Method and system for evaluating health state of high-voltage switch cabinet

Abstract

The invention relates to the related technical field of high-voltage switch cabinets, in particular to a high-voltage switch cabinet health state assessment method and system, and the method comprises the steps: obtaining an equipment operation parameter sequence of a high-voltage switch cabinet, combining an operation mechanism, determining a first response data segment and a second response data segment, and constructing a state assessment matrix, and identifying an abnormal operation period and a degradation trend cluster, and performing health risk early warning on the high-voltage switch cabinet by using the health state evaluation model. The technical problems that the sensing precision of key events such as switching-on bounce and partial discharge is insufficient, the generalization ability of health state evaluation under complex working conditions is insufficient, and the accuracy of high-voltage switch cabinet health evaluation is limited are solved, and cross correlation and variation correlation of a signal simulation verification chain are taken as an enhancement mechanism, so that the accuracy of high-voltage switch cabinet health evaluation is improved. The method has the technical effects that the response sensitivity of the model to key events is improved, the health risk of the high-voltage switch cabinet is accurately evaluated in combination with an abnormal period and a degraded cluster, and the accuracy of health evaluation of the high-voltage switch cabinet is improved.

Description

Technical Field

[0001] This invention relates to the technical field of high-voltage switchgear, specifically to a method and system for assessing the health status of high-voltage switchgear. Background Technology

[0002] High-voltage switchgear plays a crucial role in circuit switching, fault isolation, and power distribution. Its health status affects the power supply reliability and operational safety of the power system. High-voltage switchgear contains operating mechanisms such as circuit breakers and disconnecting switches. If problems such as insulation aging and mechanical jamming are not identified in time, it may lead to equipment failure or even large-scale power outages.

[0003] Health status assessments of high-voltage switchgear often focus on single parameters such as partial discharge and contact temperature. Isolated analysis cannot fully reflect the overall health status of the equipment. In addition, assessment models often rely on static data training and lack the enhanced support of signal simulation verification chains. The simulation of response sensitivity to key events such as closing bounce and partial discharge is insufficient, which limits the reliability and applicability of the assessment results and makes it difficult to meet the needs of accurate assessment under complex operating conditions.

[0004] In summary, existing technologies suffer from insufficient accuracy in sensing critical events such as closing bounce and partial discharge, inadequate generalization ability of health status assessment under complex operating conditions, and limited accuracy of health assessment for high-voltage switchgear. Summary of the Invention

[0005] This application provides a method and system for assessing the health status of high-voltage switchgear, aiming to solve the technical problems in the prior art, such as insufficient perception accuracy of key events like closing bounce and partial discharge, insufficient generalization ability of health status assessment under complex operating conditions, and limited accuracy of high-voltage switchgear health assessment.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows:

[0007] In a first aspect, this application provides a method for assessing the health status of a high-voltage switchgear. The method includes: acquiring a sequence of equipment operating parameters for the high-voltage switchgear, wherein the operating mechanisms within the switchgear include circuit breakers and disconnectors; determining a first response data segment and a second response data segment based on the operating mechanisms and the sequence of equipment operating parameters; constructing a status assessment matrix based on the first and second response data segments; identifying abnormal operating cycles and degradation trend clusters using the status assessment matrix; and using a health status assessment model enhanced by cross-correlation and mutation correlation of signal simulation verification chains to provide a health risk warning for the high-voltage switchgear based on the abnormal operating cycles and degradation trend clusters.

[0008] Preferably, the tripping control pulse signal of the operating mechanism is used as the first synchronization trigger reference to perform timestamp synchronization calibration on the sequence of operating parameters of the equipment, and the first response data segment within the time window corresponding to the tripping control pulse signal is extracted.

[0009] Preferably, the closing control pulse signal of the operating mechanism is used as the second synchronization trigger reference to perform timestamp synchronization calibration on the sequence of operating parameters of the equipment, and the second response data segment within the time window corresponding to the closing control pulse signal is extracted.

[0010] Preferably, a state evaluation matrix is ​​constructed by associating and mapping the contact state characteristics, insulation state characteristics, and mechanical operation state characteristics with the first synchronous triggering reference and the first response data segment, the second synchronous triggering reference and the second response data segment.

[0011] Preferably, based on the contact temperature subsequence, the temperature rise rate and steady-state temperature difference of the main contacts of the circuit breaker corresponding to the operating mechanism under load conditions are determined, and the contact resistance change trend is inverted by combining the load current data to extract the contact state characteristics of the contacts; wherein, the equipment operating parameter sequence includes the contact temperature subsequence, the partial discharge quantum sequence, and the mechanical characteristic parameter subsequence.

[0012] Preferably, based on the partial discharge quantum sequence, the partial discharge pulse phase distribution, discharge repetition rate, average discharge quantity, maximum discharge quantity, and rise time during multiple opening and closing operations are extracted. Combined with the trend change of dielectric loss factor, air gap discharge, surface creepage, and insulation material aging analysis are performed to extract the insulation state characteristics.

[0013] Preferably, based on the mechanical characteristic parameter subsequence, the time dispersion of opening and closing of the circuit breaker and disconnector corresponding to the operating mechanism, the peak fluctuation rate of the energy storage motor current, the abnormal change point of the operating torque, and the offset of the inflection point of the motion speed curve are determined in multiple opening and closing operations. The jamming frequency of the transmission mechanism and the change of the bearing wear damping coefficient are analyzed to extract the mechanical operating state characteristics.

[0014] Preferably, the high-dimensional feature sequences in the state evaluation matrix are visualized and clustered to distinguish between normal operating condition clusters and abnormal state clusters; the operating cycles that deviate from the main clusters are identified, and the starting point and evolution path of performance degradation are located by combining sliding window variance analysis and trend slope detection, thereby determining the degradation trend clusters.

