may include one or more entries and each entry of the one or more entries includes at least one of: an event of the one or more events of the type and the component. One of the columns of the list will indicate the one or more events of the selected type, or the list may include one or more entries associated with the one or more events detected in the selected component. Optionally, the map includes weather data at, or in vicinity of, the one or more geolocations corresponding to the one or more locations where the one or more events are detected. The histogram includes the weather data at the one or more locations during each time-instance of the set of time-instances. Further, each entry of the list includes the weather data at the geolocation included in the corresponding entry. The weather data includes one or more of temperature, humidity, wind speed, lightning condition, and thunderstorm condition. Based on selection of the area, where the one or more events have been detected, weather conditions at the area may be rendered. The weather conditions, within the area of the map, may be rendered on the map along with the heatmaps / pointers. The weather data may be dynamically linked to historical fault trends, allowing for a more comprehensive analysis of how environmental conditions impact the occurrence and severity of faults within the electric grid. This enables predictive analysis based on historical patterns to improve fault prediction accuracy. Optionally, a predictive machine learning model is trained to execute at least one task. The execution of the at least one task enables generation of the alert. Optionally, components, amongst the set of components included in the electric grid, are classified based on detection of the one or more events. The classification is a result that may be obtained based on training the predictive machine learning model on ground truth data. The ground truth data may be a mapping between inputs corresponding to historical detections of events of the one or more events and outputs corresponding to the set of components in which the events are detected. For example, an event may be mapped to a certain component of the electric grid based on consistent detection of the event in the component. The predictive machine learning model is trained based on the mapping. Furthermore, the machine learning model predicts a probability of an occurrence of each event of the one or more events at one or more time instances in the one or more locations. The predictions may be based on weather forecasts at the one or more locations. The predictive machine learning model may be trained on ground truth data that comprises of correlation between inputs corresponding to historical detections of events of the one or more events and outputs corresponding to electrical load and / or weather conditions similar to the weather forecast. For example, if historically certain events have been detected in certain locations during certain weather conditions, then mapping between the detected events and the weather conditions may be established. The predictive machine learning model may be trained based on the mappings to generate the predictions. After a maintenance crew is dispatched to the indicated location of the precursor events, often the weather or other conditions have changed in such a way that the failing component does not anymore emit precursor signals, and the crew have trouble finding the actual broken component amongst multitude of grid components. The benefit of the above-mentioned event occurrence prediction based on load and weather conditions is that the maintenance crew can be sent out for investigations at a time when there is the highest probability of the failing component to emit the precursor signal. The present disclosure also relates to the second aspect as described above. Various embodiments and variants disclosed above, with respect to the aforementioned first aspect, apply mutatis mutandis to the second aspect. The system includes a set of sensors, a processor, and a display device. The processor may be included in a server. The display device may be included in a user device such as a smartphone. In the smartphone, an application that allows predictive maintenance of the electric grid, may be installed. The application may include a user interface, which may be controlled by the server to display the visual alert or the textual alert. The application may include instructions, which allow the smartphone to request the visual alert from the server or notify an end-user that the visual alert or the textual alert has been received from the server and is ready for rendering. The present disclosure also relates to the third aspect as described above. Various embodiments and variants disclosed above, with respect to the aforementioned first aspect and second aspect, apply mutatis mutandis to the third aspect. DETAILED DESCRIPTION OF THE DRAWINGS Referring to FIG. 1, there is illustrated an exemplary visual alert 100 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The visual alert 100 includes one or more events 102 that are precursors of one or more faults in the electric grid, one or more locations 104 where the one or more events 102 are detected, and one or more components 106 of the electric grid in which the one or more events are detected. The one or more components 106 include substations in the transmission network, substations in the distribution network, transmission lines in the transmission network, distribution lines in the