A ring main unit fault rapid early warning method and system

Abstract

The application relates to the technical field of ring main unit fault early warning, and discloses a ring main unit fault rapid early warning method and system. The method acquires ring main unit topological relation data and multi-source operation data, and obtains state monitoring data through preprocessing; historical normal data is matched to generate a working condition baseline according to operation condition information, and operation disturbance is identified to obtain abnormal event data; the abnormal event data is matched with signal propagation paths of each functional compartment to construct a fault evidence chain; the fault type, fault position and development stage are determined according to the fault evidence chain to generate initial early warning data; when the verification condition is met, the target fault compartment and the adjacent compartments thereof are subjected to encryption sampling, the fault evidence chain is updated, the early warning state is determined, and the fault early warning result is obtained. The application can distinguish operation disturbance from a real fault, determine the fault compartment, the fault type and the development stage, and quickly confirm or correct the early warning through local encryption sampling.

Description

Technical Field

[0001] This invention relates to the field of ring main unit fault early warning technology, and in particular to a rapid fault early warning method and system for ring main unit. Background Technology

[0002] Ring main units in urban underground power distribution rooms and industrial park cable ring networks are subject to long-term load fluctuations, changes in air humidity, condensation, and frequent switching operations. They are prone to partial discharge, abnormal heating of connection parts, and abnormal insulation due to moisture. Rapid early warning is required without power interruption.

[0003] Existing technologies typically set up monitoring devices for partial discharge, temperature, humidity, etc., and use fixed thresholds to determine whether the monitored quantities are abnormal. When the limits are exceeded, alarms such as partial discharge abnormality or temperature abnormality are output for maintenance personnel to retest and troubleshoot.

[0004] However, existing technologies have obvious limitations in practical applications. On the one hand, a single sensor with a fixed threshold cannot distinguish between real insulation discharge, abnormal heating and switching transient interference, resulting in false alarms and missed alarms. On the other hand, multi-source signals do not combine the physical adjacency and propagation relationship of each functional compartment, making it difficult to quickly determine the faulty compartment, fault type and development stage. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and system for rapid early warning of ring main unit faults, aiming to solve at least one of the problems in the background art.

[0006] The first aspect of the present invention is to provide a method for rapid early warning of ring main unit faults, the method comprising: acquiring ring main unit topology data and multi-source operation data, preprocessing the multi-source operation data to obtain status monitoring data; Based on the operating condition information in the status monitoring data, historical normal data is matched to generate an operating condition baseline. The status monitoring data is compared with the operating condition baseline and operating disturbances are identified to obtain abnormal event data. Based on the ring main unit topology data, the abnormal event data is matched with the signal propagation path corresponding to each functional compartment to obtain the fault association results corresponding to each functional compartment, and a fault evidence chain is constructed based on the fault association results. Based on the fault evidence chain, the fault type, fault location and development stage are determined, the target fault compartment is identified and initial early warning data is generated; When the initial warning data meets the verification conditions, encrypted sampling is performed on the target fault compartment and its adjacent compartments. The fault evidence chain is updated based on the encrypted sampling results, and the initial warning data is judged based on the updated fault evidence chain to obtain the fault warning result.

[0007] Furthermore, the condition monitoring data is compared with the operating baseline and operational disturbances are identified to obtain abnormal event data, including: The monitored quantities in the condition monitoring data are compared with the corresponding operating condition baseline. When any monitored quantity continuously deviates from the corresponding operating condition baseline, a candidate abnormal event window is established. Based on the correspondence between abnormal pulses in the candidate abnormal event window and the timing of switch action, the compartment distribution and duration of abnormal pulses, switch operation disturbances are identified, and operational disturbance identification results are obtained. Based on the operational disturbance identification results, switch operation disturbances are eliminated, and anomalies that repeatedly occur within multiple consecutive event windows or repeatedly cluster within the same AC phase interval are retained to obtain anomaly event data.

[0008] Further steps for determining the switching operation disturbance include: The abnormal pulses in the candidate abnormal event window are correlated with the switching action times, and the occurrence range and duration of the abnormal pulses in each functional compartment are determined to obtain the disturbance characteristic results. When the disturbance characteristic results indicate that the abnormal pulse coincides with the switching action time, multiple functional compartments experience wide-range pulses nearly simultaneously, and the abnormal pulse disappears within a preset short period of time, the candidate abnormal event is identified as a switching operation disturbance.

[0009] Furthermore, the abnormal event data is matched with the signal propagation paths corresponding to each functional compartment to obtain the fault correlation results corresponding to each functional compartment, including: Based on the ring main unit topology data, the signal propagation path and expected amplitude attenuation sequence from each functional compartment to the corresponding sensor are determined. The sensor signals in the abnormal event data are matched with the signal propagation paths to obtain the path matching results. By comparing the sensor signal amplitude in the path matching results with the expected amplitude attenuation order, the correlation between the abnormal event data and each functional compartment is determined, and the fault correlation results are obtained.

[0010] Further steps in determining the expected amplitude decay order include: Based on the metal partitions, busbar connections, and cable connections between adjacent functional compartments, as well as the installation compartments of each sensor, each functional compartment is used as a candidate fault source. The signal propagation path from the candidate fault source to each sensor is determined, and the expected amplitude attenuation order is determined according to the order of each sensor along the signal propagation path from near to far.