[0015] Preferably, the finite element space of the integrated busbar, circuit breaker arc-extinguishing chamber, insulator, and sensor network topology corresponding to the high-voltage switchgear is encoded and modeled. The encoding and modeling is combined with spatial constraints, thermal constraints, and mechanical constraints. The static power Pareto front and dynamic power Pareto front are obtained by using a non-dominated sorting mechanism guided by multi-objective optimization. Based on the static power Pareto front and dynamic power Pareto front, the non-dominated solution set is screened. According to the non-dominated solution set, the response sensitivity of key events including hot spots, partial discharge, and closing bounce is simulated and evaluated. A signal simulation verification chain related to the simulation eye diagram opening, crosstalk level, and clock jitter is set.

[0016] In a second aspect, this application provides a health status assessment system for high-voltage switchgear, comprising: a data acquisition module for acquiring a sequence of equipment operating parameters of the high-voltage switchgear, wherein the operating mechanisms within the high-voltage switchgear include circuit breakers and disconnectors; a response data segment determination module for determining a first response data segment and a second response data segment based on the operating mechanisms and the sequence of equipment operating parameters; a status assessment matrix construction module for constructing a status assessment matrix based on the first and second response data segments, and using the status assessment matrix to identify abnormal operating cycles and degradation trend clusters; and a risk warning module for providing health risk warnings for the high-voltage switchgear using a health status assessment model enhanced by cross-correlation and mutation correlation of signal simulation verification chains, based on the abnormal operating cycles and degradation trend clusters.

[0017] In summary, one or more technical solutions provided in this application achieve the technical effect of improving the model's response sensitivity to key events by using the cross-correlation and mutation correlation of the signal simulation verification chain as an enhancement mechanism, and accurately assessing the health risks of high-voltage switchgear by combining abnormal cycles and deterioration clusters, thereby improving the accuracy of high-voltage switchgear health assessment. Attached Figure Description

[0018] Figure 1 This application provides a flowchart illustrating a method for assessing the health status of high-voltage switchgear.

[0019] Figure 2 This application provides a structural schematic diagram of a high-voltage switchgear health status assessment system.

[0020] Explanation of reference numerals in the attached diagram: Data acquisition module M100, response data segment determination module M200, status assessment matrix construction module M300, and risk warning module M400. Detailed Implementation

[0021] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1As shown, this application provides a method for assessing the health status of a high-voltage switchgear, wherein the method includes:

[0022] S1: Obtain the equipment operating parameter sequence of the high-voltage switchgear, wherein the operating mechanism in the high-voltage switchgear includes circuit breakers and disconnect switches; S2: Based on the operating mechanism and the equipment operating parameter sequence, determine the first response data segment and the second response data segment.

[0023] Specifically, the equipment operating parameter sequence refers to the sequence data of various parameters generated by the high-voltage switchgear during operation, including current, voltage, contact temperature, partial discharge, and mechanical characteristic parameters, which can reflect the performance of the equipment under different operating conditions; the operating mechanism refers to the components inside the high-voltage switchgear used to control the on and off of the circuit, including circuit breakers and disconnecting switches. The circuit breaker is used to control the on and off of the circuit, and the disconnecting switch is used to isolate the circuit to ensure safety during maintenance; the first response data segment and the second response data segment refer to the data within a specific time period extracted from the equipment operating parameter sequence based on the control pulse signal of the operating mechanism, which includes information on changes in operating parameters related to the actions of the operating mechanism.

[0024] Execution Steps: The first step involves acquiring the equipment operating parameter sequence of the high-voltage switchgear. This sequence is collected from various sensors installed within the switchgear, including temperature sensors, current sensors, and partial discharge sensors. Specifically, the contact temperature sensor monitors real-time temperature changes in the main contacts of the circuit breaker, while the partial discharge sensor detects discharge in the insulation material. Using the opening and closing control pulse signals of the operating mechanisms of the circuit breaker and disconnector as synchronous triggering references, the equipment operating parameter sequence is timestamped and calibrated. Specifically, when the circuit breaker performs an opening operation, data within a specific time window before and after the opening control pulse signal is extracted to form the first response data segment. Similarly, data within the corresponding time window is extracted using the closing control pulse signal as a reference to form the second response data segment. By accurately extracting data segments related to the actions of the operating mechanisms, the above steps ensure that subsequent analysis focuses on key operational scenarios, providing an accurate data foundation for multi-dimensional state analysis.

[0025] S3: Based on the first response data segment and the second response data segment, construct a state assessment matrix, and use the state assessment matrix to identify abnormal operation cycles and deterioration trend clusters; S4: Through the abnormal operation cycles and deterioration trend clusters, use a health status assessment model with cross-correlation and mutation correlation of signal simulation verification chain as the enhancement mechanism to provide health risk warning for the high-voltage switchgear.

[0026] Specifically, the state assessment matrix is ​​used to integrate and represent various state characteristics of high-voltage switchgear in different operating cycles. It correlates and maps key parameters, including contact state characteristics, insulation state characteristics, and mechanical operation state characteristics, in the first and second response data segments to form a comprehensive state assessment framework. Abnormal operating cycles refer to operating cycles that deviate significantly from normal operating cycles during equipment operation, manifested as abnormal fluctuations in parameters, abnormal extensions or shortenings in operating time, etc. Deterioration trend clusters refer to identifying trends in equipment performance degradation by analyzing data in the state assessment matrix and clustering these trends into different clusters. Each cluster represents a specific degradation mode, which helps to accurately locate the root cause of the problem. The signal simulation verification chain refers to simulating the operating state of high-voltage switchgear under different operating conditions to verify the response sensitivity of the health state assessment model to key events, including closing bounce and partial discharge. Cross-correlation and variation correlation are two enhancement methods in this mechanism to improve the accuracy and reliability of the model.

[0027] Execution steps: Extract key state features from the first response data segment and the second response data segment, including contact state features, insulation state features, and mechanical operation state features. Associate and map these features with the control pulse signals of the operating mechanism to form a state evaluation matrix. The contact state features are associated with the temperature rise rate and the trend of contact resistance change. The insulation state features are associated with the partial discharge pulse phase distribution and discharge quantity. The mechanical operation state features are associated with the opening and closing time dispersion and the change of operating torque.

