A power equipment fault diagnosis method and system, device, and medium

Abstract

This invention provides a method, system, equipment, and medium for fault diagnosis of power equipment, relating to the field of fault diagnosis technology. The method includes: triggering a complete operation of a disconnector switch in a gas-insulated switchgear as an independent diagnostic event; configuring N time-series segments; synchronously collecting multi-source unsteady-state response data; performing segmented constraint mapping on the multi-source unsteady-state response data based on the N time-series segments; inverting and outputting disconnector switch anomalies; and performing time-series evolution analysis on the disconnector switch anomalies output from multiple independent diagnostic events to construct a degradation evolution trajectory. This invention solves the technical problem of existing technologies' difficulty in accurately and promptly diagnosing faults in gas-insulated switchgear, leading to insufficient accuracy in fault diagnosis and reliability in early warning. It achieves high-precision and timely fault diagnosis and early warning for gas-insulated switchgear, improving the accuracy of fault diagnosis and the reliability of early warning.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, specifically to a method, system, equipment, and medium for diagnosing faults in power equipment. Background Technology

[0002] Gas-insulated switchgear is a crucial component of power systems, widely used in substations and distribution systems to connect, disconnect, and isolate electrical energy. By sealing high-voltage conductors and switching components within a metal casing filled with SF6 gas, it offers advantages such as small footprint, high insulation performance, and strong operational reliability, playing a vital role in ensuring the safe and stable operation of power systems. As power systems evolve towards higher voltage, larger capacity, and higher reliability, gas-insulated switchgear operates under frequent operation and complex conditions, inevitably leading to wear, aging, or performance degradation of its internal disconnecting switches, drive mechanisms, and contact components.

[0003] Current technologies for fault diagnosis of gas-insulated switchgear mainly rely on two methods: periodic maintenance and online monitoring. Periodic maintenance involves comprehensive inspections and tests of the equipment at fixed intervals through manual patrols and preventative testing. Online monitoring uses sensors to collect real-time operating parameters of the equipment, such as gas pressure, temperature, and partial discharge signals, and makes fault judgments based on preset thresholds or simple algorithms. However, periodic maintenance is difficult to accurately capture dynamic changes in the equipment's condition, and is prone to over-maintenance or under-maintenance, resulting in wasted resources or missed faults. While online monitoring can acquire data in real time, fault analysis based on preset thresholds or simple algorithms is prone to false alarms or missed alarms because it cannot fully consider the complex operating conditions of the equipment and the correlation between various parameters, thus affecting the safe and stable operation of power equipment.

[0004] Existing technologies have limitations in accurately and promptly diagnosing faults in gas-insulated switchgear, resulting in insufficient accuracy in fault diagnosis and reliability in early warning. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, equipment, and medium for diagnosing power equipment faults, in order to solve the technical problem that existing technologies are unable to accurately and timely diagnose faults in gas-insulated switchgear, resulting in insufficient accuracy in fault diagnosis and reliability in early warning.

[0006] In view of the above problems, this application provides a method, system, equipment and medium for diagnosing power equipment faults.

[0007] The first aspect of this application provides a method for diagnosing faults in power equipment. This method includes: when a disconnecting switch in a gas-insulated switchgear performs an opening or closing operation, triggering a complete operation of the disconnecting switch as an independent diagnostic event; configuring N time-series segments based on the motion response characteristics of the disconnecting switch; and, after the independent diagnostic event is triggered, synchronously collecting multi-source unsteady-state response data directly coupled to the operating behavior of the disconnecting switch within the same operation event, according to the N time-series segments. The multi-source unsteady-state response data includes the transient drive response of the disconnecting switch's drive structure. The study analyzes the micro-vibration response of the GIS shell, the transient pressure disturbance of the enclosed SF6 gas, and the transient electromagnetic disturbance response between the poles of the disconnector switch. Based on N time-series segments, the study performs segmented constraint mapping on the multi-source unsteady-state response data to construct an equivalent operating impedance evolution channel that evolves with the operation event over time. Utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuity relationship between adjacent time-series segments of the equivalent operating impedance evolution channel, the study inverts and outputs disconnector switch anomalies. The study then performs time-series evolution analysis on the disconnector switch anomalies output from multiple independent diagnostic events to construct a degradation evolution trajectory.

[0008] Optionally, within each time segment, a sliding time window decomposition is performed on the equivalent operating impedance evolution channel. Based on the statistical median and quantile intervals of the impedance signal within the sliding time window, a stage impedance baseline for the corresponding time segment is constructed. Under the constraint of the stage impedance baseline, a first-order difference sequence is calculated for the equivalent impedance signal within each time segment, and impedance abrupt change points are identified based on the first-order difference sequence. For each time segment, within a preset node window of the impedance abrupt change point, a disturbance energy index obtained by mapping the micro-vibration response, transient pressure disturbance, and transient electromagnetic disturbance response is calculated. The disturbance energy index is coupled with the corresponding impedance change amplitude to construct a disturbance superposition strength index. Continuity constraint analysis is performed on the stage impedance baseline, impedance abrupt change point distribution, and disturbance superposition strength index within adjacent time segments to identify contact-type anomalies and drive structure-type anomalies. Based on the identification results and the corresponding time segment, an isolation switch anomaly is output.

[0009] Optionally, based on the stage impedance baseline within each time segment, the stage impedance slope and stage impedance baseline offset of the equivalent impedance over time are calculated, and the distribution density of impedance abrupt change points is statistically analyzed. Within adjacent time segments, the correlation coefficient between the stage impedance slope and the stage impedance baseline offset is calculated, and it is determined whether the correlation coefficient is lower than a preset stage continuity threshold. When the correlation coefficient is lower than the preset stage continuity threshold, and the distribution density of impedance abrupt change points and the disturbance superposition intensity index within the corresponding time segment are higher than those of other time segments, it is determined to be a contact-type anomaly. When the stage impedance slope and stage impedance baseline offset are detected to change in the same direction in all time segments, and the correlation coefficient between each time segment is higher than the stage continuity threshold, it is determined to be a drive structure-type anomaly.

[0010] Optionally, during the opening or closing operation of the disconnecting switch, motion response signals characterizing the motion state of the disconnecting switch are continuously acquired. The motion response signals include at least one of the displacement response, velocity response, or equivalent drive current response of the drive mechanism. The first and second derivatives of the motion response signals are calculated to obtain derivative responses characterizing the changes in the velocity and acceleration of the disconnecting switch, respectively. Feature points satisfying the state switching conditions are identified in the derivative responses. The state switching conditions include derivative zero-crossing points, derivative extreme points, and derivative sign abrupt change points. Segmentation boundaries are constructed based on the feature points, and N time-series segments are configured using the segmentation boundaries.

[0011] Optionally, for each independent diagnostic event, a degradation state vector is constructed based on the stage impedance baseline offset, stage impedance slope, and disturbance superposition intensity index within the corresponding time segment; the degradation evolution trajectory is constructed by arranging the disconnector abnormality and degradation state vector in chronological order according to time; and fault diagnosis reporting management is performed based on the degradation evolution trajectory.