[0011] Furthermore, determining the correlation between the abnormal event data and each functional compartment includes: According to the expected amplitude attenuation order, the signal amplitudes of the corresponding sensors in adjacent propagation levels are compared sequentially. When the amplitude of the sensor signal near the candidate fault source is higher than that of the sensor signal far from the candidate fault source, a support association is established between the corresponding abnormal event and the functional compartment where the candidate fault source is located; otherwise, a conflict association is established, resulting in a fault association result that includes the functional compartment identifier, support association, and conflict association.

[0012] Furthermore, the fault evidence chain is updated based on the encrypted sampling results, including: Based on the encrypted sampling results, the evidence items in the fault evidence chain corresponding to the target fault compartment are reproduced to obtain the evidence verification results; Based on the evidence review results, the reproduced evidence items are identified as supporting evidence, the non-reproduced evidence items are identified as conflicting evidence, and the corresponding evidence durations are updated to obtain the updated faulty evidence chain.

[0013] Furthermore, the initial warning data is used to determine the warning status based on the updated fault evidence chain, including: The updated fault evidence chain is compared with the original fault evidence chain to generate early warning verification data. The warning status of the initial warning data is adjusted based on the warning verification data to obtain the fault warning result.

[0014] Furthermore, the updated fault evidence chain is compared with the original fault evidence chain to generate early warning verification data, including: Based on the candidate fault source and evidence type, the evidence items in the fault evidence chain before and after the update are matched to obtain the evidence matching result; Based on the evidence correspondence results, the number of supporting evidences, the number of conflicting evidences, the duration of evidence, and the intensity of anomalies corresponding to each candidate fault source before and after the update are compared, and the fault locations before and after the update are compared to generate early warning verification data.

[0015] The second aspect of the present invention provides a rapid early warning system for ring main unit faults, used to implement the method described in the first aspect, the system comprising: a data acquisition module, used to acquire topology relationship data and multi-source operation data, and generate status monitoring data; The event recognition module is used to generate operating condition baselines and identify operational disturbances to obtain abnormal event data; The evidence analysis module is used to match signal propagation paths and construct a chain of evidence of failure. The early warning determination module is used to determine the fault type, location, and development stage, and generate initial early warning data; The verification control module is used to perform encrypted sampling, update the fault evidence chain, and generate fault warning results.

[0016] A third aspect of the present invention provides an electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the rapid early warning method for ring main unit faults as described in the first aspect.

[0017] A fourth aspect of the present invention provides an electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the rapid early warning method for ring main unit faults as described in the first aspect.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention generates an operating baseline by matching historical normal data with current load, temperature and humidity and switch status, and identifies operating disturbances by combining the persistence, phase characteristics and correspondence with switch actions of abnormal signals. This enables the anomaly judgment to adapt to changes in the operating conditions of the ring main unit, thereby distinguishing between normal load temperature rise, humid background noise, transient interference from switches and real fault anomalies, and reducing false alarms and missed alarms caused by fixed threshold judgment.

[0019] 2. This invention matches abnormal events with the topological relationship and signal propagation path of each functional compartment of the ring main unit to construct a fault evidence chain containing supporting and conflicting evidence. Based on this, the fault compartment, fault type and development stage are determined. The local encrypted sampling verification and early warning of the target fault compartment and adjacent compartments are used to narrow the scope of on-site retesting and improve the timeliness of fault location and early warning confirmation. Attached Figure Description

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of a rapid early warning method for ring main unit faults provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a structural block diagram of a ring main unit fault rapid early warning system provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0023] Example 1

[0024] According to an embodiment of the present invention, a method for rapid early warning of ring main unit faults is provided. Please refer to the following embodiment. Figure 1 , Figure 1 The flowchart of a rapid early warning method for ring main unit faults provided in Embodiment 1 of the present invention is shown, specifically including steps S1 to S5.

[0025] Step S1: Obtain the ring network cabinet topology data and multi-source operation data, and preprocess the multi-source operation data to obtain status monitoring data.

[0026] Specifically, the cabinet number, busbar connection relationship, incoming and outgoing line relationship of each ring main unit are obtained, as well as the spatial adjacency relationship between the busbar compartment, switch compartment and cable compartment. The installation positions of each partial discharge sensor, ultrasonic sensor, temperature sensor, temperature and humidity sensor, current acquisition terminal and switch status acquisition terminal are recorded to form the ring main unit topology data.

[0027] The ring main unit topology data includes at least the ring main unit identifier, functional compartment identifier, adjacent functional compartment identifier, connection type between compartments, and sensor installation compartment. Connection types include metal plate connections, busbar connections, and cable connections, used in subsequent steps to determine the path taken by abnormal signals as they propagate from candidate fault compartments to each sensor.

[0028] In this embodiment, each functional compartment can be set as a compartment node, and the metal partitions, busbar connections, or cable connections between adjacent compartments can be set as the connection relationships between compartment nodes. For example, the third unit cable compartment is adjacent to the third unit switch compartment, and the third unit switch compartment is adjacent to the corresponding busbar compartment, thereby forming a topological relationship between the third unit cable compartment, switch compartment, and busbar compartment.

[0029] Furthermore, multi-source operational data is acquired. Partial discharge sensors collect partial discharge pulse amplitude, pulse count, pulse occurrence time, and phase distribution; ultrasonic sensors collect ultrasonic energy and ultrasonic pulse occurrence time; temperature sensors collect the temperature of cable terminal connection points, busbar connection points, or cabinet surface; temperature and humidity sensors collect the air temperature and relative humidity inside the cabinet; current acquisition terminals collect the load current of each circuit; and switch status acquisition terminals collect the status and operation time of load switches, circuit breakers, or grounding switches.