[0028] Visual clustering analysis is performed on the high-dimensional feature sequences in the condition assessment matrix to distinguish between normal operating condition clusters and abnormal condition clusters. Through sliding window variance analysis and trend slope detection, operating cycles deviating from the main clusters are identified, and the starting point and evolution path of performance degradation are located to determine degradation trend clusters. Based on the abnormal operating cycles and degradation trend clusters, an enhanced health status assessment model is used to provide health risk warnings for the high-voltage switchgear. Furthermore, when the health status assessment model detects an abnormal increase in the contact temperature rise rate during a certain operating cycle, which occurs simultaneously with an increase in partial discharge, it can provide an early warning of potential insulation aging problems, improving the accuracy of high-voltage switchgear health assessments.

[0029] Furthermore, based on the operating mechanism and the sequence of equipment operating parameters, the first response data segment is determined. The method of this application includes:

[0030] Using the tripping control pulse signal of the operating mechanism as the first synchronization trigger reference, the sequence of operating parameters of the equipment is timestamped and synchronized, and the first response data segment within the time window corresponding to the tripping control pulse signal is extracted.

[0031] Specifically, the tripping control pulse signal refers to the control signal issued by the operating mechanism (such as a circuit breaker) when performing a tripping operation. It is a short pulse that marks the start of the tripping operation and can be used as a reference point for time synchronization. Timestamp synchronization calibration refers to aligning each data point in the equipment operating parameter sequence with the tripping control pulse signal in time, ensuring that the time reference of all data points is consistent, eliminating possible time deviations during data acquisition, and ensuring data accuracy. The time window refers to the working time period set before and after the tripping control pulse signal, used to capture operating parameters related to the tripping operation. The length of the time window can be adjusted according to actual needs and equipment characteristics. The first response data segment refers to the sequence of equipment operating parameters captured within the time window before and after the tripping control pulse signal, containing key information during the tripping operation, such as current changes, voltage changes, and contact temperature changes.

[0032] Execution steps: The tripping control pulse signal is detected via a sensor network and used as a time synchronization reference point. Each data point in the equipment operating parameter sequence undergoes timestamp synchronization calibration to eliminate data misalignment caused by communication delays or sampling period differences, ensuring that the time reference of all data points is consistent with the tripping control pulse signal. Based on the characteristics of the high-voltage switchgear, a reasonable time window is set. If the typical tripping operation time is 50 milliseconds, the time window is set to 20 milliseconds before and 80 milliseconds after the tripping control pulse signal. Within this time window, data from the equipment operating parameter sequence is extracted to form the first response data segment. In the above steps, by using the tripping control pulse signal as a reference for timestamp synchronization calibration, it is ensured that the extracted data segment accurately reflects the equipment status during the tripping operation. Furthermore, during the tripping operation, the contact temperature will briefly rise due to rapid current changes. By analyzing the contact temperature changes in the first response data segment, abnormal situations can be detected in a timely manner, thereby providing early warning of equipment health risks and improving the accuracy of the assessment.

[0033] Furthermore, based on the operating mechanism and the sequence of equipment operating parameters, a second response data segment is determined. The method of this application includes:

[0034] Using the closing control pulse signal of the operating mechanism as the second synchronization trigger reference, the sequence of operating parameters of the equipment is timestamped and synchronized, and the second response data segment within the time window corresponding to the closing control pulse signal is extracted.

[0035] Specifically, the closing control pulse signal refers to the control signal issued by the operating mechanism when performing the closing operation. It is a short pulse that marks the start of the closing operation and can be used as a reference point for time synchronization. The second synchronization trigger reference refers to using the closing control pulse signal as a reference point for time synchronization. It is used to calibrate the timestamp of the equipment operating parameter sequence to ensure the accuracy and consistency of the data. The second response data segment refers to the sequence of equipment operating parameters captured within the time window before and after the closing control pulse signal. It contains key information during the closing operation, such as current changes, voltage changes, and contact temperature changes.

[0036] Execution steps: The closing control pulse signal is detected through the sensor network. Once the closing control pulse signal is detected, it is used as the reference point for time synchronization. The timestamp synchronization calibration is performed on each data point in the equipment operating parameter sequence to ensure that the time reference of all data points is consistent with the closing control pulse signal. According to the characteristics of the high-voltage switchgear, a reasonable time window is set. If the typical time of closing operation is 60 milliseconds, the time window can be set to 30 milliseconds before and 90 milliseconds after the closing control pulse signal. Within this time window, the data in the equipment operating parameter sequence is extracted to form the second response data segment.

[0037] In the above steps, by using the closing control pulse signal as a reference for timestamp synchronization calibration, it can be ensured that the intercepted data segment accurately reflects the equipment status during the closing operation. Furthermore, during the closing operation, the current and voltage will change rapidly, and the contact temperature will rise briefly due to the rapid change in current. By analyzing the contact temperature change in the second response data segment, this abnormal situation can be detected in time, thereby providing early warning of equipment health risks and improving the accuracy of the assessment.

[0038] In addition, closing bounce may occur during the closing operation, where the contacts briefly separate and re-engage multiple times at the moment of closing. This phenomenon may cause abnormal fluctuations in current and voltage, thus affecting the health of the equipment. By analyzing the current and voltage changes in the second response data segment, the closing bounce phenomenon can be detected. Furthermore, under normal circumstances, the current waveform during the closing operation should be smooth, but when closing bounce occurs, the current waveform will show obvious spikes. By verifying the cross-correlation and variation correlation enhancement mechanism of the signal simulation verification chain, the model's response sensitivity to this critical event can be further improved, thereby more accurately assessing the health status of the high-voltage switchgear.