distribution network, equipment in the transmission network, or equipment in the distribution network, and wherein the transmission lines and the distribution lines include one or more of lines, or cables of any type, whether overhead or underground. The visual alert includes a map 110 of a real-world region, a risk index associated with each event of the one or more events 102 detected in the real-world region, and an urgency index associated with each event. The map 110 includes one or more pointers 113 and / or heat maps 112 indicative of one or more geolocations corresponding to the one or more locations 104, and a topology map of the electric grid 115. Each of the one or more pointers 113, or heatmaps, may further indicate a location of a corresponding sensor, e.g., traveling wave sensor, of a set of sensors. The visual alert 100 may be generated based on the one or more user inputs that include a time range 114, a time interval 116, and a frequency 118. The time range 114 includes a starting timestamp and an ending timestamp. The one or more events 102 are detected at the one or more geolocations within the time range 114. The time interval 116 is a day, a week, or a month, within the time range 114. The one or more events 102 are detected at the one or more geolocations within the time interval 116. The frequency 118 is continuous or sporadic. The one or more geolocations are determined based on detection of the one or more events within the time range, wherein the one or more events are continuous phenomena or sporadic phenomena. The visual alert 100 further includes a histogram 120 or a time-graph that indicates a count of events and / or signal strength detected at each time-instance of a set of time-instances 122 within the time-range 114 or the time-interval 116, one or more characteristics associated with each event of the one or more events 102, a risk index, and / or an urgency index. The count of events and / or signal strength detected per a time-instance are shown as bars 130. A histogram 120 shows the count of events as a series of bars wherein bar's height indicates the count of events at a time-instance of question. The visual alert 100 further includes a list 124. Each entry of the list 124 may include a component of the one or more components 106 in which an event of the one or more events 102 is detected, a type 126 of the event, a timeinstance 128 of occurrence of the event, and a geolocation at which the event is detected. The time-instance is within the time-range 114 or the time-interval 116. FIG. 1 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 2, there is illustrated an exemplary visual alert 200 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The visual alert 200 is generated based on the one or more inputs. The starting timestamp and the ending timestamp of time range 114 is "29th January" and "4th February" respectively. The time interval 116 is "Week" and the frequency 118 is "All triggers". The histogram 120 or time-graph indicates a count of events or signal strength detected at each timeinstance within "29th January" and "4th February". The list 124 includes twenty-two entries corresponding to twenty-two events detected within "29th January" and "4th February". Each entry may include a component ("Device") of the one or more components 106 in which an event of the one or more events 102 is detected, a type 126 of the event, a timeinstance 128 of occurrence of the event, and a location at which the event is detected. FIG. 2 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 3A, there is illustrated a flowchart 300A for prioritizing tasks for fixing issues in an electric grid, in accordance with an embodiment of the present disclosure. The fixing of the issues may prevent occurrence of a potential fault in the electric grid. The tasks may be prioritized based on a risk index and an urgency index. The risk index and an urgency index may be determined for each event of the one or more events that have been detected. In an embodiment, the risk index or the urgency index of an event is determined based on locations of (a set of) sensors that may be used for measuring parameters for detection of a location of the event, the location of the one or more locations where the event is detected, parameters (such as signal strength) of a signal associated with the event (which is propagating through the distribution network or the transmission network of the electric grid), a type to which the event is classified as, and association of the event as a precursor to a known fault occurring in a certain type of component in the electric grid. Referring to FIG. 3B, there is illustrated a flowchart 300B for prioritizing tasks for fixing issues in an electric grid, in accordance with an embodiment of the present disclosure. The flowchart 300B differs from the flowchart 300A in that the flowchart 300B considers also environmental aspects and people risk levels, e.g., in wildfire areas, rural / urban areas. For measuring the risk index, a model may be developed. The model combines features of the signals as an event type with historical fault data to estimate the risk level of each component or section of the electric grid. Machine learning models, statistical analysis, or expert systems may be used to correlate features of the signals with the likelihood of fault occurrence. For example, a risk score may be computed for each component by weighting features according to their predictive value for failure. For instance, a primary transformer with high