[0012] Optionally, the degradation trend slope and cumulative degradation amount are analyzed on the degradation evolution trajectory, and corresponding early warning level signals are reported based on the analysis results and anomaly type. The early warning level signals include prompt level early warning, attention level early warning and severe level early warning; fault diagnosis reporting management is performed based on the early warning level signals.

[0013] Optionally, the N timing segments include a start-up timing segment, a contact sliding stage timing segment, and a termination positioning stage timing segment.

[0014] A second aspect of this application provides a power equipment fault diagnosis system, comprising: a timing segmentation configuration module, used to trigger a complete operation of a disconnector switch in a gas-insulated switchgear as an independent diagnostic event when the disconnector switch performs an opening or closing operation, and to configure N timing segments based on the motion response characteristics of the disconnector switch; and a data acquisition module, used to synchronously acquire multi-source unsteady-state response data directly coupled to the operating behavior of the disconnector switch within the same operation event, based on the N timing segments, after the independent diagnostic event is triggered. The multi-source unsteady-state response data includes the transient drive response of the disconnector switch drive structure and the micro-current response of the GIS housing. The system includes: vibration response, transient pressure disturbance of enclosed SF6 gas, and transient electromagnetic disturbance response between disconnector poles; a segmented constraint mapping module for performing segmented constraint mapping on the multi-source unsteady-state response data based on N time segments, constructing an equivalent operating impedance evolution channel that evolves with the operation event time; a disconnector abnormal output module for inverting and outputting disconnector abnormalities by utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuity change relationship between adjacent time segments of the equivalent operating impedance evolution channel; and a time-series evolution analysis module for performing time-series evolution analysis on disconnector abnormalities output from multiple independent diagnostic events, constructing a degradation evolution trajectory.

[0015] A third aspect of this application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the above-described power equipment fault diagnosis method.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of the above-described power equipment fault diagnosis method.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: When a disconnector in a gas-insulated switchgear performs an opening or closing operation, a complete operation of the disconnector is triggered as an independent diagnostic event. N time-series segments are configured based on the motion response characteristics of the disconnector. After the independent diagnostic event is triggered, multi-source unsteady-state response data directly coupled to the disconnector's operating behavior is synchronously collected within the same operation event according to the N time-series segments. This multi-source unsteady-state response data includes the transient drive response of the disconnector's drive structure, the micro-vibration response of the GIS housing, the transient pressure disturbance of the enclosed SF6 gas, and the transient electromagnetic disturbance response between the disconnector poles. Based on the N time-series segments, the multi-source unsteady-state response data is segmented and constrained to construct an equivalent operating impedance evolution channel that evolves with the operation event over time. Using the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuous change relationships between adjacent time-series segments of the equivalent operating impedance evolution channel, disconnector anomalies are inverted and output. The disconnector anomalies output from multiple independent diagnostic events are analyzed using time-series evolution to construct a degradation evolution trajectory. It achieves high-precision, real-time diagnosis and early warning of faults in gas-insulated switchgear, improving the accuracy of fault diagnosis and the reliability of early warning.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for diagnosing power equipment faults provided in this application.

[0021] Figure 2 This is a schematic diagram of the structure of a power equipment fault diagnosis system provided in this application.

[0022] Figure 3 A schematic diagram of the structure of an exemplary electronic device provided in this application.

[0023] Explanation of reference numerals in the attached figures: 11. Timing segmentation configuration module; 12. Data acquisition module; 13. Segmentation constraint mapping module; 14. Anomaly output module; 15. Timing evolution analysis module; 300. Bus; 301. Receiver; 302. Processor; 303. Transmitter; 304. Memory; 305. Bus interface. Detailed Implementation

[0024] This application provides a method, system, equipment, and medium for diagnosing power equipment faults, addressing the technical problem of insufficient accuracy and reliability in fault diagnosis and early warning due to the difficulty in accurately and promptly diagnosing faults in gas-insulated switchgear. It achieves high-precision, real-time fault diagnosis and early warning for gas-insulated switchgear, improving the accuracy of fault diagnosis and the reliability of early warning.

[0025] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0026] Example 1, as Figure 1 As shown, this application provides a method for diagnosing faults in power equipment, the method comprising: When a disconnector in a gas-insulated switchgear performs an opening or closing operation, a complete operation of the disconnector is triggered as an independent diagnostic event, and N timing segments are configured based on the motion response characteristics of the disconnector.

[0027] Specifically, in gas-insulated switchgear, when a disconnecting switch performs an opening or closing operation, the issuance of the opening or closing command is used as the initial triggering condition. The entire continuous process from the start of the action to the end of the mechanical movement and the stable positioning of the disconnecting switch is defined as a complete operation cycle. This operation cycle is triggered, collected, and analyzed as an independent diagnostic event, ensuring that the data collection and analysis for each operation are independent of each other.

[0028] Based on independent diagnostic events, during the operation of the disconnecting switch, motion response characteristics are continuously collected throughout the entire process by deploying motion state sensing units at the disconnecting switch drive mechanism or locations directly coupled to its motion. These motion response characteristics refer to the physical quantity changes that reflect the motion behavior of the disconnecting switch drive mechanism and contacts. Based on these motion response characteristics, N time-series segments are configured, dividing a complete disconnecting switch operation into N time-series segments with clear physical meaning in terms of motion mechanism. Each time-series segment corresponds to a typical motion stage in the disconnecting switch operation process.

[0029] By treating a complete operation as an independent diagnostic event and combining motion response characteristics to structurally divide the entire process of opening or closing the disconnecting switch, the originally continuous and highly unsteady mechanical operation process is transformed into multiple time segments with clear physical boundaries and comparability. This provides a more detailed basis for accurately identifying the fault type and location, thereby improving the effectiveness and reliability of the entire fault diagnosis scheme.

[0030] Furthermore, based on the motion response characteristics of the disconnecting switch, N time-series segments are configured, including: continuously acquiring motion response signals characterizing the motion state of the disconnecting switch during the opening or closing operation of the disconnecting switch; the motion response signals include at least one of the displacement response, velocity response, or equivalent drive current response of the drive mechanism; calculating the first and second derivatives of the motion response signals to obtain derivative responses characterizing the changes in the velocity and acceleration of the disconnecting switch, respectively; identifying feature points in the derivative responses that satisfy the state switching conditions, including derivative zero-crossing points, derivative extreme points, and derivative sign abrupt change points; constructing segment boundaries based on the feature points, and configuring N time-series segments using the segment boundaries.

[0031] Furthermore, the method also includes: N timing segments including a start-up timing segment, a contact sliding stage timing segment, and a termination positioning stage timing segment.