[0039] Furthermore, based on the first response data segment and the second response data segment, a state evaluation matrix is ​​constructed. The method of this application includes:

[0040] Based on the contact state characteristics, insulation state characteristics, and mechanical operation state characteristics of the contact, a state evaluation matrix is ​​constructed by associating and mapping it with the first synchronous triggering reference and the first response data segment, the second synchronous triggering reference and the second response data segment.

[0041] Specifically, contact state characteristics reflect the characteristic parameters of circuit breaker contact quality, including contact temperature changes and contact resistance changes, which can indicate whether there are problems such as poor contact or wear. Insulation state characteristics reflect the characteristic parameters of high-voltage switchgear insulation performance, including partial discharge and dielectric loss factor, which can indicate whether there are problems such as aging or damage to insulation materials. Mechanical operation state characteristics reflect the characteristic parameters of operating mechanism mechanical performance, including opening and closing time, operating torque, and movement speed, which can indicate whether there are problems such as jamming or wear of mechanical components. Correlation mapping refers to associating different state characteristics with corresponding operating mechanism control pulse signals and response data segments to form a comprehensive data structure. Correlation mapping can integrate multi-dimensional state characteristics into the state evaluation matrix, which facilitates subsequent analysis. The state evaluation matrix is ​​a multi-dimensional data structure that integrates multiple state characteristics. It is used to evaluate the health status of high-voltage switchgear, reflect the performance of the equipment in different operating cycles, and provide a basis for anomaly detection and degradation trend analysis.

[0042] Execution steps: The extracted contact state features, insulation state features, and mechanical operation state features are associated and mapped with the first synchronous triggering reference and the first response data segment, the second synchronous triggering reference and the second response data segment. Specifically, each feature parameter is aligned with the timestamp of the corresponding operation pulse signal and response data segment to form a comprehensive data structure. The associated and mapped feature parameters are integrated into a matrix to form a state evaluation matrix. Each row of the state evaluation matrix represents an operation cycle, and each column represents a state feature. Specifically, the rows of the state evaluation matrix represent different opening and closing operation cycles, and the columns of the state evaluation matrix include features such as contact temperature rise rate, partial discharge quantity, and opening and closing time.

[0043] In the above steps, by constructing a state assessment matrix, multi-dimensional state characteristics can be integrated into a unified framework, facilitating subsequent anomaly detection and degradation trend analysis. Furthermore, by analyzing the data in the state assessment matrix, abnormal operating cycles and degradation trend clusters can be identified. Specifically, if in a certain operating cycle, the contact temperature rise rate is significantly higher than the normal value, and the partial discharge also shows an abnormal increase, it indicates poor contact and insulation aging problems. Through matrix analysis, these problems can be clearly identified, and combined with the enhancement mechanism of the signal simulation verification chain, the accuracy and reliability of the assessment model can be further improved.

[0044] Furthermore, the method of this application also includes:

[0045] Based on the contact temperature subsequence, the temperature rise rate and steady-state temperature difference of the main contacts of the circuit breaker corresponding to the operating mechanism under load conditions are determined, and the contact resistance change trend is inverted by combining the load current data to extract the contact state characteristics of the contacts; wherein, the equipment operating parameter sequence includes the contact temperature subsequence, the partial discharge quantum sequence, and the mechanical characteristic parameter subsequence.

[0046] Specifically, the contact temperature subsequence refers to the part of the equipment operating parameter sequence related to contact temperature, recording the temperature changes of the main contacts of the circuit breaker at different time points; the temperature rise rate refers to the speed at which the contact temperature rises over time under load conditions, reflecting the rate at which the contacts heat up during energization; the steady-state temperature difference refers to the difference between the contact temperature after reaching a steady state and the ambient temperature or initial temperature, reflecting the degree of heat generation of the contacts during long-term operation; the load current data reflects the magnitude of the current passing through the circuit breaker during the operation of the high-voltage switchgear; the contact resistance change trend refers to the change in contact resistance over time obtained by analyzing contact temperature and load current data, reflecting the change trend of contact quality; and the contact contact state characteristics refer to the set of features extracted from parameters such as the temperature rise rate, steady-state temperature difference, and contact resistance change trend that characterize the contact contact state.

[0047] Execution steps: Extract temperature change data of the contact under load conditions from the contact temperature subsequence; select the start and end times to determine the amount of temperature change of the contact during this period; after the contact temperature reaches a steady state, record the stable temperature of the contact; combine the load current data and the contact temperature subsequence to invert the contact resistance change trend; extract the obtained temperature rise rate, steady-state temperature difference, and contact resistance change trend as contact state characteristics to obtain the contact contact state characteristics. In the above steps, by analyzing the contact temperature subsequence and load current data, the contact contact state characteristics can be accurately extracted, and problems with poor contact can be detected in a timely manner.

[0048] Furthermore, the sequence of device operating parameters includes a partial discharge quantum sequence, and the method of this application includes:

[0049] Based on the partial discharge quantum sequence, the partial discharge pulse phase distribution, discharge repetition rate, average discharge quantity, maximum discharge quantity, and rise time are extracted during multiple opening and closing operations. Combined with the trend change of dielectric loss factor, air gap discharge, surface creepage, and insulation material aging analysis are performed to extract the insulation state characteristics.

[0050] Specifically, the partial discharge quantum sequence refers to the part of the device operating parameter sequence related to partial discharge, recording information such as the amplitude, time, and phase of partial discharge events; the partial discharge pulse phase distribution refers to the phase distribution of the partial discharge pulse within the AC voltage cycle, reflecting the relative time position of the partial discharge; the discharge repetition rate refers to the number of partial discharge events occurring per unit time, reflecting the frequency of partial discharge; the average discharge quantity refers to the average discharge quantity of multiple partial discharge events, reflecting the intensity of the partial discharge; the maximum discharge quantity refers to the maximum value of a single discharge quantity in multiple partial discharge events, reflecting the extreme case of partial discharge; the rise time refers to the time it takes for the partial discharge pulse to rise from the starting point to the peak value, reflecting the steepness of the discharge pulse; the dielectric loss factor reflects the energy loss parameter of the insulating material under AC voltage, and its trend change can indicate the aging degree of the insulating material; the insulation state characteristics refer to the set of features that characterize the insulation state extracted by comprehensively considering parameters such as the partial discharge pulse phase distribution, discharge repetition rate, average discharge quantity, maximum discharge quantity, rise time, and dielectric loss factor trend change.