[0030] The edge monitoring terminal adds a unified timestamp to various multi-source operational data. The unified timestamp can be generated by the same system clock of the edge monitoring terminal, or it can be corrected according to the receiving time when the collected data enters the edge monitoring terminal, so that different types of sensor data can be correlated according to time.

[0031] When preprocessing multi-source operational data, invalid values ​​are first removed based on the sensor's effective range and data quality indicators. Individual data points that significantly exceed the sensor's effective range and show no corresponding change in adjacent sampling times can be marked as acquisition anomalies and not used for subsequent fault diagnosis.

[0032] For data with a continuous missing period less than the preset missing duration, it can be supplemented using valid data before and after the missing position. For data with a missing duration exceeding the preset missing duration, no forced supplementation is performed; instead, the corresponding time period is marked as incomplete data. The preset missing duration is determined based on the sensor's regular sampling period, for example, it can be set to no more than two regular sampling periods to avoid incorrect extrapolation of data with long periods of missing data.

[0033] Furthermore, the dimensions of the data output from different sensors are unified, and the ring main unit identifier, functional compartment identifier, sensor type, acquisition time, monitoring value, and data quality identifier are associated to obtain status monitoring data.

[0034] Condition monitoring data serves as input for generating the operating condition baseline and identifying abnormal events in step S2. By unifying multi-source operating data onto the same timeline and linking it to specific functional compartments in this step, a basis can be provided for subsequent determination of whether different monitoring signals occur simultaneously in the same time and adjacent spaces.

[0035] Step S2: Based on the operating condition information in the status monitoring data, match historical normal data to generate an operating condition baseline. Compare the status monitoring data with the operating condition baseline and identify operating disturbances to obtain abnormal event data.

[0036] Specifically, the current load current, cabinet temperature, cabinet humidity, and switch operating status are extracted from the condition monitoring data as operating condition information.

[0037] Historical normal operation data originates from status monitoring data generated when the ring main unit is operating normally. This data can be confirmed by maintenance personnel through on-site inspections, portable testing, and maintenance records. In this embodiment, the edge monitoring terminal stores data confirmed to be in a normal operating state for the past fourteen days and updates it as new normal operation data is generated.

[0038] Furthermore, historical normal data is grouped according to load current range, cabinet humidity range, cabinet temperature range, and switch operating status. The boundaries of each range can be preset based on the ring main unit's rated load, the distribution of historical data on site, and the environmental operating range. For example, low load, medium load, and high load conditions can be set as different load current ranges, and dry, normal humidity, and high humidity conditions can be set as different humidity ranges.

[0039] For each operating condition group, the range of partial discharge pulse count, amplitude, ultrasonic energy, temperature rise range corresponding to the load, and humidity change range under normal conditions are recorded to form the historical normal data corresponding to that operating condition group.

[0040] When processing current status monitoring data, first determine the current operating condition group based on the current load current, cabinet temperature and humidity, and switch operating status, and then extract data from the same operating condition group from historical normal data to generate an operating condition baseline.

[0041] The operating condition baseline is not a fixed alarm threshold, but rather a normal comparison range for each monitored quantity under the current operating condition. When the number of normal samples in a certain operating condition group is less than the preset sample number, that operating condition group is merged with the adjacent operating condition group whose load current and cabinet humidity are closest. The preset sample number is calibrated based on the requirement that the operating condition baseline can cover multiple normal operating cycles to avoid the baseline being formed by a small amount of incidental data.

[0042] Furthermore, each monitored quantity in the condition monitoring data is compared with the corresponding operating condition baseline. When any monitored quantity, such as the number of partial discharge pulses, the amplitude of partial discharge pulses, the ultrasonic energy, the rate of temperature rise, or the temperature difference corresponding to the load, continuously deviates from the corresponding operating condition baseline, a candidate abnormal event window is established.

[0043] The candidate anomaly event window is used to aggregate abnormal monitoring values, corresponding functional compartments, sensor types, anomaly occurrence times, and anomaly durations within the same continuous time period. In this embodiment, the event window can be set to one minute, consistent with the statistical period of partial discharge pulse counts. When an anomaly occurs only at a single sampling moment and does not persist until the effective duration requirement of the event window is met, it is not directly retained as a fault anomaly.

[0044] Furthermore, based on the correspondence between the abnormal pulses in the candidate abnormal event window and the timing of the switch action, as well as the distribution range and duration of the abnormal pulses in each functional compartment, switch operation disturbances are identified, and operational disturbance identification results are obtained.

[0045] Specifically, the occurrence time of the abnormal pulse is correlated with the action time recorded by the switch status acquisition terminal. The allowable time difference between the abnormal pulse and the switch action time is calibrated based on a unified clock error, sensor response delay, and a normal sampling period. When the time difference does not exceed the allowable time difference, the abnormal pulse and the switch action time are considered to coincide.

[0046] Furthermore, it is determined whether the partial discharge sensors in multiple functional compartments exhibit a wide range of pulses nearly simultaneously within an allowable time difference, and whether the abnormal pulses disappear within a preset short period of time. The preset short period of time can be determined based on the longest duration of transient pulses in normal opening and closing operation samples, and in this embodiment, it can be set to no more than one event window.

[0047] Simultaneously, partial discharge pulses and ultrasonic pulses in the candidate abnormal event window are matched according to their occurrence time. If the partial discharge pulse and ultrasonic pulse co-occur continuously within the allowable time difference, it indicates that the two form a continuous correspondence; if only an occasional ultrasonic response exists after the occurrence of the partial discharge pulse, or if the two do not co-occur within the continuous sampling period, it is determined that the ultrasonic pulse does not form a continuous correspondence.