[0032] Specifically, during the opening or closing operation of the disconnecting switch, motion response signals characterizing the switch's motion state are continuously acquired. Continuous acquisition means acquiring signal data uninterruptedly throughout the entire process—before the action starts, during the action, and until the action stabilizes—with a fixed or adaptive sampling period, ensuring the integrity and continuity of the motion state over time. The motion response signals include at least one of the following: displacement response, velocity response, or equivalent drive current response of the drive mechanism. For example, by installing a displacement sensor on the drive mechanism, real-time displacement change data during operation is acquired as the displacement response, reflecting the opening and closing stroke of the contacts. The velocity response is obtained by time-synchronized sampling or numerical differentiation of the displacement change data, reflecting the speed of movement and the start-stop process. The equivalent drive current response is obtained by measuring the current in the drive circuit, indirectly characterizing the force and load changes on the drive mechanism.

[0033] The motion response signal of the disconnecting switch is obtained. The continuously acquired displacement response, velocity response, or equivalent drive current response is digitized, converting the discrete-time sequence into a data point sequence with a uniform sampling interval. Then, the first derivative is calculated on the data point sequence in the time domain. By dividing the difference between adjacent sampling points by the sampling time interval, the instantaneous rate of change of the continuous motion signal at each moment is obtained, which characterizes the derivative response of the disconnecting switch's motion velocity change. Next, based on the first derivative sequence, the second derivative is calculated again. Similarly, by dividing the difference between adjacent first derivative points by the sampling time interval, the derivative response of the acceleration change is obtained, thus reflecting the acceleration change trend and sudden changes in mechanical force of the disconnecting switch drive mechanism during operation. To suppress high-frequency noise interference, the acquired motion response signal can be low-pass filtered or averaged before the derivative calculation to ensure the smoothness and physical interpretability of the derivative sequence.

[0034] The calculated derivative response is compared with the state switching conditions, and feature points satisfying the preset state switching conditions are further identified on the time axis. These state switching conditions are used to determine whether the motion state of the disconnector has changed, and include derivative zero-crossing points, derivative extremum points, and derivative sign abrupt change points. A derivative zero-crossing point is a point where the derivative changes from positive to negative or vice versa, passing through zero; it represents a critical moment when the motion state or force direction changes. A derivative extremum point is a point where the derivative reaches a local maximum or minimum value; it represents a typical node where the acceleration or deceleration reaches its peak. A derivative sign abrupt change point is a point where the derivative suddenly changes sign in adjacent moments; it represents the instant when the drive structure or contact state undergoes a significant change. For example, in the closing operation of a disconnector, when the drive mechanism approaches the fully closed position, the speed gradually decreases, and the first derivative of its speed response exhibits an extremum point. This extremum point can be used as a feature point satisfying the state switching conditions.

[0035] Using the distribution of feature points on the time axis as the basis for segmentation, segment boundaries are constructed between adjacent feature points. Based on these segment boundaries, a complete disconnecting switch operation process is divided into N time-series segments with clear physical meaning in terms of motion mechanism. The N time-series segments include the start-up time-series segment, the contact sliding stage time-series segment, and the termination positioning stage time-series segment. The start-up time-series segment is the stage when the disconnecting switch begins operation, the drive mechanism just starts moving, and the motion gradually accelerates from a standstill. The contact sliding stage time-series segment is the stage when the contacts begin to separate or make contact and slide within a certain distance, and the motion is relatively stable. The termination positioning stage time-series segment is the stage when the contacts reach the final position, the drive mechanism performs positioning, and the motion gradually stops. For example, in a disconnector switch opening operation, the displacement response data of the drive mechanism collected is as follows: Within 0-0.1s, the displacement slowly increases from 0mm to 3mm. During this stage, the changes in velocity and acceleration are relatively small, which conforms to the characteristics of the start-up timing segment and can be set as the start-up timing segment; Within 0.1-0.4s, the displacement rapidly increases from 3mm to 25mm. The velocity and acceleration are relatively stable and large, which belongs to the process of the contacts starting to separate and sliding within a certain distance, and can be divided into the contact sliding stage timing segment; Within 0.4-0.5s, the displacement increases from 25mm to 26mm and then remains basically unchanged. The velocity gradually decreases to zero, and the acceleration also changes accordingly. This is the stage when the contacts reach the final opening position and the drive mechanism performs positioning, and can be set as the termination positioning stage timing segment.

[0036] By configuring N time-series segments based on motion response characteristics, the complex and continuous operation process of the disconnecting switch is refined into multiple stages with different characteristics and states, enabling fault diagnosis to accurately correspond to specific operation stages and improving the accuracy and timeliness of power equipment fault diagnosis.

[0037] After an independent diagnostic event is triggered, multi-source unsteady-state response data directly coupled with the operation behavior of the disconnector switch are synchronously collected within the same operation event according to the N time-series segments. The multi-source unsteady-state response data includes the transient drive response of the disconnector switch drive structure, the micro-vibration response of the GIS housing, the transient pressure disturbance of the enclosed SF6 gas, and the transient electromagnetic disturbance response between the poles of the disconnector switch.

[0038] Specifically, when an independent diagnostic event is triggered, based on N pre-configured time segments, multi-source unsteady-state response data directly coupled to the disconnector's operation are synchronously acquired within the time axis of the same disconnector operation event. Synchronous acquisition refers to simultaneously acquiring signals from different physical channels at a fixed or adaptive sampling rate under the same time reference to ensure accurate and reliable time correspondence between signals. For example, a high-speed data acquisition card and a precise time synchronization device are used to synchronize the acquisition clocks of each sensor, ensuring strict time alignment of the acquired data and guaranteeing that the dynamic characteristics of signals from different channels within each time segment are accurately mapped to the disconnector's operation.

[0039] Multi-source unsteady-state response data refers to data generated from multiple different sources during the operation of a disconnector switch, which changes rapidly over time and lacks a stable state. This includes the transient drive response of the disconnector switch drive structure, the micro-vibration response of the GIS housing, the transient pressure disturbance of the enclosed SF6 gas, and the transient electromagnetic disturbance response between the disconnector switch poles. The transient drive response of the disconnector switch drive structure refers to the dynamic characteristics exhibited by the drive mechanism at the instant of disconnector switch operation, reflecting the force and motion applied by the drive mechanism to enable the disconnector switch to open or close. By installing force and displacement sensors at key parts of the drive mechanism, such as the motor output shaft and transmission gear connections, data on the magnitude, direction, and displacement changes of the drive force in different time segments are collected in real time. For example, during the startup phase, the driving force gradually increases to overcome the initial static friction of the disconnecting switch, causing the contacts to begin moving. During the contact sliding phase, the driving force remains relatively stable to maintain the uniform sliding of the contacts. During the termination positioning phase, the driving force gradually decreases until it reaches zero, allowing the contacts to accurately reach their final position.