[0051] Execution steps: Extract phase information of each partial discharge event from the partial discharge quantum sequence; statistically analyze the phase distribution of partial discharge events within the AC voltage cycle during multiple opening and closing operations; plot a phase distribution diagram; count the number of partial discharge events within a time window to determine the discharge repetition rate, which is the average number of partial discharge events per unit time; extract the discharge magnitude of each partial discharge event from the partial discharge quantum sequence to determine the average discharge magnitude of each partial discharge event; extract the discharge magnitude of each partial discharge event from the partial discharge quantum sequence to determine the maximum single discharge magnitude among multiple partial discharge events; extract the rise time of each partial discharge event from the partial discharge quantum sequence to determine the average or maximum rise time of multiple partial discharge events; extract the dielectric loss factor from the equipment operating parameter sequence, analyze the trend of dielectric loss factor over time, identify whether there is an upward trend, and use it to indicate the aging of insulation materials; extract the obtained partial discharge pulse phase distribution, discharge repetition rate, average discharge magnitude, maximum discharge magnitude, rise time, and dielectric loss factor trend as insulation state features to obtain insulation state features.

[0052] In the above steps, insulation state characteristics are accurately extracted by analyzing the partial discharge quantum sequence and dielectric loss factor. For example, during normal operation, the partial discharge pulse phase distribution is usually concentrated in a specific phase range of the AC voltage, with a discharge repetition rate of no more than 10 times per second, indicating a low repetition rate; the average discharge quantity is no more than 100 picocoulombs, indicating a small average discharge quantity; the maximum discharge quantity is no more than 200 picocoulombs; the rise time is no more than 1 nanosecond, indicating a short rise time; and the dielectric loss factor remains stable. When the insulation material ages or is damaged, the partial discharge pulse phase distribution becomes more dispersed, the discharge repetition rate increases significantly, the average discharge quantity and maximum discharge quantity also increase, the rise time may become longer, and the dielectric loss factor will show an upward trend. Through these characteristics, the aging or damage of the insulation material can be detected in time, providing early warning of equipment health risks.

[0053] Furthermore, the sequence of equipment operating parameters includes a subsequence of mechanical characteristic parameters, and the method of this application includes:

[0054] Based on the mechanical characteristic parameter subsequence, determine the opening and closing time dispersion, energy storage motor current peak fluctuation rate, abnormal change point of operating torque, and inflection point offset of motion speed curve of the circuit breaker and disconnector corresponding to the operating mechanism in multiple opening and closing operations. Perform transmission mechanism jamming frequency and bearing wear damping coefficient change analysis to extract mechanical operation state characteristics.

[0055] Specifically, the mechanical characteristic parameter subsequence refers to the part of the equipment operating parameter sequence related to mechanical operation, recording the mechanical characteristic parameters of circuit breakers and disconnectors during opening and closing operations, including time, current, torque, and speed; the opening and closing time dispersion refers to the degree of variation in opening and closing time during multiple opening and closing operations, reflecting the stability of the operation time. Specifically, the greater the dispersion, the greater the fluctuation in operation time; the peak current volatility of the energy storage motor refers to the degree of change in the peak current of the energy storage motor during opening and closing operations, reflecting the stability of the energy storage motor's operating state. Specifically, the greater the volatility, the greater the fluctuation in the peak current; the abnormal change point of operating torque refers to the point where the operating torque changes abnormally during opening and closing operations, reflecting possible mechanical faults or abnormalities during operation.

[0056] The offset of the inflection point of the motion speed curve refers to the offset of the inflection point of the motion speed curve during the opening and closing operation, reflecting the change in the dynamic characteristics of the moving parts and indicating the jamming or wear of the transmission mechanism; the jamming frequency of the transmission mechanism refers to the frequency of jamming in multiple opening and closing operations, reflecting the health status of the transmission mechanism; the change in bearing wear damping coefficient refers to the change in damping coefficient caused by bearing wear, reflecting the degree of bearing wear. Specifically, the larger the damping coefficient, the more severe the bearing wear; the mechanical operation state characteristics refer to the set of features that characterize the mechanical operation state extracted by comprehensively considering parameters such as the dispersion of opening and closing time, the peak fluctuation rate of the energy storage motor current, the abnormal change point of the operating torque, the offset of the inflection point of the motion speed curve, the jamming frequency of the transmission mechanism, and the change in the bearing wear damping coefficient.

[0057] Execution steps: Extract time data for each opening and closing operation from the mechanical characteristic parameter subsequence, determine the standard deviation or variance of the time for multiple opening and closing operations, and obtain the opening and closing time dispersion; extract peak current data of the energy storage motor for each opening and closing operation from the mechanical characteristic parameter subsequence, determine the standard deviation or variance of the peak current, and the ratio of this to the average peak current, to obtain the peak current fluctuation rate; extract operating torque data for each opening and closing operation from the mechanical characteristic parameter subsequence, and use differential or sliding window methods to detect abnormal abrupt changes in the operating torque data; extract motion speed curve data for each opening and closing operation from the mechanical characteristic parameter subsequence, use curve fitting or derivative methods to detect the inflection points of the motion speed curve, and calculate the offset of the inflection points; identify jamming phenomena in the transmission mechanism by detecting abnormal abrupt changes in the operating torque and the offset of the inflection points of the motion speed curve, count the number of jamming phenomena in multiple opening and closing operations, and determine the jamming frequency.