temperature trends and frequent high-frequency disturbances is likely to be assigned with a higher risk score than a pole-mounted lightning arrester. The purpose of the risk index is to describe the potential fatality if the component, which emits the precursor events and signals, would fail. By knowing the event location and possibly also the grid components at or near that location, and the trend of the emitted precursor events and signals, a risk index for a group of precursor events appearing in that location can be calculated. For example, a risk index can be in the range of 1..10 where 1 contributes low risk, and 10 very high risk. Then, if the location has an overhead line in a moist rural area without wildfire risks, and the precursor event type detected indicates a broken lightning arrestor with very small signal strength, the risk index could be very low, e.g. 2. However, for precursor events in a location which is known for wildfires and pole fires, and the area consists of urban or suburban areas, and the precursor event type indicates an insulator failure or a broken and downed conductor, the risk level can be very high, e.g., 8 for a broken insulator and 10 for a downed conductor (risk of electric shock). The urgency index, indicative of urgency of addressing a potential fault in a component, can be determined based on the trend of the Signal Strength of precursor events, together with event type, and known component(s) at that location. E.g., Low signal strengths of precursor event signals of an insulator may not be urgent to be repaired as typically the insulators fail gradually - the urgency index could be "Weeks". As another example, a sparking blade switch component, due to bad contact, will soon cease to conduct at all, and probably will generate a considerable amount of heat. In this case the urgency index should be "Minutes". A further example could be a lightning strike on an overhead line, which has been trapped by lightning arresters (overvoltage arresters). If there are very mild indicators of a damaged lightning arrester, the urgency index could be "Days", as the probability of a slightly damaged lightning arrester to fail fatally soon is rather low. FIGs. 3A and 3B are merely examples, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 3C, there is illustrated a flowchart 300C for a probability model for a precursor event to occur at the location in an electric grid, in accordance with an embodiment of the present disclosure. In an embodiment a precursor event location, a precursor event type, a precursor event signal strength, weather data and a state of the electric grid, including e.g., but not limited to, states of loads, and states of switches, are collected to and stored as a history data per event location, and used in machine learning, which is used to provide a probability model for a precursor to occur at the location. Based on the provided probability model an event / events at a certain time-instance can be predicted. FIG. 3D illustrates a flowchart for a list of prioritized tasks with a proposed time to dispatch the crew to the event location, in accordance with an embodiment of the present disclosure. The flowchart 300D in FIG. 3D is a continuation of the flow chart depicted in FIG. 3C. After the probability model for a precursor to occur at the location is provided the probabilities of the precursor event signals to exist over a given timespan, e.g., every hour during the next 7 days, are estimated by take into account the weather forecast and grid state. Further, by taken into account, the event type location, risk index, urgency index and said probabilities of the precursor event signals, a list of prioritized tasks with proposed time to dispatch the crew to event location is provided. Referring to FIG. 4, there is illustrated an exemplary visual alert 400 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The starting timestamp and the ending timestamp of time range 114 is "12th February" and "18th February" respectively. The time interval 116 is "Week" (i.e., "Monday to Sunday") and the frequency 118 is "All" (i.e., All triggers). The histogram 120 or time-graph indicates a count of events or signal strength detected at each time-instance within "12th February" and "18th February". The list includes four hundred and ninety-two entries corresponding to four hundred and ninety-two events, shown on top of the map, detected within "12th February" and "18th February". Each entry may include a component ("Device") of the one or more components in which an event of the one or more events is detected, a type of the event, a time-instance of occurrence of the event, and a location at which the event is detected. FIG. 4 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 5, there is illustrated an exemplary visual alert 500 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The visual alert 500 is generated based on an input. The input may be a selection of an area 117 (shown inside a rectangle in FIG. 5) on the map 110 of the visual alert 500. Based on the selection, events detected within the area may be rendered on the map 110. The time-graph further indicates events detected at each time-instance of a set of time-instances within the selected area. The list includes one hundred and twenty-two entries, shown on top of the map, corresponding to one hundred and twenty-two detected in the selected area. FIG. 5 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 6, there is illustrated an exemplary visual alert 600 