[0040] Micro-vibration response refers to the minute displacement changes in the GIS housing caused by mechanical vibration during disconnector operation. Because disconnector operation generates mechanical shocks and vibrations, these vibration signals are transmitted to the GIS housing. By installing a high-sensitivity accelerometer on the GIS housing, micro-vibration signals are collected in various time segments. The frequency, amplitude, and other characteristics of these micro-vibration signals reflect the operating status of mechanical components during disconnector operation, such as the presence of loosening or wear. In GIS equipment, SF6 gas serves as an insulating and arc-extinguishing medium, and its pressure state is crucial for the normal operation of the GIS equipment. Transient pressure disturbance refers to the instantaneous change in the internal pressure of the sealed SF6 gas during disconnector operation. By installing a pressure sensor inside the GIS equipment, the pressure changes of SF6 gas in different time segments are monitored in real time. For example, during the disconnector closing operation, an electric arc may be generated at the moment of contact, leading to a local increase in gas temperature and pressure. Transient electromagnetic disturbance response is the electromagnetic signal generated by the instantaneous change of the electromagnetic field between the poles during the operation of a disconnecting switch. Since the opening or closing operation of the disconnecting switch changes the circuit topology, it causes a rapid change in the electromagnetic field between the poles. By installing electromagnetic sensors between the poles of the disconnecting switch to collect transient electromagnetic disturbance signals, the intensity, frequency and other characteristics of the transient electromagnetic disturbance signals can reflect the electrical performance of the disconnecting switch during operation, such as whether there are insulation faults, arc reignition and other problems.

[0041] For example, data acquisition is performed according to three pre-configured time segments: start-up, contact sliding, and termination positioning. In the start-up time segment (0-0.1s), the transient drive response of the drive structure is acquired, showing the driving force gradually increasing from 0N to 50N. In the micro-vibration response of the GIS housing, the accelerometer detects a maximum acceleration of 0.2g. The transient pressure disturbance of the sealed SF6 gas shows the pressure rising from 0.5MPa to 0.52MPa. In the transient electromagnetic disturbance response between the disconnecting switch poles, the electromagnetic sensor detects a maximum electromagnetic field strength of 10mT. In the contact sliding stage (0.1-0.4s), the driving force remains around 50N, the housing micro-vibration acceleration stabilizes at around 0.1g, the SF6 gas pressure stabilizes at 0.52MPa, and the electromagnetic field strength between the poles fluctuates between 8-10mT. During the termination positioning phase, which is segmented into 0.4-0.5s, the driving force gradually decreases to 0N, the micro-vibration acceleration of the shell gradually decreases to 0, the SF6 gas pressure drops slightly to 0.515MPa, and the inter-electrode electromagnetic field strength also gradually decreases to close to 0mT.

[0042] By synchronously acquiring multi-source unsteady-state response data directly coupled with the operating behavior of disconnecting switches, it is possible to comprehensively and accurately obtain the working status information of disconnecting switches at different operating stages. This allows for more accurate identification of whether a fault exists in the disconnecting switch during operation, as well as the type, location, and severity of the fault. Consequently, the accuracy and specificity of fault diagnosis are improved, ensuring the safe and stable operation of gas-insulated switchgear.

[0043] Based on N time-series segments, the multi-source unsteady-state response data is segmented and constrained to construct an equivalent operating impedance evolution channel that evolves with the time of the operating event.

[0044] Specifically, the multi-source unsteady-state response data collected in each time series segment is divided according to the corresponding time series boundary to ensure that the data in each time series segment only reflects the operational characteristics and physical processes of that stage. Based on the segmentation results, the collected multi-source unsteady-state response data is classified and organized according to their respective time series segments, so that the data corresponds to specific operational stages in the time dimension.

[0045] For the multi-source unsteady-state response data within each time segment, key parameters reflecting the characteristics related to operating impedance are extracted. For example, driving force and displacement data are extracted from the transient driving response of the drive structure. Based on the relationship between the driving force and displacement, the mechanical impedance of the drive mechanism is calculated. Mechanical impedance is defined as the ratio of resistance to displacement, representing the mechanical response of the disconnecting switch at that stage. Voltage and current data are extracted from the transient electromagnetic disturbance response between the disconnecting switch poles, and electrical impedance is calculated. Electrical impedance refers to the ratio between voltage and current in a circuit, reflecting the influence of electromagnetic fluctuations on switch operation. The relationship between gas pressure changes and operation is analyzed from the transient pressure disturbance data of enclosed SF6 gas. Based on the disconnecting switch operating mechanism and fluid dynamics model, a mapping relationship between gas pressure and contact force and motion impedance is established. For example, by calculating the changes in contact force and friction caused by pressure changes, or by performing electrical model analysis based on the influence of gas pressure on electric field distribution and dielectric impedance, the changes in mechanical and electrical impedance caused by pressure disturbances are converted into quantitative indicators, and the gas disturbance of gas state on operating impedance is obtained.

[0046] Mechanical impedance, electrical impedance, and gas disturbance are normalized using the min-max normalization method to ensure they are compared under the same dimensions. Then, impedances from different sources, including mechanical impedance, electrical impedance, and gas disturbance, are weighted and integrated. By assigning appropriate weights to each impedance, the influence of each factor on the disconnector impedance during operation is comprehensively reflected, yielding the equivalent impedance value reflecting the disconnector operation within this time segment. The weighted integration expression is: Z = w1 × mechanical impedance + w2 × electrical impedance + w3 × gas disturbance, where Z is the equivalent operating impedance within this time segment, and w1, w2, and w3 are the weighting coefficients for mechanical, electrical, and gas disturbances, respectively. These can be optimized using empirical data to ensure that the influence of each factor on the operating impedance is reasonably reflected.

[0047] The equivalent operating impedance values ​​calculated for each time segment are connected in chronological order to form an equivalent operating impedance evolution channel that evolves with the operation event time. The equivalent operating impedance evolution channel can intuitively display the change of the operating impedance of the disconnecting switch throughout the entire operation process, including the impedance characteristics and change trends at different stages such as start-up, sliding, and termination positioning.

[0048] By constructing an equivalent operating impedance evolution channel through segmented constraint mapping and multi-source data fusion, the operating status and performance changes of the disconnecting switch at different operating stages can be reflected more comprehensively and accurately, providing a more direct and targeted basis for subsequent fault diagnosis. Analysis of the equivalent operating impedance evolution channel makes it easier to detect abnormal impedance changes during operation, thereby enabling timely and accurate determination of whether the disconnecting switch has a fault, as well as the type and location of the fault. This significantly improves the efficiency and accuracy of electrical equipment fault diagnosis, ensuring the safe and stable operation of gas-insulated switchgear.

[0049] By utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuous change relationship between adjacent time segments of the equivalent operating impedance evolution channel, the output disconnect switch anomaly can be inverted.

[0050] Furthermore, utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuous change relationships between adjacent time segments of the equivalent operating impedance evolution channel, the output disconnect switch anomaly is inverted, including: within each time segment, performing a sliding time window decomposition on the equivalent operating impedance evolution channel; constructing a stage impedance baseline for the corresponding time segment based on the statistical median and quantile intervals of the impedance signal within the sliding time window; under the constraint of the stage impedance baseline, calculating a first-order difference sequence for the equivalent impedance signal within each time segment, and based on the... The first-order difference sequence identifies impedance abrupt change points; for each time segment, the disturbance energy index, mapped from the micro-vibration response, transient pressure disturbance, and transient electromagnetic disturbance response, is calculated within a preset node window of the impedance abrupt change point; the disturbance energy index is coupled with the corresponding impedance change amplitude to construct a disturbance superposition strength index; continuity constraint analysis is performed on the stage impedance baseline, impedance abrupt change point distribution, and disturbance superposition strength index within adjacent time segments to identify contact-type anomalies and drive structure-type anomalies; based on the identification results and the corresponding time segment, disconnect switch anomalies are output.