[0058] By analyzing the inflection point offset of the motion speed curve and the abnormal mutation point of the operating torque, the wear degree of the bearing is assessed. Using a damping coefficient model and combining actual data, the change in the damping coefficient caused by bearing wear is calculated. The obtained opening and closing time dispersion, peak current fluctuation rate of the energy storage motor, abnormal mutation point of the operating torque, inflection point offset of the motion speed curve, jamming frequency of the transmission mechanism, and change in the bearing wear damping coefficient are extracted as mechanical operating state features to obtain the mechanical operating state characteristics. In the above steps, by analyzing the subsequence of mechanical characteristic parameters, the mechanical operating state characteristics can be accurately extracted. When mechanical components malfunction or wear, characteristic parameters such as opening and closing time dispersion, peak current fluctuation rate of the energy storage motor, abnormal mutation point of the operating torque, and inflection point offset of the motion speed curve will change significantly. Through these characteristic parameters, potential problems of mechanical components can be detected in time, and health risks of equipment can be warned in advance, improving the accuracy of the assessment.

[0059] Furthermore, by utilizing the aforementioned state evaluation matrix to identify abnormal operation cycles and degradation trend clusters, the method of this application includes:

[0060] Visual clustering is performed on the high-dimensional feature sequences in the state evaluation matrix to distinguish between normal operating condition clusters and abnormal state clusters; the operating cycle that deviates from the main cluster is identified, and the starting point and evolution path of performance degradation are located by combining sliding window variance analysis and trend slope detection to determine the degradation trend cluster.

[0061] Specifically, high-dimensional feature sequences refer to multiple feature parameter sequences contained in the state assessment matrix, reflecting multi-dimensional information such as contact status, insulation status, and mechanical operation status of high-voltage switchgear in different operating cycles. Visual clustering refers to mapping high-dimensional feature sequences to a low-dimensional space through data visualization technology. The low-dimensional space is usually two-dimensional or three-dimensional, and clustering algorithms are used to group similar data points into different clusters to intuitively identify normal and abnormal states. Normal operating condition clusters refer to the set of data points in the clustering results that represent the normal operating state of the equipment, and the feature parameters of these data points fluctuate within the normal range. Abnormal state clusters refer to the set of data points in the clustering results that represent the abnormal operating state of the equipment, and the feature parameters of these data points deviate from the normal range, indicating potential problems with the equipment.

[0062] The operational cycle deviating from the main cluster refers to the operational cycle corresponding to data points that significantly deviate from the normal operating condition cluster in the clustering results. The operational cycle includes abnormal behavior of the equipment. Sliding window variance analysis refers to calculating the variance of the data within a fixed-size window by sliding it across the time series data to detect data fluctuations. Trend slope detection refers to identifying the upward or downward trend of the data by calculating the trend slope of the time series data to determine the starting point and evolution path of performance degradation. Degradation trend clusters refer to the clusters formed by analyzing and identifying performance degradation trends, reflecting the pattern and path of equipment performance degradation.

[0063] Execution steps: Extract high-dimensional feature sequences from the state assessment matrix, including contact state features, insulation state features, and mechanical operation state features. Normalize the feature sequences to ensure comparability between different features. Use dimensionality reduction techniques such as principal component analysis to map the high-dimensional feature sequences to a low-dimensional space. Apply clustering algorithms, including K-means (k-means clustering algorithm) or DBSCAN (Density-Based Spatial Clustering of Applications with Noise), to cluster the low-dimensional data into different clusters. Display the clustering results using visualization tools such as scatter plots to distinguish between normal operating condition clusters and abnormal state clusters.

[0064] Data points that significantly deviate from normal operating conditions are identified. The corresponding operating cycles of these data points contain abnormal equipment behavior. The operating cycles of these deviating data points are statistically analyzed to form a list of abnormal operating cycles. Sliding window analysis of variance is applied to the time series data of each abnormal operating cycle to determine the variance within the window and identify time points where the variance increases significantly, indicating the starting point of performance degradation. The trend slope is calculated for the time series data of each abnormal operating cycle to identify upward or downward trends, determining the starting point and evolution path of performance degradation—the process from normal to abnormal states. The identified performance degradation trends are then clustered to form degradation trend clusters. The characteristics of each cluster are analyzed to determine the degradation pattern and path. In these steps, visual clustering allows for intuitive identification of normal and abnormal states, quickly locating potential problems. Combined with sliding window analysis of variance and trend slope detection, the starting point and evolution path of performance degradation can be accurately located, providing a reliable basis for equipment maintenance and repair.

[0065] Furthermore, using a health status assessment model enhanced by cross-correlation and mutation correlation of signal simulation verification chains, the method of this application includes:

[0066] Finite element spatial coding modeling is performed on the integrated busbar, circuit breaker arc-extinguishing chamber, insulator, and sensor network topology corresponding to the high-voltage switchgear. Coding modeling is performed in combination with spatial constraints, thermal constraints, and mechanical constraints. Using a non-dominated sorting mechanism guided by multi-objective optimization, the static power Pareto front and dynamic power Pareto front are obtained. Based on the static power Pareto front and dynamic power Pareto front, the non-dominated solution set is screened. According to the non-dominated solution set, the response sensitivity of key events including hot spots, partial discharge, and closing bounce is simulated and evaluated. A signal simulation verification chain is set up that is associated with the simulation eye diagram opening, crosstalk level, and clock jitter.

[0067] Specifically, finite element spatial coding modeling refers to using the finite element method to model the integrated busbar, circuit breaker arc-extinguishing chamber, insulator, and sensor network topology of a high-voltage switchgear. The finite element method is a numerical analysis technique used to simulate the mechanical, thermal, and electrical behavior of complex physical systems. Spatial constraints refer to the physical space limitations considered during the modeling process, including the size, shape, and layout of the equipment, ensuring that the model conforms to actual physical space requirements. Thermal constraints refer to the thermal limitations considered during the modeling process, including the temperature distribution and heat dissipation conditions during equipment operation, ensuring that the model conforms to actual thermal behavior. Mechanical constraints refer to the mechanical limitations considered during the modeling process, including the mechanical strength of the equipment and the range of motion of moving parts, ensuring that the model conforms to actual mechanical behavior. Multi-objective optimization refers to simultaneously considering multiple objective functions to find the optimal solution that satisfies all objectives, including static power consumption and dynamic power consumption.