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The visual alert 600 is generated based on an input. The input may be a zoom gesture (e.g., by a user) on one of the events detected within the area. Based on reception of the zoom gesture, details associated with the specific event may be rendered on the map 110. FIG. 6 shows the area selected in FIG. 5 enlarged, showing the selected area in more detail, and the detected events, at their locations, on the power line of the electric grid, wherein the events marked with "X" 129. The time-graph 120 further indicates events detected at each time-instance of a set of time-instances within the selected area. FIG. 6 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 7, there is illustrated an exemplary visual alert 700 that corresponds to prediction of faults in an electric grid, in accordance with an embodiment of the present disclosure. The map 110 in the visual alert 700 includes weather data 119 at, or in vicinity of, the one or more geolocations corresponding to the one or more locations where the one or more events (a total of 237 events during week) are detected. The histogram, or time-graph, 120 includes the weather data 119 at the one or more locations during each time-instance 128 of the set of timeinstances. The weather data includes one or more of temperature, humidity, wind speed, lightning condition, and a thunderstorm condition. Based on selection of the area, where the events have been detected, weather conditions at the area may be rendered. In FIG. 7, the selection of time-instance 128' in the time-graph 120 is represented by a doublepointed arrow. By moving the selection, or selection bar (two-way arrow in the FIG. 7) to the right or left, the user can select the desired timeinstance for further evaluation. The rectangle 128", which is represented by dashed lines on top of the time-graph, depicts the predictive machine learning model's prediction of events at a certain time-instance. The benefit of the above-mentioned event occurrence prediction based on load and weather conditions is that the maintenance crew can be sent out for investigations at a time when there is the highest probability of the failing component to emit the precursor signal. FIG. 7 is merely an example, which should not unduly limit the scope of the claims herein. A person skilled in the art will recognize many variations, alternatives, and modifications of embodiments of the present disclosure. Referring to FIG. 8, there is shown a schematic diagram of a system 800 for predicting faults in electric grids, in accordance with an embodiment of the present disclosure. The system 800 comprises a server 802, a set of sensors 804, and a user device 806. The server 802 includes a processor 808. The user device 806 includes a display device 810. The processor 808 may collect measurements from the set of sensors 804. The set of sensors 804 may include a magnetic field detection sensor, an electric field detection sensor, an electromagnetic field detection sensor, an accelerometer, a temperature sensor, a wind detection sensor, a lightning sensor, a humidity sensor, a strain gauge sensor, a vibration sensor, an image sensor, a motion sensor, or a personal digital device, an acoustic sensor, an ultrasound sensor, an infrared sensor, an ultraviolet sensor, or a traveling wave sensor. The processor 808 receives signals that are propagating through a distribution network or a transmission network of an electric grid. In an event, the reception may be triggered periodically, or the reception may be triggered based on reception of a user input that includes an instruction to fetch events that may occurred in the electric grid in a particular location over a certain time range or period. The processor 808 is capable of detecting one or more events that are precursors of one or more faults in the electric grid. The detection is based on at least one of parameters associated with the received signals or radiation produced by operation of components in the distribution network or the transmission network. Said events are detected by use of the set of sensors 804. The processor 808 determines one or more locations, in the distribution network or the transmission network, where the one or more events are detected, wherein the determination is based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, or location-specific sensor measurements. Based on detection, the processor 808 generates an alert (alerts) that includes the one or more events, the one or more locations, and one or more components of the electric grid in which the one or more events are detected, wherein the alert corresponds to predictions of occurrences of the one or more faults. It may be understood by a person skilled in the art that FIG. 8 includes a simplified architecture of the system 800, for sake of clarity, which should not unduly limit the scope of the claims herein. It is to be understood that the specific implementation of the system 800 is provided as an example and is not to be construed as limiting. The person skilled in the art will recognize variations, alternatives, and modifications of embodiments of the present disclosure. FIG. 9 illustrates steps of a method for detecting one or more events, determining one or more locations, and generating alerts that include one or more events, one or more locations, and one or more components of the electric grid, in accordance with an embodiment of the present disclosure. At step 902, one or more events 102, that are precursors of one or more faults in