[0051] Specifically, within each time segment, a sliding time window decomposition is performed on the equivalent operating impedance evolution path. This decomposition divides the entire time segment into multiple consecutive small windows of fixed length. Local features are captured by analyzing the data within each window. The fixed time length of the sliding time window is determined based on the physical characteristics and duration of the disconnector operation. First, the total operating time of a complete opening or closing operation of the switch can be calculated. Then, based on the local dynamic features to be captured, the total time is divided into several consecutive small windows, ensuring that each window contains enough data points for statistical analysis while also capturing local abrupt changes. For example, 1 / 10 of the total operating time or a fixed number of milliseconds, such as 5ms, can be used as the window length. For the impedance signal within each sliding time window, its statistical median and quantile interval are calculated to construct the stage impedance baseline corresponding to each time segment. The statistical median reflects the intermediate level of impedance within the sliding time window, and the quantile interval reflects the distribution range of data within the sliding time window. The constructed stage impedance baseline serves as a reference standard for normal impedance changes within the time segment, reflecting the normal impedance level and overall trend of that stage. Under the constraint of the stage impedance baseline, a first-order difference operation is performed on the equivalent impedance signal within each time segment. By calculating the difference between impedance values ​​at adjacent times, a first-order difference sequence is obtained. Impedance abrupt change points are identified based on the first-order difference sequence. When the difference value exceeds a preset threshold, it can be determined as an impedance abrupt change point, indicating that there may be abnormal contact of the contacts or abnormality of the driving structure. For each time segment, within a preset node window at each impedance abrupt change point, the disturbance energy index, mapped from the micro-vibration response, transient pressure disturbance, and transient electromagnetic disturbance response, is calculated. The preset node window is a data window centered on the impedance abrupt change point, extending forward and backward by a certain time range. This time range is determined based on the duration of the local response of the switch action and the propagation speed of various disturbances, for example, 10 ms forward and 10 ms backward. By analyzing the changes in different response data within this window, the disturbance energy index is calculated to quantify the transient impact of mechanical, gas, and electromagnetic disturbances on the operation process. The disturbance energy index is coupled with the corresponding impedance change amplitude. This coupling calculation can employ a weighted or normalized superposition method: first, the disturbance energy index and impedance change amplitude are normalized to be within the same range; then, weights are assigned based on the importance of each index for anomaly identification; finally, the weights are multiplied by the corresponding index and summed to obtain the disturbance superposition intensity index, which more comprehensively reflects the impact of abnormal conditions on the operating impedance of the disconnecting switch. By analyzing the continuity relationship of stage impedance baseline, impedance mutation point distribution, and disturbance superposition intensity index within adjacent time segments, the spatial and temporal characteristics of anomalies are constrained and judged, thereby distinguishing contact-type anomalies from drive structure-type anomalies. Based on the identification results and the corresponding time segment output isolating switch anomalies, the location and approximate time of the anomaly are determined.

[0052] By comprehensively analyzing the amplitude distribution, abrupt change characteristics, disturbance superposition effect, and timing continuity of the equivalent operating impedance, we can accurately identify and distinguish the types of anomalies during the operation of disconnecting switches, thereby achieving efficient and accurate diagnosis of power equipment faults.

[0053] Furthermore, identifying contact-related anomalies and drive structure-related anomalies includes: calculating the equivalent impedance change over time as a function of the stage impedance slope and the stage impedance baseline offset based on the stage impedance baseline within each time segment, and statistically analyzing the distribution density of impedance abrupt change points; calculating the correlation coefficient between the stage impedance slope and the stage impedance baseline offset within adjacent time segments, and determining whether the correlation coefficient is lower than a preset stage continuity threshold; when the correlation coefficient is lower than the preset stage continuity threshold, and the distribution density of impedance abrupt change points and the disturbance superposition intensity index within the corresponding time segment are higher than those of other time segments, it is determined to be a contact-related anomaly; when the stage impedance slope and stage impedance baseline offset are detected to change in the same direction in all time segments, and the correlation coefficient between each time segment is higher than the stage continuity threshold, it is determined to be a drive structure-related anomaly.

[0054] Specifically, for each time segment, based on the established stage impedance baseline, the stage impedance slope of the equivalent impedance over time is calculated. The stage impedance slope reflects the rate of change of the equivalent impedance over time within that time segment, reflecting the upward or downward trend of the operating impedance in that stage. Simultaneously, the stage impedance baseline offset within each time segment is calculated, representing the degree of deviation between the actual equivalent impedance value and the stage impedance baseline in different time segments. This offset is obtained by calculating the difference between the actual impedance value and the corresponding baseline value and averaging the results. Furthermore, impedance abrupt change points within each time segment are statistically analyzed to obtain the impedance abrupt change point distribution density, i.e., the number of impedance abrupt change points per unit time, reflecting the severity of impedance changes in different time periods.

[0055] Within adjacent time series segments, the correlation coefficient between the stage impedance slope and the stage impedance baseline offset is calculated. For two adjacent time series segments, the stage impedance slope sequence and the stage impedance baseline offset sequence are extracted for each segment. The mean of the two sets of sequences is calculated, followed by the covariance and standard deviation of each set. The Pearson correlation coefficient is obtained by dividing the covariance by the standard deviation. The correlation coefficient measures the degree of linear correlation between two variables; a value closer to 1 indicates a stronger correlation, a value closer to 0 indicates a weaker correlation, and a negative value indicates opposite trends. By calculating the correlation coefficient, it is determined whether the changes of these two characteristic parameters within adjacent time series segments are consistent.

[0056] The calculated correlation coefficient is compared with a preset stage continuity threshold, which is set based on a large amount of normal operating data and experience. For example, a threshold of 0.6 is used to determine whether the continuity of characteristic parameter changes between adjacent time series segments is normal. When the correlation coefficient is lower than the preset stage continuity threshold, the distribution density of impedance abrupt change points and the intensity of disturbance superposition within the corresponding time series segment are further analyzed. If these two indicators are higher than those of other time series segments, it indicates that the impedance change within that time series segment is abnormally drastic and the disturbance intensity is large. In this case, it is determined to be a contact-related anomaly. This is because if the contacts experience problems such as poor contact or wear during operation, it can lead to abrupt changes in local impedance and generate strong mechanical and electrical disturbances.

[0057] When the stage impedance slope and stage impedance baseline offset show a consistent change in the same direction across all time segments, and the correlation coefficients between each time segment are all higher than the stage continuity threshold, it is determined to be a drive structure anomaly. Drive structure anomalies occur because if a fault occurs in the drive structure during operation, such as a drive motor failure or jamming of transmission components, the impedance change trend will be relatively consistent throughout the entire operation, and the continuity of changes between adjacent time segments will be good. This method enables rapid location of the fault type, location, and severity, achieving efficient and accurate diagnosis of power equipment faults.

