Insulation state evaluation method and system for medium and high voltage switch cabinet based on data fusion

Abstract

The application relates to the technical field of power equipment monitoring, and discloses a medium and high voltage switch cabinet insulation state evaluation method and system based on data fusion, which comprises the following steps: collecting local discharge pulse data, slowly changing state data and reference environment data of each evaluation subarea; performing time sequence correlation processing on the local discharge pulse data to obtain transient candidate events; performing first-time judgment according to time proximity relationship, space consistency relationship, propagation order relationship and phase aggregation relationship to obtain homologous discharge events and source evaluation subareas; calling the slowly changing state data to perform delay closing processing, performing second-time judgment on the processing result to obtain effective deterioration events, performing subarea sequence analysis on the effective deterioration events to obtain deterioration evolution results; and outputting the insulation state evaluation result of the source evaluation subarea according to the deterioration evolution results. The application improves the accuracy, stability and maintenance pertinence of insulation state evaluation and graded early warning.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and more specifically, to a method and system for assessing the insulation status of medium and high voltage switchgear based on data fusion. Background Technology

[0002] Medium and high voltage switchgear operates under conditions of high voltage, fluctuating temperature, alternating humidity and heat, and complex electromagnetic fields. Insulating components are prone to partial discharge and gradual insulation degradation due to manufacturing deviations, assembly defects, aging, moisture absorption, and operational stress. In severe cases, this can lead to power outages, breakdowns, or even equipment accidents. Therefore, conducting online monitoring, condition assessment, and risk warning for switchgear insulation effectiveness is a crucial technical direction in electrical equipment condition monitoring.

[0003] Existing technologies typically use ultra-high frequency (UHF), ultrasonic, transient ground voltage (TGF-β), and operational information such as ozone, temperature, and humidity to comprehensively assess the insulation status of switchgear, aiming to improve early anomaly detection capabilities and targeted maintenance. For example, Chinese invention patent application CN118444113A discloses the use of ultrasonic signals, transient ground voltage signals, and UHF electromagnetic wave signals to detect partial discharge in switchgear, and combines background value correction and status value judgment for early warning. While this technology can effectively identify partial discharge anomalies, in application scenarios where medium- and high-voltage switchgear compartments are compact, environmental disturbances are complex, and adjacent areas significantly influence each other, there is still a lack of stable attribution for detected partial discharge pulse anomalies and subsequent changes in ozone, temperature, and humidity. It is difficult to accurately distinguish whether the anomaly originates from the continued development of the same insulation defect or is caused by the influence of adjacent zones, environmental fluctuations, or external electromagnetic interference, thus affecting the reliability of insulation status assessment results and the accuracy of early warning conclusions.

[0004] Therefore, it is necessary to design a data fusion-based method and system for assessing the insulation status of medium and high voltage switchgear to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a data fusion-based method and system for assessing the insulation status of medium and high voltage switchgear, aiming to solve the problems of difficulty in attributing insulation anomalies and accurately identifying the continuous deterioration process of the same insulation defect in scenarios where medium and high voltage switchgear compartments are compactly arranged, partial discharge pulse anomalies coexist with slowly changing anomalies such as ozone, temperature and humidity, and environmental disturbances and mutual influence between adjacent zones are obvious.

[0006] This invention proposes a method for assessing the insulation status of medium- and high-voltage switchgear based on data fusion, comprising:

[0007] Collect partial discharge pulse data, slowly changing state data, and reference environment data for each assessment zone;

[0008] The partial discharge pulse data is subjected to time-series correlation processing to obtain transient candidate events. The transient candidate events are then judged for the first time according to the temporal proximity, spatial consistency, propagation order, and phase convergence to obtain the same-source discharge events and source evaluation partitions.

[0009] The slowly changing state data of the source assessment partition is retrieved and combined with the reference environmental data for delayed closure processing. The delayed closure processing result is judged a second time to eliminate the influence of migration of adjacent assessment partitions, environmental disturbances and external electromagnetic interference, and obtain effective degradation events. The effective degradation events are then subjected to partition sequence analysis to obtain degradation evolution results.

[0010] The insulation status assessment results of the source assessment partition are output based on the degradation evolution results.

[0011] Furthermore, the partial discharge pulse data includes ultra-high frequency signals, ultrasonic signals, and transient ground voltage signals, and the slowly changing state data includes ozone data, temperature data, and humidity data.

[0012] Furthermore, when performing time-series correlation processing on the partial discharge pulse data, the process includes: extracting the start time, peak time, main response position, and phase aggregation segment within a preset short time window, and grouping consecutively occurring or adjacent abnormal pulses into the transient candidate events.

[0013] Furthermore, the first determination includes:

[0014] The temporal proximity, spatial consistency, propagation order, and phase convergence relationships of the transient candidate events are compared item by item. When a preset number of judgment relationships are met, the event is determined to be a homogeneous discharge event. The evaluation partition where the main response location is located and where the abnormal pulse first appears is determined as the source evaluation partition.