[0048] When an abnormal pulse coincides with the switching action, multiple functional compartments experience wide-range pulses nearly simultaneously, the abnormal pulse disappears within a preset short period, and the ultrasonic pulses do not form a continuous correspondence, the corresponding candidate abnormal event is identified as a switching operation disturbance.

[0049] Based on the operational disturbance identification results, switching operation disturbances are removed from the candidate abnormal events. For candidate abnormal events that are not identified as switching operation disturbances, if they repeatedly occur in multiple consecutive event windows or repeatedly cluster in the same AC phase interval, they are retained as abnormal event data.

[0050] The abnormal event data includes at least the abnormal event identifier, the time period of the abnormality, the abnormal monitoring quantity, the abnormality intensity, the corresponding functional compartment, the phase distribution, the associated sensors, and the operational disturbance identification results, which are used as inputs for matching the signal propagation path in step S3.

[0051] For example, during a normal opening and closing operation, multiple partial discharge sensors in the cable compartment, switch compartment, and busbar compartment of Unit 3 simultaneously emitted high-amplitude pulses. The pulse occurrence time coincided with the switch state change time, the duration was less than one event window, and the ultrasonic sensor did not continuously collect the ultrasonic pulses corresponding to the partial discharge pulses. Based on this, the edge monitoring terminal identified this candidate abnormal event as a switch operation disturbance and did not input it into the subsequent fault evidence chain construction process.

[0052] For example, as exemplary implementation data, in the early morning of the same day, the relative humidity inside the cabinet rose to approximately 86%, and the number of partial discharge pulses in the cable compartment of the third unit gradually increased from approximately 15 times per minute in the corresponding operating condition baseline to approximately 48 times per minute, appearing consecutively in two event windows. The corresponding ultrasonic sensors continuously acquired the corresponding ultrasonic pulses within the allowable time difference, and this anomaly did not coincide with the switching action time. Therefore, this candidate anomaly event was retained as anomaly event data.

[0053] This step identifies operational disturbances by first matching historical normal data under the current operating conditions, and then combining the timing of switch action, spatial distribution of abnormalities, duration of abnormalities, and continuous ultrasonic response. This can reduce the chances of misjudging normal temperature rise under high load, humid background noise, and transient pulses from switches as real faults.

[0054] Step S3: Based on the ring network cabinet topology data, match the abnormal event data with the signal propagation paths corresponding to each functional compartment to obtain the fault association results corresponding to each functional compartment, and construct a fault evidence chain based on the fault association results.

[0055] Specifically, the busbar compartment, switch compartment, and cable compartment are selected as candidate fault sources, and the signal propagation path from each candidate fault source to each sensor is determined based on the ring main unit topology data.

[0056] The signal propagation path is determined based on the metal partitions, busbar connections, and cable connections between adjacent functional compartments, as well as the sensor installation compartment. For example, when the third unit cable compartment is considered as a candidate fault source, the sensor located in the third unit cable compartment is at the first propagation level, the sensor located in the adjacent switch compartment is at a subsequent propagation level, and the sensor located in the busbar compartment is at a more distant propagation level.

[0057] Furthermore, the expected amplitude attenuation order is determined according to the order in which each sensor is located relative to the candidate fault source along the signal propagation path, from near to far. The expected amplitude attenuation order indicates that as the abnormal signal propagates outward from the candidate fault source, sensors closer to the candidate fault source should generally obtain higher signal amplitudes, while sensors farther away from the candidate fault source should generally obtain lower signal amplitudes.

[0058] The expected amplitude attenuation order is only used to determine the relative order of amplitudes among the sensors, and does not require different types of sensors to have the same absolute amplitude. Partial discharge signals and ultrasonic signals are compared according to the same type of sensor, thereby avoiding the influence of differences in the dimensions of different sensors on the judgment results.

[0059] The sensor signals in the abnormal event data are mapped to the signal propagation paths of each candidate fault source according to the sensor identifier and installation compartment, resulting in path matching results. The path matching results indicate which candidate propagation path and propagation level the sensor involved in the abnormal event is located on.

[0060] Furthermore, the actual signal amplitudes of the corresponding sensors at adjacent propagation levels are compared sequentially according to the expected amplitude attenuation order. When the sensor signal amplitude closer to the candidate fault source is higher than that of the sensor signal amplitude farther away from the candidate fault source, a support association is established between the corresponding abnormal event and the functional compartment where the candidate fault source is located; when the actual amplitude order is inconsistent with the expected amplitude attenuation order, a conflict association is established.

[0061] Based on the supporting and conflicting associations formed between each abnormal event and the candidate fault source, the fault association results for each functional compartment are obtained. The fault association results include at least the candidate fault source identifier, abnormal event identifier, signal propagation path, supporting association, and conflicting association.

[0062] For example, as exemplary implementation data, when an anomaly occurs in the third unit cable compartment, after normalizing the amplitudes of the partial discharge sensors of the same type, the relative amplitude of the sensor in the third unit cable compartment is 1.00, the relative amplitude of the sensor in the adjacent switch compartment is 0.62, and the relative amplitude of the sensor in the busbar compartment is 0.18. This actual amplitude order is consistent with the expected amplitude attenuation order formed when the third unit cable compartment is the candidate fault source. Therefore, the anomaly event forms a supporting correlation with the third unit cable compartment.