the electric grid, are detected. The detection is based on at least one of parameters associated with signals propagating through a distribution network or a transmission network of the electric grid or radiation produced by operation of components in the distribution network or the transmission network. At step 904, one or more locations 104, in the distribution network or the transmission network, where the one or more events are detected, may be determined. The determination is based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, or location-specific sensor measurements. At step 906, an alert 100 that includes the one or more events, the one or more locations, and one or more components 106 of the electric grid, in which the one or more events are detected, are generated. The alert corresponds to predictions of occurrences of the one or more faults. The aforementioned steps are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. FIG. 10 shows a phase resolved partial discharge view, in accordance with an embodiment of the present disclosure. The phase resolved partial discharge view of FIG. 10 is depicted, when zooming into the map 110 or when clicking on the graph 120, e.g., the bar 130 on the time-graph. When zooming or clicking, the individual event locations are then displayed on the map as icons 113 or markers 112 and on histogram or time-graph 120 as icons or markers 130 (shown in FIG. 6) at that time, and the user can further click or touch on the icon or marker to open detailed view of this event, and a phase-resolved partial discharge (PR.PD) diagram of the partial discharge or phase synchronous signal of the sensors detecting the event. FIG. 11 shows oscillographs of the high frequency and phase signals, in accordance with an embodiment of the present disclosure. The oscillographs view of FIG. 11 is depicted when zooming into the map 110 or graph 120, the individual event locations are displayed on the map as icons 113 or markers 112 and on graph 120 as icons or markers 129 at that time, and the user can click or touch on the icon or marker to open up detailed view of this event, including the signal data amplitude vs time of the sensors detecting that event.
Claims
1. A method for predicting faults in an electric grid, the method comprising:detecting one or more events (102) that are precursors of one or more faults in the electric grid, wherein the detection is based on at least one of parameters associated with signals propagating through a distribution network or a transmission network of the electric grid or radiation produced by operation of components in the distribution network or the transmission network;determining one or more locations (104), in the distribution network or the transmission network, where the one or more events are detected, wherein the determination is based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, or location-specific sensor measurements; andgenerating an alert (100) that includes the one or more events, the one or more locations, and one or more components (106) of the electric grid in which the one or more events are detected, wherein the alert corresponds to predictions of occurrences of the one or more faults.
2. The method according to claim 1, wherein the one or more events (102) are phenomena associated with probable occurrence of the one or more faults at the one or more locations (104), wherein the phenomena includes one or more of partial discharge, intermittent earthing, high-impedance earthing, lightning strike, circuit-breaker tripping, fuse blowing, abnormal current harmonics, abnormal voltage harmonics, sparking, physical discontinuity of one or more conductors in the electric grid, and leaking insulation.
3. The method according to claim 1 or 2, wherein the one or more components (106), where the one or more events (102) are detected,include substations in the transmission network, substations in the distribution network, transmission lines in the transmission network, distribution lines in the distribution network, equipment in the transmission network, or equipment in the distribution network, and wherein the transmission lines and the distribution lines include one or more of overhead lines, underground cables.
4. The method according to any of the preceding claims, wherein temporal and geographic correlation of events is used to identify specific components of an electric grid.
5. The method according to any of the preceding claims, wherein the one or more events (102) are detected by use of a set of sensors (804), wherein the set of sensors include one or more of each of a magnetic field detection sensor, an electric field detection sensor, an electromagnetic field detection sensor, an accelerometer, a temperature sensor, a wind detection sensor, a lightning sensor, a humidity sensor, a strain gauge sensor, a vibration sensor, an image sensor, a motion sensor, or a personal digital device, an acoustic sensor, an ultrasound sensor, an infrared sensor, an ultraviolet sensor, or a traveling wave sensor, wherein each of the signals are associated with a first phase, a second phase, a third phase, a neutral conductor, or a ground conductor, and wherein each sensor of the set of sensors are configured to simultaneously monitor the signals associated with the first phase, the second phase, the third phase, the neutral conductor, or the ground conductor.
6. The method according to claim 5, wherein the sensors are configured to monitor changes in signal propagation and fault-related anomalies over time.