[0015] Furthermore, the delayed closure process includes:

[0016] During the observation period following the formation of the same-source discharge event, the ozone data, temperature data, and humidity data of the source assessment zone are correlated and verified according to the order of occurrence. It is determined that the ozone continues to shift, or that the temperature data or humidity data continues to shift, and the delayed closure processing result is obtained.

[0017] Furthermore, the second determination includes:

[0018] When a similar slow-change anomaly appears in an adjacent evaluation partition before the source evaluation partition, the result of the delayed closure process is determined to be a migration effect; when the slow-change state data changes synchronously with the reference environment data, the result of the delayed closure process is determined to be an environmental disturbance; when the partial discharge pulse data does not reappear in the source evaluation partition and the slow-change state data does not continuously shift, the result of the delayed closure process is determined to be external electromagnetic interference.

[0019] Furthermore, when obtaining a valid degradation event, it includes:

[0020] If a sustained ozone shift occurs first in the source assessment zone, and a sustained temperature or humidity shift subsequently occurs during the observation period, and no similar slow-change anomaly occurs first in adjacent assessment zones, the delayed closure processing result is determined as the effective degradation event.

[0021] Furthermore, when obtaining the deterioration evolution results, it includes:

[0022] An event sequence is established according to the source evaluation partition. When the repetition frequency of the effective degradation event in the same source evaluation partition increases, the interval between adjacent events shortens, and the main response position is consistent, it is determined as the degradation evolution result. Events with the same source evaluation partition and consistent main response position are grouped into the same event sequence. The degradation evolution result is established when at least three events are grouped into the same event sequence and the interval between adjacent events shortens sequentially.

[0023] Furthermore, when outputting the insulation status assessment results of the source assessment partition, the following are included:

[0024] When the aforementioned degradation evolution result is not achieved, output the discharge observation result;

[0025] When the aforementioned degradation evolution result is formed and the event sequence does not continue to extend, an insulation degradation early warning result is output.

[0026] When the aforementioned degradation evolution result is formed and the event sequence continues to extend, a breakdown risk warning result is output.

[0027] Compared with existing technologies, the beneficial effects of this invention are as follows: Transient candidate events are formed through time-series correlation processing. The first judgment locks in the same-source discharge event and the source assessment zone, thus establishing a stable correspondence between partial discharge pulse anomalies and specific zones. Then, the slowly changing state data of the source assessment zone is retrieved and combined with reference environmental data for delayed closure processing. The second judgment eliminates the influence of migration from adjacent assessment zones, environmental disturbances, and external electromagnetic interference, making the included anomaly information more authentic and attributable. This effectively avoids misjudging heterogeneous anomalies, occasional disturbances, or environmental changes as insulation degradation. By performing partitioned sequence analysis on effective degradation events and extracting degradation evolution results, one-time discharge anomalies can be distinguished from continuous insulation degradation. This makes the insulation condition assessment results not only more accurate and stable but also possess evolution judgment capabilities and graded early warning capabilities, thereby improving the reliability of insulation condition assessment for medium and high voltage switchgear, reducing the probability of false alarms and missed alarms, and enhancing the targeted nature of maintenance location.

[0028] On the other hand, this application also provides a data fusion-based insulation condition assessment system for medium and high voltage switchgear, used to apply the above-mentioned data fusion-based insulation condition assessment method for medium and high voltage switchgear, including:

[0029] The acquisition unit is configured to acquire partial discharge pulse data, slowly changing state data, and reference environment data for each evaluation zone;

[0030] The first determination unit is configured to perform time-series correlation processing on the partial discharge pulse data to obtain transient candidate events, and to make a first determination on the transient candidate events according to the temporal proximity relationship, spatial consistency relationship, propagation order relationship and phase aggregation relationship to obtain the same source discharge events and source evaluation partitions.

[0031] The second determination unit is configured to retrieve the slowly changing state data of the source evaluation partition, and perform delayed closure processing in combination with the reference environment data. The delayed closure processing result is then used for a second determination to exclude the influence of migration of adjacent evaluation partitions, environmental disturbances and external electromagnetic interference, to obtain effective degradation events. The effective degradation events are then subjected to partition sequence analysis to obtain degradation evolution results.

[0032] The output unit is configured to output the insulation status assessment result of the source assessment partition based on the degradation evolution result.

[0033] It is understandable that the above-mentioned data fusion-based method and system for assessing the insulation status of medium and high voltage switchgear have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0034] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0035] Figure 1 A flowchart of a data fusion-based insulation status assessment method for medium and high voltage switchgear provided in an embodiment of the present invention;

[0036] Figure 2 A functional block diagram of a data fusion-based insulation status assessment system for medium and high voltage switchgear provided in an embodiment of the present invention. Detailed Implementation

[0037] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] In some embodiments of this application, see Figure 1 As shown, this application proposes a data fusion-based method for assessing the insulation status of medium- and high-voltage switchgear, including:

[0039] S100: Collect partial discharge pulse data, slow-change state data, and reference environment data for each evaluation zone.