[0058] For example, in the startup timing segment, the stage impedance slope is 5Ω / s, the stage impedance baseline offset is 2Ω, the impedance abrupt change point distribution density is 1 / 0.02s, and the disturbance superposition intensity index is 0.3. In the contact separation timing segment, the stage impedance slope is 10Ω / s, the stage impedance baseline offset is 5Ω, the impedance abrupt change point distribution density is 3 / 0.02s, and the disturbance superposition intensity index is 0.7. In the termination positioning timing segment, the stage impedance slope is 8Ω / s, the stage impedance baseline offset is 4Ω, the impedance abrupt change point distribution density is 2 / 0.02s, and the disturbance superposition intensity index is 0.5. The correlation coefficients between the stage impedance slope and the stage impedance baseline offset for adjacent timing segments, i.e., startup and contact separation, and contact separation and termination positioning, are calculated to be 0.3 and 0.4, respectively, while the preset stage continuity threshold is 0.6, and the correlation coefficients are all below the threshold. Furthermore, the impedance abrupt change point distribution density and disturbance superposition intensity index within the contact separation timing segment were significantly higher than those in the other two timing segments, thus classifying it as a contact-related anomaly. Subsequent inspection revealed ablation marks on the contact surface, verifying the accuracy of the diagnosis.

[0059] By combining the analysis of stage impedance slope, baseline offset, abrupt change distribution, and disturbance superposition intensity, not only can abnormalities in the operation of disconnecting switches be identified, but also local contact faults and overall degradation of the drive structure can be clearly distinguished. This enables rapid location of power equipment fault types and locations, effectively improving the efficiency, accuracy, and reliability of power equipment fault diagnosis.

[0060] A time-series evolution analysis was performed on the disconnector anomalies output by multiple independent diagnostic events to construct a degradation evolution trajectory.

[0061] Furthermore, a time-series evolution analysis is performed on the disconnector switch anomalies output by multiple independent diagnostic events to construct a degradation evolution trajectory. This includes: for each independent diagnostic event, constructing a degradation state vector based on the stage impedance baseline offset, stage impedance slope, and disturbance superposition intensity index within the corresponding time segment; arranging the disconnector switch anomalies and degradation state vectors in chronological order to construct a degradation evolution trajectory; and performing fault diagnosis reporting management based on the degradation evolution trajectory.

[0062] Specifically, each independent diagnostic event formed by a tripping or closing operation is used as the basic analysis unit. The diagnostic results of each time segment within the event are summarized, and a corresponding degradation state vector is constructed based on the stage impedance baseline offset, stage impedance slope, and disturbance superposition intensity index extracted within the time segment. The degradation state vector can be represented as [stage impedance baseline offset, stage impedance slope, disturbance superposition intensity index]. Each degradation state vector serves as a quantitative description of the health status of the disconnector under the independent diagnostic event and corresponds to the disconnector abnormality type identified in the current event.

[0063] Each independent diagnostic event contains disconnector switch anomaly information and a corresponding degradation state vector. Following the chronological order of occurrence of each independent diagnostic event, the disconnector switch anomalies and corresponding degradation state vectors are arranged chronologically. The discrete event-level diagnostic results are then concatenated on a time axis to form a degradation evolution trajectory that reflects the change in disconnector switch state over operating time. This degradation evolution trajectory is essentially a time-series path describing the gradual evolution of the disconnector switch's operational performance and abnormal characteristics, clearly demonstrating its performance degradation trend over time. Based on the constructed degradation evolution trajectory, fault diagnosis reporting management is further implemented. Through comprehensive analysis of degradation trends, evolution rates, and anomaly persistence, continuous monitoring and dynamic evaluation of the disconnector switch's operating status are achieved.

[0064] By constructing a degradation evolution trajectory, information from multiple independent diagnostic events can be integrated and correlated to comprehensively analyze the performance degradation process of disconnectors from a temporal perspective. This not only helps to gain a deeper understanding of the fault mechanisms and development patterns of disconnectors but also enables the early detection of potential faults, providing maintenance personnel with accurate fault warnings and decision-making support. Compared to relying solely on single diagnostic events for fault diagnosis, fault diagnosis and reporting management based on degradation evolution trajectories significantly improves the accuracy and timeliness of fault diagnosis, reduces the probability and scope of fault occurrence, and effectively enhances the reliability and safety of disconnector operation.

[0065] Furthermore, fault diagnosis reporting management is performed based on the degradation evolution trajectory, including: analyzing the degradation trend slope and cumulative degradation amount of the degradation evolution trajectory, and reporting corresponding early warning level signals based on the analysis results and anomaly types. The early warning level signals include prompt-level early warning, attention-level early warning, and severe-level early warning; and performing fault diagnosis reporting management based on the early warning level signals.

[0066] Specifically, the trend of the degradation state vector changing over time in the degradation evolution trajectory is fitted. Time is used as the independent variable, with the time point of each independent diagnostic event as the time coordinate. Degradation indicators in the corresponding degradation state vector, such as stage impedance baseline offset and stage impedance slope, are used as dependent variables to construct a discrete data sequence of degradation quantity changing over time. This time sequence is modeled using a trend fitting method, preferably linear least squares fitting. This involves minimizing the squared error between the actual degradation quantity and the fitted value to obtain a fitted straight line characterizing the overall trend of degradation evolution. After fitting, the slope of this fitted line is taken as the degradation trend slope. The degradation trend slope represents the average change in degradation quantity per unit time, used to quantify the rate of performance degradation of the disconnector switch. A positive slope and a larger value indicate accelerated degradation, while a slope close to zero indicates a relatively stable state. Simultaneously, a cumulative degradation analysis is performed on the degradation evolution trajectory. The cumulative degradation quantity is obtained by accumulating the deviations of key characteristic parameters at each time point relative to the initial normal state, reflecting the overall degree of degradation of the disconnector switch from its initial normal state to the current moment. The greater the cumulative degradation, the more severe the degradation process experienced by the disconnecting switch, and the higher the risk of failure.