[0068] Non-dominated sorting mechanisms are used in multi-objective optimization. By comparing the quality of solutions, solutions are divided into different non-dominated layers to find the Pareto optimal solution set. The static power Pareto front refers to the solution set with optimal power consumption under static conditions in multi-objective optimization, where static conditions refer to the high-voltage switchgear being in a stable operating state. The dynamic power Pareto front refers to the solution set with optimal power consumption under dynamic conditions in multi-objective optimization, where dynamic conditions refer to the high-voltage switchgear being in the process of opening and closing operations. The non-dominated solution set refers to the optimal solution set found in multi-objective optimization, where these solutions achieve a balance among multiple objective functions. The signal simulation verification chain refers to a verification mechanism based on signal simulation, used to evaluate the model's response sensitivity to key events. Furthermore, key events include hot spots, partial discharge, and closing bounce. Simulated eye diagram opening, crosstalk level, and clock jitter are key indicators of signal integrity.

[0069] Execution steps: Model the various components of the high-voltage switchgear using finite element analysis software, including the integrated busbar, circuit breaker arc-extinguishing chamber, insulators, and sensor network topology. During modeling, consider spatial constraints including equipment size and layout, thermal constraints including heat dissipation and temperature distribution, and mechanical constraints including mechanical strength and range of motion. Define optimization objectives, including static power consumption and dynamic power consumption. Optimize using a multi-objective optimization algorithm, finding the Pareto front through a non-dominated sorting mechanism to obtain the static power consumption Pareto front and the dynamic power consumption Pareto front. These fronts represent the optimal power consumption solution set under different conditions. Select non-dominated solutions from the Pareto fronts; these solutions achieve balance among multiple objective functions. Choose appropriate non-dominated solutions as the basis for simulation. Preferably, through finite element spatial coding modeling and multi-objective optimization, the behavior of the high-voltage switchgear under different operating conditions can be accurately simulated, finding the optimal power consumption solution set.

[0070] Based on the non-dominated solution set, the response sensitivity of the model to key events, including hotspots, partial discharges, and closing bounce, is simulated and evaluated. A signal simulation verification chain is used, setting signal integrity indicators related to the simulated eye diagram opening, crosstalk level, and clock jitter to assess the model's response capability to these key events. The signal simulation verification chain, including a signal generator, transmission line model, and receiver model, is constructed to evaluate the model's response sensitivity to key events, ensuring the model's reliability and accuracy in practical applications. Preferably, the signal simulation verification chain can evaluate the model's response sensitivity to key events, improving the model's reliability and accuracy. In the above steps, the simulation verification chain shows that when the local discharge increases, the simulated eye diagram opening decreases significantly, indicating that the health status assessment model can respond sensitively to partial discharge events, providing strong support for equipment health status assessment.

[0071] In summary, the beneficial effects of the embodiments of this application are:

[0072] This application provides a method and system for assessing the health status of high-voltage switchgear. It utilizes the acquisition of equipment operating parameter sequences from the high-voltage switchgear, including operating mechanisms such as circuit breakers and disconnectors. Based on these sequences, a first response data segment and a second response data segment are determined. A status assessment matrix is ​​constructed based on these segments, and this matrix is ​​used to identify abnormal operating cycles and degradation trend clusters. By using a health status assessment model enhanced by cross-correlation and mutation correlation of signal simulation verification chains as an enhancement mechanism, a health risk warning for the high-voltage switchgear is provided.

[0073] Example 2, based on the same inventive concept as the high-voltage switchgear health status assessment method in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides a high-voltage switchgear health status assessment system, wherein the system includes:

[0074] Data acquisition module M100: Acquires the sequence of equipment operating parameters of the high-voltage switchgear, wherein the operating mechanisms in the high-voltage switchgear include circuit breakers and disconnect switches.

[0075] Response data segment determination module M200: Based on the operating mechanism and the sequence of equipment operating parameters, determine the first response data segment and the second response data segment.

[0076] State assessment matrix construction module M300: Based on the first response data segment and the second response data segment, construct a state assessment matrix and use the state assessment matrix to identify abnormal operation cycles and degradation trend clusters.

[0077] Risk warning module M400: Based on the abnormal operation cycle and degradation trend clusters, a health status assessment model with cross-correlation and mutation correlation of signal simulation verification chain as the enhancement mechanism is used to provide health risk warning for the high-voltage switchgear.

[0078] Furthermore, the response data segment determination module M200 is used to perform the following method:

[0079] Using the tripping control pulse signal of the operating mechanism as the first synchronization trigger reference, the sequence of operating parameters of the equipment is timestamped and synchronized, and the first response data segment within the time window corresponding to the tripping control pulse signal is extracted.

[0080] Furthermore, the response data segment determination module M200 is also used to perform the following method:

[0081] Using the closing control pulse signal of the operating mechanism as the second synchronization trigger reference, the sequence of operating parameters of the equipment is timestamped and synchronized, and the second response data segment within the time window corresponding to the closing control pulse signal is extracted.

[0082] Furthermore, the state evaluation matrix construction module M300 is used to perform the following method:

[0083] Based on the contact state characteristics, insulation state characteristics, and mechanical operation state characteristics of the contact, a state evaluation matrix is ​​constructed by associating and mapping it with the first synchronous triggering reference and the first response data segment, the second synchronous triggering reference and the second response data segment.

[0084] Furthermore, the state evaluation matrix construction module M300 is also used to perform the following method:

[0085] Based on the contact temperature subsequence, the temperature rise rate and steady-state temperature difference of the main contacts of the circuit breaker corresponding to the operating mechanism under load conditions are determined, and the contact resistance change trend is inverted by combining the load current data to extract the contact state characteristics of the contacts; wherein, the equipment operating parameter sequence includes the contact temperature subsequence, the partial discharge quantum sequence, and the mechanical characteristic parameter subsequence.