[0063] When the third unit switch compartment is considered as a candidate fault source, it is expected that the sensor amplitudes near the switch compartment should be higher than those in the cable compartment. However, the actual comparison results do not meet this expected order, thus creating a conflicting association between the abnormal event and the third unit switch compartment. By performing the above matching separately for each functional compartment, the fault location can be avoided by directly determining it based solely on a single maximum amplitude value.

[0064] Furthermore, a chain of evidence for the failure is constructed based on the failure correlation results. For each candidate failure source, the associated anomalous events, supporting correlations, conflicting correlations, and evidence durations are organized in chronological order.

[0065] When constructing the fault evidence chain, the following should be recorded: whether the partial discharge pulse and the ultrasonic pulse occur together within the allowable time difference; whether the phase distribution of the partial discharge remains similar in multiple cycles; whether the actual amplitude meets the expected amplitude decay sequence; whether the temperature rise corresponds to the change in load current; whether the increase in humidity precedes the enhancement of partial discharge; and whether the abnormality coincides with the switch action.

[0066] Evidence items that satisfy the correspondence are recorded as supporting evidence, while evidence items that do not satisfy the correspondence are recorded as conflicting evidence. The duration of evidence is determined based on the number of consecutive event windows in which the corresponding evidence items appear. This results in a fault evidence chain that includes candidate fault sources, supporting evidence, conflicting evidence, and evidence duration.

[0067] In the above-mentioned example of anomalies in the cable compartment of Unit 3, the relative humidity first increased to approximately 86%, and then the number of partial discharge pulses increased from approximately 15 per minute to approximately 48 per minute; the partial discharge pulses and ultrasonic pulses continuously corresponded; the sensor amplitudes conformed to a progressively decreasing sequence from the cable compartment to the switch compartment and busbar compartment; and the connection point temperature did not abnormally increase with the load current. This forms a fault evidence chain from increased humidity to enhanced partial discharge to corresponding ultrasonic pulses and then to consistent spatial amplitude attenuation.

[0068] This step matches the abnormal event with the signal propagation path under the actual physical structure of the ring main unit. It not only determines whether the monitored quantity is abnormal, but also determines whether the spatial distribution of the abnormal signal conforms to the pattern of propagation from the candidate fault compartment outward. This provides a basis for step S4 to jointly determine the fault location, fault type and development stage.

[0069] Step S4: Based on the fault evidence chain, determine the fault type, fault location and development stage, identify the target fault compartment and generate initial warning data.

[0070] Specifically, the fault evidence chain corresponding to each candidate fault source is matched with the preset fault discrimination rules.

[0071] When the partial discharge pulse and the ultrasonic pulse correspond continuously, the phase distribution of the partial discharge remains similar over multiple cycles, and the amplitude of the sensor signal conforms to the order of gradual attenuation from the candidate fault compartment to the adjacent compartment, the fault type is determined to be the insulation partial discharge of the corresponding functional compartment.

[0072] When the temperature at the connection point continuously deviates from the temperature of similar connection points as the load current increases, and no stable partial discharge and ultrasonic supporting evidence are formed in the fault evidence chain, the fault type is determined to be abnormal heating of the connection part.

[0073] When partial discharge gradually increases after the humidity inside the cabinet rises or the risk of condensation occurs, and then weakens after the humidity decreases, the fault type is determined to be an insulation abnormality induced by moisture.

[0074] When an anomaly occurs only at the moment of switching action and no further supporting evidence is formed in the fault evidence chain, it is identified as a switching operation disturbance. Switching operation disturbances that have been identified and eliminated in step S2 will not be included in the fault location and development stage determination.

[0075] Furthermore, the number of supporting evidence and conflicting evidence corresponding to each candidate fault source is counted. The candidate fault sources are sorted in order of the number of supporting evidence from most to least and the number of conflicting evidence from least to most. The candidate fault source ranked first is determined as the target fault compartment.

[0076] If the number of supporting and conflicting pieces of evidence for multiple candidate fault sources is the same, it indicates that the existing fault evidence chain cannot yet form a unique fault location. In this case, instead of forcibly outputting a unique target fault compartment, subsequent status monitoring data is continued to be acquired until new supporting or conflicting evidence that can distinguish candidate fault sources is formed, in order to avoid making incorrect location when evidence is insufficient.

[0077] After identifying the target fault compartment, the development stage is determined based on the fault evidence chain corresponding to the target fault compartment. The criteria for determining the development stage include the number of evidence duration windows, the degree of deviation of the abnormal intensity from the baseline operating conditions, the trend of abnormal intensity changes, the number of supporting evidence types, and the number of affected compartments.

[0078] The latent stage is defined as the intermittent occurrence of anomalies, the small number of continuous windows, and the lack of significant increase in anomaly intensity. The development stage is defined as the continuous occurrence of anomalies within multiple event windows, the stable location of the fault, and the continuous increase in anomaly intensity. The emergency stage is defined as the rapid increase in anomaly intensity, the simultaneous deterioration of multiple supporting evidence, or the expansion of anomalies to adjacent functional compartments.

[0079] The thresholds for each stage can be calibrated based on confirmed fault events, on-site maintenance results, and normal operation data. During calibration, the number of persistent windows, the degree of deviation from the baseline operating condition, and the number of affected compartments in historical events are correlated with the final maintenance results to determine the boundaries between latent, developing, and emergency stages. When historical samples are insufficient, initial boundaries can be set using operation and maintenance rules, and then adjusted based on subsequent on-site verified fault events.