[0067] Based on the degradation trend slope, cumulative degradation amount, and identified anomaly types, corresponding warning level signals are reported according to preset rules. Warning level signals are divided into alert level, attention level, and severe level. For example, when the degradation trend slope is small and the cumulative degradation amount is within a preset range, and the anomaly type is minor contact failure, an alert level warning is issued, indicating to maintenance personnel that the disconnector switch has a minor anomaly and needs to be checked at an appropriate time. The preset range can be determined based on historical operating data of the disconnector switch under normal or light wear conditions. By selecting a degradation state vector data from a long-term stable operating period, the mean and fluctuation range of its cumulative degradation amount are statistically analyzed. For example, the mean ± 2 times the standard deviation can be taken as the normal degradation interval. For instance, if the statistically obtained cumulative degradation amount under normal operating conditions is 0.15 and the standard deviation is 0.05, the preset range of cumulative degradation amount can be set to 0-0.25, and the degradation trend slope is set to less than 0.01. When both the cumulative degradation amount and the trend slope fall within this range, combined with the anomaly type, an alert level warning is issued. When the degradation trend slope is moderate and the cumulative degradation amount has increased (moderate usually means the degradation rate is significantly higher than normal, but has not yet reached the accelerated failure stage, such as a degradation trend slope in the range of 0.01-0.03 and a cumulative degradation amount in the range of 0.25-0.45), a warning at the attention level is issued based on the specific anomaly type, reminding maintenance personnel to closely monitor the operating status of the disconnector and increase the frequency of inspections. When the degradation trend slope is large and the cumulative degradation amount exceeds a certain threshold (e.g., the degradation trend slope threshold is set to be greater than 0.03, the cumulative degradation amount danger threshold is set to be greater than 0.45), and the anomaly type is severe contact erosion or drive structure failure, a severe warning is issued, requiring maintenance personnel to immediately stop the equipment and conduct emergency repairs. Based on the reported warning level signal, fault diagnosis reporting management is executed, including anomaly information recording, maintenance alarm push notifications, and maintenance priority adjustment, to achieve hierarchical management and response to the operating status of the disconnector.

[0068] By analyzing the degradation trend slope and cumulative degradation amount of the degradation evolution trajectory, the performance degradation of the disconnecting switch can be obtained comprehensively and accurately from both speed and degree dimensions. Combined with the anomaly type, different levels of early warning signals are reported, providing operation and maintenance personnel with clear and explicit fault risk prompts. This enables them to take targeted measures according to the warning level, thereby effectively avoiding further deterioration of the fault and improving the reliability and safety of the disconnecting switch operation of power equipment.

[0069] Example 2, based on the same inventive concept as the power equipment fault diagnosis method in the foregoing examples, such as... Figure 2 As shown, this application provides a power equipment fault diagnosis system, wherein the power equipment fault diagnosis system includes: The timing segmentation configuration module 11 is used to trigger a complete operation of the disconnector switch as an independent diagnostic event when the disconnector switch in the gas-insulated switchgear performs an opening or closing operation, and to configure N timing segments based on the motion response characteristics of the disconnector switch; the data acquisition module 12 is used to synchronously acquire multi-source unsteady-state response data directly coupled to the operating behavior of the disconnector switch within the same operation event according to the N timing segments after the independent diagnostic event is triggered. The multi-source unsteady-state response data includes the transient drive response of the disconnector switch drive structure, the micro-vibration response of the GIS housing, and the transient pressure disturbance of the enclosed SF6 gas. The transient electromagnetic disturbance response between the poles of the disconnecting switch; the segmented constraint mapping module 13, used to perform segmented constraint mapping on the multi-source unsteady-state response data based on N time segments, and construct the equivalent operating impedance evolution channel that evolves with the operation event time; the anomaly output module 14, used to invert and output the disconnecting switch anomaly by utilizing the impedance amplitude distribution characteristics, impedance change characteristics, disturbance superposition characteristics and continuous change relationship between adjacent time segments of the equivalent operating impedance evolution channel in each time segment; the time evolution analysis module 15, used to perform time evolution analysis on the disconnecting switch anomalies output by multiple independent diagnostic events, and construct the degradation evolution trajectory.

[0070] Furthermore, the abnormal output module 14 is also used for: performing sliding time window decomposition on the equivalent operating impedance evolution channel within each time segment, constructing a stage impedance baseline for the corresponding time segment based on the statistical median and quantile interval of the impedance signal within the sliding time window; calculating a first-order difference sequence for the equivalent impedance signal within each time segment under the constraint of the stage impedance baseline, and identifying impedance abrupt change points based on the first-order difference sequence; calculating the disturbance energy index obtained by mapping micro-vibration response, transient pressure disturbance, and transient electromagnetic disturbance response within a preset node window of the impedance abrupt change point for each time segment; coupling the disturbance energy index with the corresponding impedance change amplitude to construct a disturbance superposition strength index; performing continuity constraint analysis on the stage impedance baseline, impedance abrupt change point distribution, and disturbance superposition strength index within adjacent time segments to identify contact-type abnormalities and drive structure-type abnormalities; and outputting disconnect switch abnormalities based on the identification results and the corresponding time segment.

[0071] Furthermore, the abnormal output module 14 is also used to: calculate the slope of the equivalent impedance changing with time and the offset of the stage impedance baseline based on the stage impedance baseline in each time segment, and count the distribution density of impedance abrupt change points; calculate the correlation coefficient between the stage impedance slope and the stage impedance baseline offset in adjacent time segments, and determine whether the correlation coefficient is lower than a preset stage continuity threshold; when the correlation coefficient is lower than the preset stage continuity threshold, and the distribution density of impedance abrupt change points and the disturbance superposition intensity index in the corresponding time segment are higher than those in other time segments, it is determined to be a contact-type abnormality; when the stage impedance slope and the stage impedance baseline offset are detected to change in the same direction in all time segments, and the correlation coefficient between each time segment is higher than the stage continuity threshold, it is determined to be a drive structure-type abnormality.

[0072] Furthermore, the timing segmentation configuration module 11 is also used to: continuously acquire motion response signals characterizing the motion state of the disconnector during the opening or closing operation of the disconnector, wherein the motion response signals include at least one of the displacement response, velocity response, or equivalent drive current response of the drive mechanism; calculate the first and second derivatives of the motion response signals to obtain derivative responses characterizing the changes in the velocity and acceleration of the disconnector, respectively; identify feature points in the derivative responses that satisfy the state switching conditions, wherein the state switching conditions include derivative zero-crossing points, derivative extreme points, and derivative sign abrupt change points; construct segmentation boundaries based on the feature points, and configure N timing segments using the segmentation boundaries.

[0073] Furthermore, the time-series evolution analysis module 15 is also used to: construct a degradation state vector for each independent diagnostic event based on the stage impedance baseline offset, stage impedance slope and disturbance superposition intensity index within the corresponding time-series segment; construct a degradation evolution trajectory by arranging the disconnector abnormality and degradation state vector in time sequence; and perform fault diagnosis reporting management based on the degradation evolution trajectory.

[0074] Furthermore, the time-series evolution analysis module 15 is also used to: analyze the degradation trend slope and cumulative degradation amount of the degradation evolution trajectory, and report the corresponding early warning level signal according to the analysis results and anomaly type, wherein the early warning level signal includes a prompt level early warning, a attention level early warning and a severe level early warning; and perform fault diagnosis reporting management according to the early warning level signal.

[0075] Furthermore, the timing segmentation configuration module 11 is also used for: N timing segments including a start timing segment, a contact sliding stage timing segment, and a termination positioning stage timing segment.

[0076] Example 3: Based on the same inventive concept as the power equipment fault diagnosis method in the foregoing embodiments, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the power equipment fault diagnosis method described in any one of the above embodiments.