[0040] S200: Perform time-series correlation processing on partial discharge pulse data to obtain transient candidate events. Make a first judgment on the transient candidate events according to temporal proximity, spatial consistency, propagation order, and phase aggregation to obtain co-source discharge events and source evaluation partitions.

[0041] S300: Retrieve the slowly changing state data of the source assessment zone and perform delayed closure processing in combination with the reference environmental data. Make a second judgment on the delayed closure processing results to eliminate the influence of migration of adjacent assessment zones, environmental disturbances and external electromagnetic interference, obtain effective degradation events, perform partition sequence analysis on the effective degradation events, and obtain degradation evolution results.

[0042] S400: Outputs the insulation status assessment results of the source assessment zone based on the degradation evolution results.

[0043] Specifically, this embodiment is applied to medium- and high-voltage switchgear with multiple isolation chambers. To clearly identify the source of the anomaly, the switchgear is first divided into multiple evaluation zones, such as busbar evaluation zone, circuit breaker evaluation zone, and cable evaluation zone, according to the primary component layout boundaries, insulation partition separation boundaries, and sensor installation areas. Each evaluation zone corresponds to at least one partial discharge acquisition location and at least one slowly changing state acquisition location. During acquisition, a unified time stamp is added to all types of data, and a correspondence is established between data source, evaluation zone, and acquisition time. The reference environmental data includes at least external ozone data, external temperature data, external humidity data, and background electromagnetic noise data. Among them, external ozone data, external temperature data, and external humidity data are used to characterize common environmental changes caused by non-fault sources, and background electromagnetic noise data is used for background noise suppression of partial discharge pulse data. Partial discharge pulse data is acquired using a short-period sampling method, while slowly changing state data and reference environmental data are acquired using a long-period sampling method. The reason for this distinction is that partial discharge manifests as millisecond-level pulse anomalies, while changes in ozone, temperature, and humidity are cumulative and lag-dependent. Direct comparison using the same sampling granularity can easily distort the sequential relationship between pulse anomalies and gradual anomalies. Preferably, the sampling time accuracy of partial discharge pulse data is controlled at the same millisecond level, and the sampling period for gradual state data and reference environmental data is set to 10s to 60s. The main response position refers to the acquisition position with the highest response amplitude in the same transient candidate event. If the response amplitudes of two or more acquisition positions are the same, the position with the more consecutive triggers is the main response position. If the number of consecutive triggers is still the same, the position with the earlier peak time is the main response position. Continuous offset refers to the corresponding gradual state data deviating in the same direction from the reference environmental data or the normal fluctuation range established during the healthy operation of the equipment for at least three consecutive sampling periods without falling back into the normal fluctuation range. The normal fluctuation range can be obtained statistically based on stable operating data for more than 7 consecutive days during the initial operation of the equipment, or it can be obtained by combining factory test data and historical health samples.

[0044] In S200, background noise suppression and anomaly detection are first performed on the partial discharge pulse data. During background noise suppression, background electromagnetic noise data from the reference environment is preferably combined with the data to weaken or eliminate pulse components consistent with external noise characteristics, thereby reducing the impact of external electromagnetic disturbances on the formation of transient candidate events. Then, the start time, peak time, main response location, and phase aggregation segment are extracted within a preset short time window, and continuously occurring or adjacent abnormal pulses are grouped into transient candidate events. Subsequently, the transient candidate events are first judged according to temporal proximity, spatial consistency, propagation order, and phase aggregation. Preferably, when at least three of the four relationships are satisfied, the transient candidate event is determined as a homogeneous discharge event, and the evaluation partition where the main response location is located and the first abnormal pulse appears is determined as the source evaluation partition. The threshold of "at least three relationships satisfied" is derived from comparative statistics of healthy samples, simulated defect samples, and historical fault samples, aiming to balance early anomaly detection capability and false positive suppression capability.