7. The method according to any of the preceding claims, wherein the alert (100) is a visual alert or a textual alert, wherein the visual alert includes a map (110) of a real-world region, a risk index associated with eachevent of the one or more events (102) detected in the real-world region, and an urgency index associated with each event, and wherein the map includes one or more pointers or heatmaps (112) indicative of one or more geolocations corresponding to the one or more locations (104), and a topology map of the electric grid.
8. The method according to claim 7, wherein the risk and urgency indices are dynamically generated based on the strength, frequency, and location of detected events.
9. The method according to any of the preceding claims, wherein the method further comprises receiving one or more user inputs, wherein the alert is generated based on the one or more user inputs.
10. The method according to claim 9, wherein the user inputs refine the temporal and spatial scope of fault analysis.
11. The method according to claim 9 or 10, wherein the one or more user inputs are indicative of at least one of:a time range (114), wherein the time range includes a starting timestamp and an ending timestamp, wherein the one or more events (102) are detected at the one or more geolocations within the time range;a time interval (116), wherein the time interval is one of a day, a week, or a month within the time range, or a time range specifically provided by the user, wherein the one or more events are detected at the one or more geolocations within the time interval;a frequency (118), wherein the frequency is one of continuous or sporadic, wherein the one or more geolocations are determined based on detection of the one or more events within the time range, wherein the one or more events are continuous phenomena or sporadic phenomena; anda selection of an area on the map (110), wherein the one or moreevents are detected in the area, wherein the one or more events are detected within the area of the map.
12. The method according to any of the preceding claims, wherein the alert further includes a histogram (120) or a time-graph that indicates a count of events or signal strength detected at each time-instance of a set of time-instances (122) within the time-range (114) or the time-interval (116), one or more characteristics associated with each event of the one or more events, the risk index, or the urgency index.
13. The method according to claim 1, wherein historical and real-time data of the detected events are analyzed to identify patterns of recurring anomalies over predefined time intervals and geographic regions within the electric grids.
14. The method according to claim 2, wherein the patterns and temporal characteristics of the phenomena are used to determine fault severity and locations.
15. The method according to claim 2, wherein the partial discharge is classified as one of corona discharge, surface partial discharge, floating partial discharge, internal partial discharge, void partial discharge, or a phase-synchronous sparking.
16. The method according to any of the preceding claims, wherein the determination of the one or more locations where the events are detected is further based on weather conditions correlated to event occurrences, traveling wave analysis for identifying fault locations by analyzing wave propagation characteristics caused by precursor events, or locationspecific sensor measurements.
17. The method according to any of the preceding claims, wherein the method comprises generating a visual representation of the fault location and its associated risk and urgency indices on a map.
18. The method according to any of the preceding claims, wherein the alert dynamically updates as additional data is received from event detection, location-specific sensors, traveling wave analysis, or pattern detection.
19. The method according to any of the preceding claims, wherein the detection of events is further based on traveling waves propagating in the electric grid, or environmental conditions affecting the electric grid.
20. The method according to any of the preceding claims, wherein the alert includes a dynamically updating risk index and urgency index based on signal patterns, event severity, and historical trends.
21. The method according to any of the preceding claims, wherein the traveling wave analysis comprises detecting wave propagation characteristics caused by the one or more events, determining time differences in wave arrival at multiple sensors, and correlating the time differences with the locations of the one or more events within the electric grid.
22. The method according to any of the preceding claims, wherein the traveling wave analysis comprises detecting reflected waves from the event location to determine its distance from sensors, and analyzing wave attenuation, polarity, and propagation time to classify the severity and type of event.
23. The method according to claim 1, wherein the pattern detection comprises analyzing one or more of: temporal patterns of signal anomalies across predefined intervals, frequency domain characteristics to identify fault-related harmonics, and spatial patterns of events across geographic regions to correlate anomalies with specific grid components.
24. The method according to claim 1, wherein the results of pattern detection and traveling wave analysis are combined to refine the faultlocation and provide a confidence score for the prediction.
25. The method according to claim 1, wherein the alert (100) includes a risk index and an urgency index derived from the strength, frequency, and recurrence of patterns identified by pattern detection, and the precision and severity indicated by traveling wave analysis.