[0086] Furthermore, the state evaluation matrix construction module M300 is also used to perform the following method:

[0087] Based on the partial discharge quantum sequence, the partial discharge pulse phase distribution, discharge repetition rate, average discharge quantity, maximum discharge quantity, and rise time are extracted during multiple opening and closing operations. Combined with the trend change of dielectric loss factor, air gap discharge, surface creepage, and insulation material aging analysis are performed to extract the insulation state characteristics.

[0088] Furthermore, the state evaluation matrix construction module M300 is also used to perform the following method:

[0089] Based on the mechanical characteristic parameter subsequence, determine the opening and closing time dispersion, energy storage motor current peak fluctuation rate, abnormal change point of operating torque, and inflection point offset of motion speed curve of the circuit breaker and disconnector corresponding to the operating mechanism in multiple opening and closing operations. Perform transmission mechanism jamming frequency and bearing wear damping coefficient change analysis to extract mechanical operation state characteristics.

[0090] Furthermore, the state evaluation matrix construction module M300 is used to perform the following method:

[0091] Visual clustering is performed on the high-dimensional feature sequences in the state evaluation matrix to distinguish between normal operating condition clusters and abnormal state clusters; the operating cycle that deviates from the main cluster is identified, and the starting point and evolution path of performance degradation are located by combining sliding window variance analysis and trend slope detection to determine the degradation trend cluster.

[0092] Furthermore, the risk warning module M400 is used to perform the following methods:

[0093] Finite element spatial coding modeling is performed on the integrated busbar, circuit breaker arc-extinguishing chamber, insulator, and sensor network topology corresponding to the high-voltage switchgear. Coding modeling is performed in combination with spatial constraints, thermal constraints, and mechanical constraints. Using a non-dominated sorting mechanism guided by multi-objective optimization, the static power Pareto front and dynamic power Pareto front are obtained. Based on the static power Pareto front and dynamic power Pareto front, the non-dominated solution set is screened. According to the non-dominated solution set, the response sensitivity of key events including hot spots, partial discharge, and closing bounce is simulated and evaluated. A signal simulation verification chain is set up that is associated with the simulation eye diagram opening, crosstalk level, and clock jitter.

[0094] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0095] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A high-voltage switchgear health state evaluation method, characterized in that, The method comprises: obtaining a device operation parameter sequence of a high-voltage switch cabinet, wherein the operating mechanism in the high-voltage switch cabinet comprises a circuit breaker and a disconnector; determining a first response data segment and a second response data segment based on the operating mechanism and the device operation parameter sequence; constructing a state evaluation matrix based on the first response data segment and the second response data segment, and identifying an abnormal operation period and a deterioration trend cluster group by using the state evaluation matrix; using a health state evaluation model with enhanced mechanisms of cross-correlation and variation correlation of a signal simulation verification chain to perform health risk early warning on the high-voltage switch cabinet through the abnormal operation period and the deterioration trend cluster group.

2. The health state evaluation method of a high-voltage switch cabinet according to claim 1, characterized in that, The method comprises: taking the opening control pulse signal of the operating mechanism as a first synchronous trigger reference, performing timestamp synchronization calibration on the device operation parameter sequence, and intercepting the first response data segment in the time window corresponding to the opening control pulse signal.

3. The health state evaluation method of a high-voltage switch cabinet according to claim 2, characterized in that, The method comprises: taking the closing control pulse signal of the operating mechanism as a second synchronous trigger reference, performing timestamp synchronization calibration on the device operation parameter sequence, and intercepting the second response data segment in the time window corresponding to the closing control pulse signal.

4. The health state evaluation method of a high-voltage switch cabinet according to claim 3, characterized in that, The method comprises: based on the contact contact state feature, the insulation state feature, and the mechanical operation state feature, associating and mapping the first synchronous trigger reference and the first response data segment, and the second synchronous trigger reference and the second response data segment to construct a state evaluation matrix.

5. The health state evaluation method of a high-voltage switchgear according to claim 4, characterized in that, The method further comprises: determining the temperature rise rate and the steady-state temperature difference of the main contact of the circuit breaker corresponding to the operating mechanism under the load working condition according to the contact temperature subsequence, and combining the load current data to inverse the contact resistance change trend to extract the contact contact state feature. The device operation parameter sequence comprises a contact temperature subsequence, a partial discharge quantity subsequence, and a mechanical characteristic parameter subsequence.

6. The health state evaluation method of a high-voltage switchgear according to claim 5, characterized in that, The device operation parameter sequence comprises a partial discharge quantity subsequence, and the method comprises: extracting the partial discharge pulse phase distribution, the discharge repetition rate, the average discharge quantity, the maximum discharge quantity, and the rising edge time in the multiple opening and closing operation processes according to the partial discharge quantity subsequence, combining the trend change of the dielectric loss factor, and performing air gap discharge, surface creeping, and insulation material aging analysis to extract the insulation state feature.

7. The health state evaluation method of a high-voltage switchgear according to claim 6, characterized in that, The device operation parameter sequence comprises a mechanical characteristic parameter subsequence, and the method comprises: determining the opening and closing time dispersion, the energy storage motor current peak fluctuation rate, the operation torque abnormal mutation point, and the motion speed curve inflection point offset of the circuit breaker and the disconnector corresponding to the operating mechanism in the multiple opening and closing operations according to the mechanical characteristic parameter subsequence, performing transmission mechanism jamming frequency and bearing wear damping coefficient change analysis to extract the mechanical operation state feature.

8. The health state evaluation method of a high-voltage switchgear according to claim 1, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

9. The health state evaluation method of a high-voltage switchgear according to claim 8, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

10. A high voltage switchgear health condition assessment system, characterized in that, The system comprises the following modules: The system comprises the following modules: The system comprises the following modules: The system comprises the following modules: ​

Images