[0045] In S300, partial discharge pulse anomalies and slow-change state anomalies are not directly paralleled and merged at the same time. Instead, after the formation of a common-source discharge event, ozone, temperature, and humidity data of the source assessment zone are subjected to delayed closure processing within a preset observation period. Preferably, the observation period is set to 10 to 60 minutes, and its value can be determined according to the cabinet volume, ventilation conditions, and environmental disturbance intensity. During the delayed closure processing, the continuous shift of ozone is first judged, and then the continuous shift of temperature or humidity data is judged to obtain the delayed closure processing result. Then, the delayed closure processing result is judged a second time: if the adjacent assessment zone shows the same type of slow-change anomaly before the source assessment zone, it is judged as a migration effect. If the slow-change state data changes synchronously with the reference environmental data, it is judged as an environmental disturbance. If the partial discharge pulse data does not reappear in the source assessment zone and the slow-change state data does not shift continuously, it is judged as external electromagnetic interference. Only when a persistent ozone shift occurs first in the source assessment zone, followed by a persistent shift in temperature or humidity data during the observation period, and no similar gradual anomaly occurs first in adjacent assessment zones, is the delayed closure processing result determined as a valid degradation event. Subsequently, using the source assessment zone as the aggregation object, a zone sequence analysis is performed on the valid degradation events. Preferably, events with the same source assessment zone and consistent main response location are grouped into the same event sequence. When at least three valid degradation events are grouped into the same event sequence, and the interval between adjacent events decreases sequentially, a degradation evolution result is considered to have been formed. Finally, in S400, if no degradation evolution result is formed, a discharge observation result is output. If a degradation evolution result is formed and the event sequence does not continue to extend, an insulation degradation warning result is output. If a degradation evolution result is formed and the event sequence continues to extend, a breakdown risk warning result is output.

[0046] Understandably, this embodiment achieves stable locking of the causal relationship between homologous discharge events and slowly changing anomalies in complex scenarios with compact compartment layouts, significant ventilation disturbances, and strong mutual influence between adjacent assessment zones. This results in events entering the degradation evolution analysis having higher homology and purity. Even if there is ozone diffusion, heat conduction, or synchronous external environmental disturbances in adjacent assessment zones, this embodiment can still reduce the probability of mistakenly identifying anomalies in neighboring zones, environmental drift, or transient electromagnetic noise as continuous degradation in this zone, thereby reducing the possibility of prematurely raising the warning level and enabling the warning results to maintain sensitivity while possessing higher level stability and maintenance direction.

[0047] In some embodiments of this application, the partial discharge pulse data includes ultra-high frequency signals, ultrasonic signals, and transient ground voltage signals, and the slowly changing state data includes ozone data, temperature data, and humidity data.

[0048] In some embodiments of this application, when performing time-series correlation processing on partial discharge pulse data, the following steps are included: extracting the start time, peak time, main response position, and phase aggregation segment within a preset short time window, and grouping consecutively occurring or adjacent abnormal pulses into transient candidate events.

[0049] Specifically, ultra-high frequency (UHF) signals and transient ground voltage signals are used to rapidly characterize the transient process of discharge, while ultrasonic signals are used to characterize the mechanical wave response caused by the discharge. Together, these three describe the short-term pulse characteristics of partial discharge activity. Ozone data, temperature data, and humidity data are used to characterize the chemical, thermal, and moisture effects caused by the discharge, respectively, and represent slowly varying information that changes over time. This classification is not arbitrary but based on differences in data change rates, response mechanisms, and ways in which they are influenced by the environment. This provides the foundation for the subsequent processing logic of "pulse-based determination followed by slow-change closure."

[0050] Specifically, the preset short-time window is preferably determined based on a comprehensive consideration of the cabinet structure dimensions, the spacing between acquisition positions, typical propagation delay, and on-site noise level, and is preferably between 50ms and 500ms. If the short-time window is too small, the same discharge event may be divided into multiple isolated pulses. If the short-time window is too large, different discharge events may be incorrectly merged. The start time refers to the time when the abnormal pulse first exceeds the normal fluctuation range, the peak time refers to the time when the abnormal pulse reaches its maximum response value, the main response position refers to the position with the strongest and most obvious persistence within the same pulse merging range, and the phase aggregation segment refers to the phase range in which abnormal pulses occur concentratedly within a continuous power frequency cycle. When merging, it is preferably limited to: at least two abnormal pulses appearing in the same evaluation partition within the preset short-time window, or different types of partial discharge pulse data being triggered consecutively in adjacent time periods, before entering the transient candidate event generation process.

[0051] Understandably, this embodiment improves the purity and consistency of subsequent judgment inputs from the event generation source. By unifying UHF signals, ultrasonic signals, and transient ground voltage signals into transient candidate events, and then using these events for subsequent processing, the fragmentation problem caused by differences in response delay and manifestation of different pulse signals can be effectively alleviated. Even if the early partial discharge intensity is weak and the amplitude of a single pulse is insufficient to stably trigger subsequent evaluation, it can still be identified as a transient candidate event with analytical value by utilizing the adjacent occurrence and phase clustering characteristics of cross-type pulses, thereby reducing the probability of missing early discharge anomalies. At the same time, it can also reduce the situation where random noise or single disturbances are mistakenly expanded into complete discharge events. This embodiment provides more reliable preconditions for homogeneous source determination and delayed closure processing by optimizing the formation quality of transient candidate events.

[0052] In some embodiments of this application, the first determination includes: comparing the temporal proximity, spatial consistency, propagation order, and phase aggregation relationships of transient candidate events one by one; determining them as homogeneous discharge events when the preset number of determination relationships are met; and determining the evaluation partition where the main response location is located and where the abnormal pulse first appears as the source evaluation partition.