26. The method according to any of the preceding claims, wherein the alert includes a topology map of the electric grid with: fault locations indicated based on traveling wave analysis, and regions of concern highlighted based on pattern detection.
27. The method according to any of the preceding claims, wherein the alert dynamically updates the fault location and severity as additional data from traveling wave analysis and pattern detection are received.
28. The method according to any of the preceding claims, wherein the alert further includes a list (124), wherein each entry of the list includes a component of the one or more components (106) in which an event of the one or more events (102) is detected, a type (126) of the event, a time-instance (128) of occurrence of the event, and a geolocation at which the event is detected, wherein the time-instance is within the timerange or the time-interval.
29. The method according to claim 28, wherein the visual alert further reflects an event recurrence pattern.
30. The method according to any of the preceding claims, wherein the one or more user inputs are indicative of a type of event or a component of the electric grid, wherein the one or more events of the type are detected at the one or more geolocations, wherein the one or more events (102) are detected in the component, wherein the list (124) includes one or more entries and each entry of the one or more entries includes at least one of: an event of the one or more events of the typeand the component.
31. The method according to any of the preceding claims, wherein the map (110) further includes weather data at, or in vicinity of, the one or more geolocations corresponding to the one or more locations (104), wherein the histogram (120) includes the weather data at the one or more locations during each time-instance of the set of time-instances, wherein each entry of the list (124) includes the weather data at the geolocation included in the corresponding entry, and wherein the weather data includes one or more of temperature, humidity, wind speed, lightning condition, and thunderstorm condition.
32. The method according to claim 31, wherein the weather data is dynamically linked to historical fault trends.
33. The method according to claim 1, wherein the method further comprises training a predictive machine learning model to execute at least one task, wherein the execution of the at least one task enables generation of the alert (100).
34. The method according to claim 33, wherein the at least one task comprises:classifying components, amongst a set of components in the electric grid, based on detection of the one or more events (102), wherein the classification is based training the predictive machine learning model on ground truth data that comprises a mapping between historical detections of events of the one or more events (102) and the set of components (106) in which the events are detected; orpredicting probability of occurrence of the one or more events at one or more time-instances in the one or more locations (104) based on weather forecasts at the one or more locations, wherein the predictive machine learning model is trained on ground truth data that comprisescorrelation between historical detections of events of the one or more events and weather conditions that are similar to the weather forecast.
35. A system (800) for predicting faults in an electric grid, the system comprising a processor (808) and a display device (810),wherein the processor is operable to:receive signals propagating through a distribution network or a transmission network of the electric grid;detect one or more events (102) that are precursors of one or more faults in the electric grid, wherein the detection is based on at least one of parameters associated with the signals or radiation produced by operation of components in the distribution network or the transmission network;determine one or more locations (104), in the distribution network or the transmission network, where the one or more events are detected, wherein, the determination is based on one or more of: a pattern of detection of the one or more events, weather conditions, traveling wave analysis, or location-specific sensor measurements;generate an alert (100) that includes the one or more events, the one or more locations, and one or more components (106) of the electric grid in which the one or more events are detected, wherein the alert corresponds to predictions of occurrences of the one or more faults; andcontrol the display device to display the alert.
36. The system according to claim 35, wherein the processor is further operable to:analyze historical and real-time data of the detected events to identify patterns of recurring anomalies, wherein the patterns areevaluated over predefined time intervals and geographic regions within the electric grid.
37. The system according to claim 35, wherein the processor is further operable to:5 generate a visual representation of the fault location and itsassociated risk and urgency indices on a map.
38. The system according to claim 35, wherein the determination of the one or more locations where the events are detected is further based on weather conditions correlated to event occurrences, traveling wave 10 analysis for identifying fault locations by analyzing wave propagation characteristics caused by precursor events, or location-specific sensor measurements.
39. A computer program product for detecting and predicting faults in an electric grid, wherein the computer program product comprises 15 instructions which, when executed by a computer, causes the computer to detect and predict faults in the electric grid according to the method of any one of claims 1-34.