[0080] Furthermore, initial early warning data is generated based on the fault type, target fault compartment, and development stage. The initial early warning data includes at least the ring main unit number, target fault compartment, fault type, development stage, main supporting evidence, main conflicting evidence, and recommended retest items.

[0081] In this example, the fault evidence chain corresponding to the third unit cable compartment has supporting evidence such as initial increase in humidity, enhanced partial discharge, continuous ultrasonic correlation, and consistent spatial amplitude attenuation, and the connection point temperature did not show an abnormal increase corresponding to the load. Therefore, the fault type is determined to be moisture-induced insulation partial discharge.

[0082] The third unit cable compartment has more supporting evidence than the switch compartment and busbar compartment, and the fewest conflicting pieces of evidence. Therefore, the third unit cable compartment is identified as the target fault compartment. Since the anomaly appears continuously within multiple event windows, the fault location remains stable, and the number of partial discharge pulses continues to rise, it is identified as the development stage, and corresponding initial warning data is generated.

[0083] This step can output the fault type, specific functional compartment and development stage in the same judgment process, so that the early warning result is transformed from a general partial discharge anomaly into an early warning information that can directly guide on-site retesting, reducing the scope of maintenance personnel's cabinet-by-cabinet-by-compartment inspection.

[0084] Step S5: When the initial warning data meets the verification conditions, encrypted sampling is performed on the target fault compartment and its adjacent compartments. The fault evidence chain is updated based on the encrypted sampling results. The initial warning data is then used to determine the warning status based on the updated fault evidence chain to obtain the fault warning result.

[0085] Specifically, the development stage in the initial warning data is read. When the development stage is either the development stage or the emergency stage, the initial warning data is deemed to meet the verification conditions, and the adjacent compartments of the target fault compartment are determined based on the ring network cabinet topology data.

[0086] The sensors corresponding to the target fault compartment and its adjacent compartments are identified as encrypted sampling sensors. During a preset verification period, the sampling frequency of the encrypted sampling sensors is increased, while the normal sampling frequency of the sensors corresponding to other functional compartments is maintained, thus obtaining encrypted sampling results.

[0087] The encrypted sampling frequency is pre-configured based on the sensor's rated sampling capacity, the edge monitoring terminal's processing capacity, and the communication bandwidth; its sampling frequency only needs to be higher than the conventional sampling frequency. The preset verification period is determined based on the time during which faults and anomalies can recur under normal operating conditions and the requirements for operation and maintenance early warning response.

[0088] In this embodiment, the preset verification period is exemplarily set to 15 minutes. Only the sampling frequency of the partial discharge sensor and ultrasonic sensor in the third unit cable compartment and its adjacent switch compartment is increased, while the sensors in the busbar compartment and other ring main unit units continue to maintain the normal sampling frequency.

[0089] Furthermore, based on the encrypted sampling results, the evidence items corresponding to the target fault compartment in the original fault evidence chain are reproduced and judged to obtain the evidence verification results.

[0090] Among them, evidence reproduction refers to the reappearance of the corresponding abnormal relationship in the original fault evidence chain within a preset verification period. For example, if there is evidence of a continuous correspondence between partial discharge pulses and ultrasonic pulses in the original fault evidence chain, then it is determined whether the two will appear continuously together again within the allowable time difference in the encrypted sampling results; if there is evidence of spatial amplitude attenuation in the original fault evidence chain, then it is determined whether the actual amplitude order in the encrypted sampling results continues to conform to the expected amplitude attenuation order.

[0091] Based on the evidence review results, the reproduced evidence items are identified as supporting evidence, and the evidence items that are not reproduced or have the opposite relationship to the original are identified as conflicting evidence. The duration of the evidence is updated according to the continuous occurrence of the evidence items during the verification period, resulting in an updated fault evidence chain.

[0092] Furthermore, the updated fault evidence chain is compared with the original fault evidence chain. During the comparison, the evidence items before and after the update are matched according to the candidate fault source and evidence type to obtain the evidence matching results.

[0093] Based on the evidence correspondence results, compare the number of supporting evidence, the number of conflicting evidence, the duration of evidence and the intensity of anomalies for each candidate fault source before and after the update, and compare whether the fault location is consistent before and after the update to generate early warning verification data.

[0094] In this context, whether the fault location is consistent refers to whether the candidate fault source with the most supporting evidence and the fewest conflicting evidence before and after the update is the same functional compartment. Changes in anomaly intensity can be determined by the deviation of the same monitoring quantity from the corresponding operating condition baseline before and after the update.

[0095] The initial warning data is assessed for its warning status based on the warning verification data. The initial warning data is confirmed when the updated fault evidence chain adds supporting evidence, the anomaly intensity continues to rise, or the fault location remains stable. The warning level is raised when the anomaly intensity increases to the threshold for a higher warning level.

[0096] When the anomaly disappears rapidly during the encrypted sampling period and the main supporting evidence in the original fault evidence chain fails to reproduce, the warning level is reduced; when the anomaly only appears briefly and has a clear correspondence with the switch operation, the initial warning data is converted into an operational disturbance record.

[0097] In this embodiment, after 15 minutes of encrypted sampling, the partial discharge pulse and the ultrasonic pulse continue to correspond in the third unit cable chamber and its adjacent switch chamber. The sensor amplitude in the third unit cable chamber is still higher than the sensor amplitude in the adjacent switch chamber, and the target fault compartment remains the third unit cable chamber.