[0053] In some embodiments of this application, the delayed closure process includes: during the observation period after the formation of the same source discharge event, performing correlation verification on the ozone data, temperature data and humidity data of the source assessment zone according to the order of occurrence, determining the continuous shift of ozone, determining the continuous shift of temperature data or humidity data, and obtaining the delayed closure process result.

[0054] Specifically, temporal proximity is obtained by comparing the difference between the start time and the peak time of each abnormal pulse within the same transient candidate event. A temporal proximity is considered satisfied when both the start time difference and the peak time difference fall within a preset allowable time band. The preset allowable time band can be determined based on cabinet dimensions, the distance between acquisition positions, typical propagation paths, and on-site sampling accuracy calibration, preferably between 5ms and 50ms, and not exceeding one-third of a preset short time window. Spatial consistency is obtained by comparing the main response positions of each abnormal pulse. If the main response positions fall within the same evaluation zone, spatial consistency is considered satisfied. If the main response positions are located in adjacent evaluation zones, spatial consistency is considered satisfied only when the interval between the two acquisition positions does not exceed one sensor installation spacing and there are no independent metal partitions in the cabinet structure. Propagation sequence is obtained by comparing whether the order of occurrence of different types of abnormal pulses is consistent with a pre-established allowable propagation sequence template. The allowable propagation sequence template is pre-established based on sensor installation positions, propagation paths, and pre-commissioning calibration results. A propagation sequence is considered satisfied when the actual triggering order falls within the allowable propagation sequence template. When making judgments, it is not required that various abnormal pulses appear in a strictly unique and fixed order. As long as the overall sequence within a preset allowable time band is consistent with the allowable propagation sequence template, the propagation sequence relationship is considered satisfied. The phase clustering relationship is obtained by statistically analyzing the phase distribution of abnormal pulses over at least five consecutive power frequency cycles. When most abnormal pulses are concentrated in the same phase segment or adjacent phase segments, the phase clustering relationship is considered satisfied. Preferably, at least three of the four relationships are satisfied to determine a common-source discharge event. If the number of satisfied items is the same and corresponds to two candidate source evaluation zones, the evaluation zone where the abnormal pulse first appears is taken as the source evaluation zone. If the first occurrence times are the same, the evaluation zone where the main response location is located is taken as the source evaluation zone to prevent adjacent evaluation zones affected by transmission from being misjudged as fault sources.

[0055] Specifically, delayed closure processing refers to not directly merging partial discharge pulse anomalies and slow-change state anomalies at the same moment, but rather performing correlation verification on the slow-change state changes in chronological order during the observation period after the formation of the same-source discharge event. The observation period is preferably 10 to 60 minutes; too short a period will prevent ozone and heat accumulation from fully manifesting, while too long a period may introduce more environmental noise and the influence of adjacent assessment zones. Ozone persistent offset preferably refers to ozone data deviating in the same direction relative to reference environmental data or a stable healthy operating range for at least three consecutive sampling periods. The same caliber is used for persistent offsets in temperature or humidity data. The order of ozone first, then temperature or humidity, is because ozone usually reflects the chemical co-effects of discharge earlier, while temperature and humidity are more easily affected by load, ventilation, and the external environment, making them more suitable as subsequent confirmation conditions. If neither temperature nor humidity data meets the persistent offset condition during the observation period, a delayed closure processing result is not generated. If both meet the persistent offset condition, the one that meets the condition first is used as the subsequent confirmation basis, and the other is recorded as an auxiliary record.

[0056] It is understandable that this embodiment can still improve the stability of identifying early persistent insulation anomalies even when the partial discharge pulse is weak, early ozone accumulation is not obvious, or temperature changes have a certain lag, through the combination of "relationship quantity judgment plus delayed closing sequence verification". At the same time, it can also reduce the probability of false closing caused by ambient temperature rise, instantaneous humidity changes, or interference from adjacent zones. This embodiment improves the authenticity and causal consistency of events before proceeding to the subsequent second judgment.

[0057] In some embodiments of this application, the second determination includes: when an adjacent evaluation partition exhibits the same type of slowly changing anomaly before the source evaluation partition, the delayed closure processing result is determined to be a migration effect. When the slowly changing state data changes synchronously with the reference environment data, the delayed closure processing result is determined to be an environmental disturbance. When the partial discharge pulse data does not reappear in the source evaluation partition and the slowly changing state data does not continuously shift, the delayed closure processing result is determined to be external electromagnetic interference.

[0058] In some embodiments of this application, obtaining a valid degradation event includes: when a continuous ozone shift occurs first in the source assessment partition, and a continuous temperature data shift or a continuous humidity data shift occurs subsequently during the observation period, and no similar slow-change anomaly occurs first in adjacent assessment partitions, the delayed closure processing result is determined as a valid degradation event.