[0077] Appendix Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0078] Example 4: Based on the same inventive concept as the power equipment fault diagnosis method in the foregoing examples, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of the power equipment fault diagnosis method described in any one of Examples 1 above.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for diagnosing faults in power equipment, characterized in that, The method includes: When the disconnector in the gas-insulated switchgear performs opening or closing operations, a complete operation process of the disconnector is triggered as an independent diagnostic event, and N timing segments are configured based on the motion response characteristics of the disconnector. After an independent diagnostic event is triggered, multi-source unsteady-state response data directly coupled with the operation behavior of the disconnector switch are synchronously collected within the same operation event according to the N time sequence segments. The multi-source unsteady-state response data includes the transient drive response of the disconnector switch drive structure, the micro-vibration response of the GIS shell, the transient pressure disturbance of the enclosed SF6 gas, and the transient electromagnetic disturbance response between the poles of the disconnector switch. Based on N time-series segments, the multi-source unsteady-state response data is segmented and constrained to construct an equivalent operating impedance evolution channel that evolves with the time of the operating event. By utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuous change relationship between adjacent time segments of the equivalent operating impedance evolution channel, the abnormality of the output isolating switch can be inverted. A time-series evolution analysis was performed on the disconnector anomalies output by multiple independent diagnostic events to construct a degradation evolution trajectory.

2. The power equipment fault diagnosis method as described in claim 1, characterized in that, By utilizing the impedance amplitude distribution characteristics, impedance abrupt change characteristics, disturbance superposition characteristics, and continuous change relationships between adjacent time segments of the equivalent operating impedance evolution channel, output disconnect switch anomalies are inverted, including: Within each time segment, a sliding time window decomposition is performed on the equivalent operating impedance evolution channel, and the stage impedance baseline of the corresponding time segment is constructed based on the statistical median and quantile interval of the impedance signal within the sliding time window. Under the constraint of the stage impedance baseline, a first-order differential sequence is calculated for the equivalent impedance signal in each time segment, and impedance abrupt change points are identified based on the first-order differential sequence. For each time segment, the disturbance energy index obtained by mapping the micro-vibration response, transient pressure disturbance, and transient electromagnetic disturbance response is calculated within the preset node window of the impedance abrupt change point. The disturbance energy index is coupled with the corresponding impedance change amplitude to construct a disturbance superposition strength index. Continuity constraint analysis is performed on the stage impedance baseline, impedance abrupt change point distribution, and disturbance superposition intensity index within adjacent time segments to identify contact-type anomalies and drive structure-type anomalies. Based on the identification results and the corresponding timing segment, the disconnect switch is output as abnormal.

3. The power equipment fault diagnosis method as described in claim 2, characterized in that, Identify contact-related anomalies and drive structure-related anomalies, including: Based on the stage impedance baseline within each time segment, calculate the stage impedance slope and stage impedance baseline offset of the equivalent impedance as a function of time, and statistically analyze the distribution density of impedance abrupt change points. Within adjacent time segments, calculate the correlation coefficient between the stage impedance slope and the stage impedance baseline offset, and determine whether the correlation coefficient is lower than a preset stage continuity threshold. When the correlation coefficient is lower than the preset stage continuity threshold, and the impedance mutation point distribution density and disturbance superposition intensity index in the corresponding time segment are higher than those in other time segments, it is determined to be a contact-type anomaly. When the stage impedance slope and stage impedance baseline offset are detected to change in the same direction in all time segments, and the correlation coefficient between each time segment is higher than the stage continuity threshold, it is determined to be a drive structure anomaly.

4. The power equipment fault diagnosis method as described in claim 1, characterized in that, Based on the motion response characteristics of the disconnector switch, N timing segments are configured, including: During the opening or closing operation of the disconnecting switch, motion response signals characterizing the motion state of the disconnecting switch are continuously collected. The motion response signals include at least one of the displacement response, velocity response, or equivalent drive current response of the drive mechanism. The first and second derivatives of the motion response signal are calculated to obtain the derivative responses characterizing the changes in the velocity and acceleration of the disconnecting switch, respectively. Identify feature points in the derivative response that satisfy state switching conditions, including derivative zero-crossing points, derivative extreme points, and derivative sign abrupt change points. Segmentation boundaries are constructed based on the feature points, and N time-series segments are configured using the segmentation boundaries.

5. The power equipment fault diagnosis method as described in claim 1, characterized in that, A time-series evolution analysis was performed on the disconnector anomalies output by multiple independent diagnostic events to construct a degradation evolution trajectory, including: For each independent diagnostic event, a degradation state vector is constructed based on the stage impedance baseline offset, stage impedance slope, and disturbance superposition intensity index within the corresponding time segment. Based on the abnormal and degraded state vectors of the disconnecting switch, the degraded evolution trajectory is constructed by arranging them in chronological order. Fault diagnosis and reporting management are performed based on the described degradation and evolution trajectory.

6. The power equipment fault diagnosis method as described in claim 5, characterized in that, Fault diagnosis and reporting management is performed based on the aforementioned degradation and evolution trajectory, including: The degradation trend slope and cumulative degradation amount are analyzed on the degradation evolution trajectory. Based on the analysis results and the anomaly type, a corresponding early warning level signal is reported. The early warning level signal includes a prompt level warning, a concern level warning, and a severe level warning. Fault diagnosis and reporting management are carried out based on the aforementioned warning level signals.

7. The power equipment fault diagnosis method as described in claim 1, characterized in that, The N timing segments include the start-up timing segment, the contact sliding stage timing segment, and the termination positioning stage timing segment.

8. A fault diagnosis system for power equipment, characterized in that, The step of implementing the power equipment fault diagnosis method according to any one of claims 1 to 7, wherein the power equipment fault diagnosis system comprises: The timing segmentation configuration module is used to trigger a complete operation process of the disconnecting switch as an independent diagnostic event when the disconnecting switch in the gas-insulated switchgear performs opening or closing operations, and to configure N timing segments based on the motion response characteristics of the disconnecting switch. The data acquisition module is used to synchronously acquire multi-source unsteady-state response data directly coupled with the operation behavior of the disconnector switch within the same operation event, based on the N time sequence segments after the independent diagnostic event is triggered. The multi-source unsteady-state response data includes the transient drive response of the disconnector switch drive structure, the micro-vibration response of the GIS shell, the transient pressure disturbance of the sealed SF6 gas, and the transient electromagnetic disturbance response between the poles of the disconnector switch. The segmented constraint mapping module is used to perform segmented constraint mapping on the multi-source unsteady response data based on N time segments, and to construct the equivalent operating impedance evolution channel that evolves with the time of the operating event. The abnormal output module is used to invert and output the abnormality of the isolating switch by utilizing the impedance amplitude distribution characteristics, impedance change characteristics, disturbance superposition characteristics and continuous change relationship between adjacent time segments of the equivalent operating impedance evolution channel in each time segment. The temporal evolution analysis module is used to perform temporal evolution analysis on the disconnector switch anomalies output by multiple independent diagnostic events and construct the degradation evolution trajectory.

9. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the steps of the power equipment fault diagnosis method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the power equipment fault diagnosis method according to any one of claims 1 to 8.

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