[0098] As exemplary implementation data, the number of partial discharge pulses can be increased from approximately 48 pulses per minute before encrypted sampling to approximately 55 pulses per minute. The spatial amplitude attenuation evidence and the corresponding ultrasonic evidence in the fault evidence chain are both reproduced. After comparing the fault evidence chain before and after the update, it is determined that the supporting evidence has not decreased, the abnormal intensity continues to increase, and the fault location remains stable. Therefore, the initial warning data is confirmed and a secondary fault warning result is generated.

[0099] The fault warning results include the ring main unit number, target fault compartment, fault type, warning level, development stage, main supporting evidence, and recommended retesting content. For the example above, suggestions can be output to check the moisture level of the cable termination surface, the contamination of the insulation, and the grounding connection status of the third unit.

[0100] After completing the early warning verification, the corresponding sensors in the target fault compartment and adjacent compartments are restored to their normal sampling frequencies. This allows for a localized increase in sampling frequency only when significant anomalies meet the verification criteria. This enables rapid confirmation or correction of early warnings through high-density retest data, while avoiding a continuous increase in communication data volume and edge computing load caused by long-term high-frequency sampling of all sensors.

[0101] In summary, this embodiment uses historical normal data matching the operating conditions as the benchmark for anomaly judgment. It identifies operational disturbances by the timing of switch action, the duration of anomalies, and the correspondence between multi-source signals. Then, it matches the abnormal events with the signal propagation paths under the ring network cabinet topology to construct a fault evidence chain containing supporting and conflicting evidence. Finally, it uses local encrypted sampling to verify the initial warning, thereby achieving joint judgment of fault type, functional compartment, and development stage.

[0102] Example 2

[0103] This embodiment provides a rapid early warning system for ring main unit faults, used to execute the rapid early warning method for ring main unit faults described in Embodiment 1.

[0104] The system includes a data acquisition module, an event recognition module, an evidence analysis module, an early warning judgment module, and a verification control module. Each module can be implemented by the processor in the edge monitoring terminal executing the corresponding program, or it can be implemented by both the hardware circuitry and the program in the edge monitoring terminal.

[0105] The data acquisition module is used to acquire ring main unit topology data and multi-source operational data. It performs unified timestamp appending, outlier removal, short-term missing data completion, and dimensional standardization on the multi-source operational data to obtain status monitoring data. The data acquisition module is connected to a partial discharge sensor, an ultrasonic sensor, a temperature sensor, a temperature and humidity sensor, a current acquisition terminal, and a switch status acquisition terminal.

[0106] The event identification module is used to match historical normal data with the operating condition information in the condition monitoring data, generate an operating condition baseline, and compare the condition monitoring data with the operating condition baseline. The event identification module identifies operating disturbances and obtains abnormal event data based on the correspondence between abnormal pulses and switch action times, the compartment distribution of abnormal pulses, duration, and continuous ultrasonic correspondence.

[0107] The evidence analysis module determines the signal propagation path and expected amplitude attenuation sequence for each functional compartment based on the ring main unit topology data. It matches sensor signals from abnormal event data with the signal propagation paths and forms supporting or conflicting associations based on the consistency between the actual signal amplitude and the expected amplitude attenuation sequence, thus obtaining fault association results. The evidence analysis module further generates a fault evidence chain based on the fault association results, including candidate fault sources, supporting evidence, conflicting evidence, and the duration of the evidence.

[0108] The early warning determination module is used to determine the fault type, fault location and development stage based on the fault evidence chain, identify the candidate fault source with the most supporting evidence and the fewest conflicting evidence as the target fault compartment, and generate initial early warning data.

[0109] The verification control module performs encrypted sampling only on the target fault compartment and its adjacent compartments when the initial warning data meets the verification conditions. Based on the encrypted sampling results, it performs a reproduction judgment on the evidence items in the fault evidence chain. The verification control module updates supporting evidence, conflicting evidence, and evidence duration according to the evidence review results, compares the fault evidence chain before and after the update, generates warning verification data, and confirms, raises, lowers, or changes the initial warning state based on the warning verification data to obtain the fault warning result.

[0110] Data is transmitted between modules in the following order: status monitoring data, abnormal event data, fault correlation results, fault evidence chain, initial warning data, encrypted sampling results, and fault warning results, so that each processing result has a clear source of generation and subsequent use.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0112] Those skilled in the art should understand that the embodiments of the present invention can be implemented using a pure hardware architecture, a pure software architecture, or an integrated hardware and software architecture. The present invention can be prepared as a computer program product, which can be stored in various non-volatile computer-readable storage media, including but not limited to solid-state drives, flash memory chips, mobile storage devices, optical discs, cloud storage servers, and other standardized storage media, and is not limited to traditional storage media.