[0059] Specifically, the second judgment is set after the delayed closure process. Its purpose is not to re-determine whether an anomaly exists, but to attribute and divert the results of the delayed closure process. The second judgment is executed sequentially in the order of migration impact, environmental disturbance, external electromagnetic interference, and effective degradation events. If the previous judgment is valid, the next judgment will not proceed. Similar slowly varying anomalies refer to anomalies formed by data of the same slowly varying state, such as ozone anomalies compared with ozone anomalies, temperature anomalies compared with temperature anomalies, and humidity anomalies compared with humidity anomalies. "Appearing before the source assessment partition" means that the time when a similar slowly varying anomaly in an adjacent assessment partition reaches the condition for sustained offset is earlier than the time when the same type of slowly varying anomaly in the source assessment partition reaches the condition for sustained offset. To reduce time inversion misjudgments caused by sampling jitter, a minimum time lead of 1 to 2 slowly varying sampling periods is preferably set. If the lead of a similar slowly varying anomaly in an adjacent assessment partition reaches this minimum time lead, it is determined to be a migration impact. For environmental disturbances, synchronous changes are preferably defined as follows: ozone data is compared with external ozone data, temperature data with external temperature data, and humidity data with external humidity data, and they must maintain the same direction of change for at least two consecutive sampling periods, with the difference between the start and end times of the change falling within a synchronous tolerance of one to three slowly changing sampling periods. For external electromagnetic interference, a dual-condition combination is used for judgment: on the one hand, the number of repetitions of the same type of partial discharge pulse anomaly within the observation period does not reach the minimum requirement for forming another transient candidate event. On the other hand, the slowly changing state data does not form the aforementioned continuous offset. Through the above sequential exclusion rules, it is possible to prevent the misidentification of inter-regional diffusion, environmental fluctuations, or instantaneous external pulses as insulation degradation processes.

[0060] Specifically, this embodiment uses a positive admission method to determine valid degradation events, rather than automatically assuming they are valid after exclusion. That is, a valid degradation event is only recorded when three conditions are met simultaneously: First, a sustained ozone shift occurs first in the source assessment zone. Second, a sustained shift in temperature or humidity data subsequently occurs within the same observation period. Third, no similar slowly changing anomalies first appear in adjacent assessment zones. Temperature or humidity is chosen as one of the two, considering that different insulation defect types may manifest as heat accumulation or localized dampness, and both are not required to be met simultaneously. If the aforementioned valid degradation event conditions are not met by the end of the observation period, the delayed closure process is terminated and archived, and not included in subsequent event sequences. This positive admission condition ensures that events entering subsequent sequence analysis not only satisfy the existence of anomalies but also satisfy zoning attribution and development continuity.

[0061] In some embodiments of this application, obtaining the degradation evolution result includes: establishing an event sequence according to the source assessment partition; when the repetition frequency of valid degradation events in the same source assessment partition increases, the interval between adjacent events shortens, and the main response position is consistent, it is determined as a degradation evolution result. Specifically, events with the same source assessment partition and consistent main response position are grouped into the same event sequence, and the degradation evolution result is established when at least three events are grouped into the same event sequence and the interval between adjacent events shortens sequentially.

[0062] In some embodiments of this application, when outputting the insulation status assessment results of the source assessment zone, the following steps are included: when no degradation evolution result is formed, outputting discharge observation results; when a degradation evolution result is formed and the event sequence does not continue to extend, outputting insulation degradation warning results; and when a degradation evolution result is formed and the event sequence continues to extend, outputting breakdown risk warning results.

[0063] Specifically, an event sequence is not an arbitrary set of anomalies occurring sequentially at any given time, but rather an ordered set of valid degradation events with the same source assessment partition and consistent main response location. Consistent main response location means that the main response locations of multiple valid degradation events fall within the same assessment location range, which can be determined based on the density of data collection locations, cabinet structural dimensions, and on-site calibration results. When establishing an event sequence, the valid degradation events are sorted according to their occurrence time, and the source assessment partition and main response location of each newly entering event are compared with the current event sequence. If both conditions are met, the event is included in the current event sequence; otherwise, a new event sequence is established. Increased repetition frequency means that the number of valid degradation events included in the same event sequence within the current statistical period is higher than in the previous statistical period, and at least one new valid degradation event is added within the current statistical period. The statistical period can be set to 24h, 48h, or 72h according to the equipment operating rhythm and risk assessment requirements. The previous statistical period is a consecutive statistical interval of the same length preceding the current statistical period. A shortened interval between adjacent events refers to a time interval between the occurrence of a subsequent event and the preceding event in the same event sequence being less than the interval of the previous set. To ensure that the increase in repetition frequency is consistent with the minimum number of events caliber in the embodiments, this embodiment limits the establishment of a degradation evolution result to: at least three valid degradation events within the same event sequence arranged chronologically; the entry of the third valid degradation event causing the number of events in the event sequence in the current statistical period to be higher than that in the previous statistical period; and the interval between the two most recent sets of adjacent events shortening sequentially. When this condition is met, a degradation evolution result is considered to have been formed.

[0064] Specifically, this embodiment divides the insulation condition assessment results into three levels. When no degradation evolution result is formed, although valid degradation events exist, the conditions for continuous evolution have not yet been met; therefore, a discharge observation result is output. When a degradation evolution result is formed and the event sequence does not continue to extend, it indicates that a confirmed continuous insulation degradation trend exists in the source assessment area. However, no new valid degradation events are added in the next statistical period after the formation of the degradation evolution result, or although new events are added, the main response positions are no longer consistent, or the interval between the two most recent adjacent events no longer shortens; therefore, an insulation degradation warning result is output. When a degradation evolution result is formed and the event sequence continues to extend, it indicates that in subsequent statistical periods after the formation of the degradation evolution result, the same event sequence continues to add valid degradation events, and the main response positions of the new events still fall within the aforementioned same assessment position range; therefore, a breakdown risk warning result is output. The above three levels of results can correspond to different operation and maintenance strategies such as routine observation, key review, and priority handling; however, this correspondence is only an application example and does not constitute additional limitations.

[0065] In one possible embodiment, the preset short time window is 100ms, the preset allowable time band is 20ms, the data sampling period for slowly changing states is 30s, the observation period is 30min, the minimum time lead is 1 slowly changing sampling period, the synchronization tolerance is 2 slowly changing sampling periods, and the statistical period is 24h. The above values ​​are only feasible examples. In actual applications, they can be adjusted according to the cabinet type, sensor layout, ventilation conditions, and on-site noise level. However, after the same equipment is commissioned and calibrated once, a unified judgment standard should be used to ensure the comparability of the evaluation results before and after the commissioning.

[0066] Understandably, this embodiment isolates the question of whether "effective degradation events truly constitute a continuous development process" and establishes a continuous judgment chain from "abnormality confirmed" to "risk escalation" through event sequence convergence, minimum event number limits, interval change trend constraints, and hierarchical output relationships. Even if multiple effective degradation events have been confirmed to belong to the same source assessment zone, they will not be prematurely upgraded to a high-level warning due to occasional repetition within a short period. Only when these events remain consistent in location, gradually approach each other in time, and continue to extend within the observation period will a higher risk level be triggered.

[0067] In summary, transient candidate events are generated through time-series correlation processing. The first judgment identifies the same-source discharge event and its corresponding assessment zone, establishing a stable correspondence between partial discharge pulse anomalies and specific zones. Then, the slowly changing state data of the assessment zone is retrieved and combined with reference environmental data for delayed closure processing. A second judgment eliminates the influence of migration from adjacent assessment zones, environmental disturbances, and external electromagnetic interference, making the analyzed anomaly information more authentic and attributable. This effectively avoids misjudging heterogeneous anomalies, occasional disturbances, or environmental changes as insulation degradation. By performing zoned sequence analysis on effective degradation events and extracting degradation evolution results, one-time discharge anomalies can be distinguished from continuous insulation degradation. This results in insulation condition assessment results with higher accuracy and stability, as well as evolution judgment and graded early warning capabilities, thereby improving the reliability of insulation condition assessment for medium and high voltage switchgear, reducing the probability of false alarms and missed alarms, and enhancing the targeted nature of maintenance.

[0068] Based on another preferred embodiment described above, see [link to preferred embodiment]. Figure 2 As shown, this embodiment provides a data fusion-based insulation status assessment system for medium and high voltage switchgear, used to apply the aforementioned data fusion-based insulation status assessment method for medium and high voltage switchgear, including:

[0069] The acquisition unit is configured to acquire partial discharge pulse data, slowly changing state data, and reference environment data for each evaluation zone.

[0070] The first determination unit is configured to perform time-series correlation processing on partial discharge pulse data to obtain transient candidate events, and to make a first determination on the transient candidate events according to temporal proximity, spatial consistency, propagation order, and phase aggregation to obtain co-source discharge events and source evaluation partitions.

[0071] The second determination unit is configured to retrieve the slowly changing state data of the source assessment partition, and perform delayed closure processing in combination with the reference environmental data. The delayed closure processing result is then used for a second determination to exclude the influence of migration of adjacent assessment partitions, environmental disturbances and external electromagnetic interference, to obtain effective degradation events. The effective degradation events are then subjected to partition sequence analysis to obtain the degradation evolution results.

[0072] The output unit is configured to output the insulation status assessment results of the source assessment zone based on the degradation evolution results.