[0113] Based on the foregoing description in conjunction with the accompanying drawings, those skilled in the art will understand that the embodiments of this application can also be implemented by software programs. Therefore, this application also provides a computer-readable storage medium. This computer-readable storage medium stores computer-readable instructions thereon, which, when executed by one or more processors, implement the method described above in conjunction with the accompanying drawings.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] It should be noted that although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart can be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0116] It should be understood that when the terms "first," "second," "third," and "fourth," etc., are used in the claims, specification, and drawings of this application, they are used only to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" as used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0117] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0118] Although the embodiments of this application are as described above, the content is merely an example adopted for the purpose of facilitating understanding of this application and is not intended to limit the scope and application scenarios of this application. Any person skilled in the art described in this application may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for rapid early warning of ring main unit faults, characterized in that, include: Obtain ring main unit topology data and multi-source operation data, and preprocess the multi-source operation data to obtain status monitoring data; Based on the operating condition information in the status monitoring data, historical normal data is matched to generate an operating condition baseline. The status monitoring data is compared with the operating condition baseline and operating disturbances are identified to obtain abnormal event data. Based on the ring main unit topology data, the abnormal event data is matched with the signal propagation path corresponding to each functional compartment to obtain the fault association results corresponding to each functional compartment, and a fault evidence chain is constructed based on the fault association results. Based on the fault evidence chain, the fault type, fault location and development stage are determined, the target fault compartment is identified and initial early warning data is generated; When the initial warning data meets the verification conditions, encrypted sampling is performed on the target fault compartment and its adjacent compartments. The fault evidence chain is updated based on the encrypted sampling results, and the initial warning data is judged based on the updated fault evidence chain to obtain the fault warning result.

2. The method according to claim 1, characterized in that, The condition monitoring data is compared with the operating condition baseline and operational disturbances are identified to obtain abnormal event data, including: The monitored quantities in the condition monitoring data are compared with the corresponding operating condition baseline. When any monitored quantity continuously deviates from the corresponding operating condition baseline, a candidate abnormal event window is established. Based on the correspondence between abnormal pulses in the candidate abnormal event window and the timing of switch action, the compartment distribution and duration of abnormal pulses, switch operation disturbances are identified, and operational disturbance identification results are obtained. Based on the operational disturbance identification results, switch operation disturbances are eliminated, and anomalies that repeatedly occur within multiple consecutive event windows or repeatedly cluster within the same AC phase interval are retained to obtain anomaly event data.

3. The method according to claim 2, characterized in that, The steps for determining switching operation disturbances include: The abnormal pulses in the candidate abnormal event window are correlated with the switching action times, and the occurrence range and duration of the abnormal pulses in each functional compartment are determined to obtain the disturbance characteristic results. When the disturbance characteristic results indicate that the abnormal pulse coincides with the switching action time, multiple functional compartments experience wide-range pulses nearly simultaneously, and the abnormal pulse disappears within a preset short period of time, the candidate abnormal event is identified as a switching operation disturbance.

4. The method according to claim 1, characterized in that, By matching abnormal event data with the signal propagation paths corresponding to each functional compartment, fault correlation results for each functional compartment are obtained, including: Based on the ring main unit topology data, the signal propagation path and expected amplitude attenuation sequence from each functional compartment to the corresponding sensor are determined. The sensor signals in the abnormal event data are matched with the signal propagation paths to obtain the path matching results. By comparing the sensor signal amplitude in the path matching results with the expected amplitude attenuation order, the correlation between the abnormal event data and each functional compartment is determined, and the fault correlation results are obtained.

5. The method according to claim 4, characterized in that, The steps for determining the expected amplitude decay order include: Based on the metal partitions, busbar connections, and cable connections between adjacent functional compartments, as well as the installation compartments of each sensor, each functional compartment is used as a candidate fault source. The signal propagation path from the candidate fault source to each sensor is determined, and the expected amplitude attenuation order is determined according to the order of each sensor along the signal propagation path from near to far.

6. The method according to claim 4, characterized in that, Determining the correlation between the abnormal event data and each functional compartment includes: According to the expected amplitude attenuation order, the signal amplitudes of the corresponding sensors in adjacent propagation levels are compared sequentially. When the amplitude of the sensor signal near the candidate fault source is higher than that of the sensor signal far from the candidate fault source, a support association is established between the corresponding abnormal event and the functional compartment where the candidate fault source is located; otherwise, a conflict association is established, resulting in a fault association result that includes the functional compartment identifier, support association, and conflict association.

7. The method according to claim 1, characterized in that, Update the fault evidence chain based on the encrypted sampling results, including: Based on the encrypted sampling results, the evidence items in the fault evidence chain corresponding to the target fault compartment are reproduced to obtain the evidence verification results; Based on the evidence review results, the reproduced evidence items are identified as supporting evidence, the non-reproduced evidence items are identified as conflicting evidence, and the corresponding evidence durations are updated to obtain the updated faulty evidence chain.

8. The method according to claim 1, characterized in that, The initial warning data is used to determine the warning status based on the updated fault evidence chain, including: The updated fault evidence chain is compared with the original fault evidence chain to generate early warning verification data. The warning status of the initial warning data is adjusted based on the warning verification data to obtain the fault warning result.

9. The method according to claim 8, characterized in that, The updated fault evidence chain is compared with the original fault evidence chain to generate early warning verification data, including: According to the candidate fault source and evidence type, the evidence items in the fault evidence chain before and after the update are matched to obtain the evidence matching result; Based on the evidence correspondence results, the number of supporting evidences, the number of conflicting evidences, the duration of evidence, and the intensity of anomalies corresponding to each candidate fault source before and after the update are compared, and the fault locations before and after the update are compared to generate early warning verification data.

10. A rapid early warning system for ring main unit faults, characterized in that, The system for implementing the method as described in any one of claims 1-9 includes: The data acquisition module is used to acquire topological relationship data and multi-source operational data, and generate status monitoring data; The event recognition module is used to generate operating condition baselines and identify operational disturbances to obtain abnormal event data; The evidence analysis module is used to match signal propagation paths and construct a chain of evidence of failure. The early warning determination module is used to determine the fault type, location, and development stage, and generate initial early warning data; The verification control module is used to perform encrypted sampling, update the fault evidence chain, and generate fault warning results.

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