[0073] It is understandable that the above-mentioned data fusion-based method and system for assessing the insulation status of medium and high voltage switchgear have the same beneficial effects, and will not be elaborated further here.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for assessing the insulation status of medium- and high-voltage switchgear based on data fusion, characterized in that, include: Collect partial discharge pulse data, slowly changing state data, and reference environment data for each assessment zone; The partial discharge pulse data is subjected to time-series correlation processing to obtain transient candidate events. The transient candidate events are then judged for the first time according to the temporal proximity, spatial consistency, propagation order, and phase convergence to obtain the same-source discharge events and source evaluation partitions. The slowly changing state data of the source assessment partition is retrieved and combined with the reference environmental data for delayed closure processing. The delayed closure processing result is judged a second time to eliminate the influence of migration of adjacent assessment partitions, environmental disturbances and external electromagnetic interference, and obtain effective degradation events. The effective degradation events are then subjected to partition sequence analysis to obtain degradation evolution results. The insulation status assessment results of the source assessment partition are output based on the degradation evolution results.

2. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 1, characterized in that, The partial discharge pulse data includes ultra-high frequency signals, ultrasonic signals, and transient ground voltage signals, and the slowly changing state data includes ozone data, temperature data, and humidity data.

3. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 2, characterized in that, When performing time-series correlation processing on the partial discharge pulse data, the process includes: extracting the start time, peak time, main response position, and phase aggregation segment within a preset short time window, and grouping consecutively occurring or adjacent abnormal pulses into the transient candidate events.

4. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 3, characterized in that, The first determination includes: The temporal proximity, spatial consistency, propagation order, and phase convergence relationships of the transient candidate events are compared item by item. When a preset number of judgment relationships are met, the event is determined to be a homogeneous discharge event. The evaluation partition where the main response location is located and where the abnormal pulse first appears is determined as the source evaluation partition.

5. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 2, characterized in that, The delayed closing process includes: During the observation period following the formation of the same-source discharge event, the ozone data, temperature data, and humidity data of the source assessment zone are correlated and verified according to the order of occurrence. It is determined that the ozone continues to shift, or that the temperature data or humidity data continues to shift, and the delayed closure processing result is obtained.

6. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 5, characterized in that, The second determination includes: When a similar slow-change anomaly appears in an adjacent evaluation partition before the source evaluation partition, the result of the delayed closure process is determined to be a migration effect; when the slow-change state data changes synchronously with the reference environment data, the result of the delayed closure process is determined to be an environmental disturbance; when the partial discharge pulse data does not reappear in the source evaluation partition and the slow-change state data does not continuously shift, the result of the delayed closure process is determined to be external electromagnetic interference.

7. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 6, characterized in that, When a valid degradation event is obtained, it includes: If a sustained ozone shift occurs first in the source assessment zone, and a sustained temperature or humidity shift subsequently occurs during the observation period, and no similar slow-change anomaly occurs first in adjacent assessment zones, the delayed closure processing result is determined as the effective degradation event.

8. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 7, characterized in that, When obtaining the deterioration evolution results, the following are included: An event sequence is established according to the source evaluation partition. When the repetition frequency of the effective degradation event in the same source evaluation partition increases, the interval between adjacent events shortens, and the main response position is consistent, it is determined as the degradation evolution result. Events with the same source evaluation partition and consistent main response position are grouped into the same event sequence. The degradation evolution result is established when at least three events are grouped into the same event sequence and the interval between adjacent events shortens sequentially.

9. The method for assessing the insulation status of medium and high voltage switchgear based on data fusion according to claim 8, characterized in that, When outputting the insulation status assessment results of the source assessment partition, the following are included: When the aforementioned degradation evolution result is not achieved, output the discharge observation result; When the aforementioned degradation evolution result is formed and the event sequence does not continue to extend, an insulation degradation early warning result is output. When the aforementioned degradation evolution result is formed and the event sequence continues to extend, a breakdown risk warning result is output.

10. A data fusion-based insulation status assessment system for medium and high voltage switchgear, used to apply the data fusion-based insulation status assessment method for medium and high voltage switchgear as described in any one of claims 1-9, characterized in that, include: The acquisition unit is configured to acquire partial discharge pulse data, slowly changing state data, and reference environment data for each evaluation zone; The first determination unit is configured to perform time-series correlation processing on the partial discharge pulse data to obtain transient candidate events, and to make a first determination on the transient candidate events according to the temporal proximity relationship, spatial consistency relationship, propagation order relationship and phase aggregation relationship to obtain the same source discharge events and source evaluation partitions. The second determination unit is configured to retrieve the slowly changing state data of the source evaluation partition, and perform delayed closure processing in combination with the reference environment data. The delayed closure processing result is then used for a second determination to exclude the influence of migration of adjacent evaluation partitions, environmental disturbances and external electromagnetic interference, to obtain effective degradation events. The effective degradation events are then subjected to partition sequence analysis to obtain degradation evolution results. The output unit is configured to output the insulation status assessment result of the source assessment partition based on the degradation evolution result.

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