Method and system for detecting partial discharge of non-power-cut operation insulation tool

Abstract

The invention discloses a method and a system for detecting partial discharge of an insulation tool working without power failure, and relates to the technical field of partial discharge detection. The method comprises the following steps: synchronously acquiring a high-frequency current detection signal and an ultrasonic detection signal of the insulation tool in a detection period; dividing the detection period into a plurality of continuous time periods containing at least one effective discharge event; for each time period, respectively analyzing high-frequency current propagation stability and ultrasonic propagation stability; based on the two types of stability analysis results, determining the discharge propagation dominant characteristics of each time period; and determining the dominant discharge mechanism in the detection period according to the judgment results of all the time periods. The method can accurately identify the dominant mechanism when two kinds of discharge coexist in a complex environment, solves the problem of criterion failure caused by signal aliasing in a traditional method, improves the stability and reliability of a detection result, and provides a scientific basis for the use, maintenance or replacement of an insulation tool.

Description

Technical Field

[0001] This application relates to the field of partial discharge detection technology, specifically to a method and system for detecting partial discharge in insulating tools used in live-line working. Background Technology

[0002] In live-line work on power systems, insulating tools are critical equipment for ensuring the personal safety of workers and the stable operation of the power grid. Their insulation performance directly affects operational safety and power supply reliability. These tools are exposed to complex outdoor environments for extended periods, and their surfaces are prone to adverse conditions due to humidity changes, condensation, or contaminant deposits, leading to surface corona discharge or micro-discharge. Furthermore, with increasing service life, insulating tools may exhibit aging or develop internal partial discharges due to potential defects in the manufacturing process.

[0003] In actual operating conditions, surface discharge and internal partial discharge are not mutually exclusive; they often coexist and superimpose, resulting in complex mixed-state characteristics in the detection signal. Specifically, the high-frequency current pulse amplitude generated by surface discharge is large, easily masking the weak signal of internal partial discharge; surface water film or contaminant layer alters the boundary conditions for ultrasonic wave propagation, causing distortion or changes in the attenuation characteristics of the ultrasonic signal generated by internal discharge; the partial temporal overlap of the two types of discharge further renders traditional "presence / absence" and "strong / weak" judgments based on signal amplitude or single events ineffective.

[0004] Most existing partial discharge detection methods assume that surface discharge and internal partial discharge can be directly distinguished, or rely solely on the single characteristics of high-frequency current signals and ultrasonic signals for judgment. They fail to effectively solve the signal aliasing problem when multiple discharge mechanisms coexist in complex environments, making it difficult to accurately identify the dominant discharge mechanism in the detection signal. This results in insufficient reliability of the insulation status assessment results of insulating tools, posing potential risks to the safety of uninterrupted power supply operations. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a method and system for detecting partial discharge of insulating tools used in uninterrupted power supply operations.

[0006] A first aspect provides a method for detecting partial discharge in insulating tools used in uninterrupted power supply operations. The method includes: simultaneously acquiring high-frequency current detection signals and ultrasonic detection signals on the insulating tool during a detection period; dividing the detection period into multiple consecutive time periods; wherein each time period contains at least one valid discharge event; for each time period, analyzing the high-frequency current propagation stability based on the high-frequency current detection signals and the ultrasonic propagation stability based on the ultrasonic detection signals; for each time period, determining the dominant discharge propagation characteristic based on the corresponding high-frequency current propagation stability analysis results and the ultrasonic propagation stability analysis results; wherein the dominant discharge propagation characteristic includes internal partial discharge dominance, surface discharge dominance, or a combination of both; and determining the dominant discharge mechanism within the detection period based on the determination results of the dominant discharge propagation characteristics for all time periods.

[0007] Preferably, analyzing the stability of the high-frequency current propagation includes: analyzing the amplitude stability and the concentration of the occurrence time of the discharge pulse in the high-frequency current detection signal; analyzing the stability of the ultrasonic propagation includes: analyzing the arrival time distribution, amplitude variation, and attenuation consistency of the ultrasonic response signal in the ultrasonic detection signal.

[0008] Preferably, the determination of the dominant discharge propagation characteristic is based on the following logic: if both the high-frequency current propagation stability analysis result and the ultrasonic propagation stability analysis result indicate stability, then it is determined to be dominated by internal partial discharge; if both the high-frequency current propagation stability analysis result and the ultrasonic propagation stability analysis result indicate instability, then it is determined to be dominated by surface discharge; otherwise, it is determined to be a composite characteristic.

[0009] Preferably, the analysis of the high-frequency current propagation stability specifically involves: extracting effective discharge pulses from the high-frequency current detection signal within the time period; calculating the amplitude variation coefficient of the effective discharge pulses to characterize amplitude stability; calculating the occurrence time clustering coefficient of the effective discharge pulses to characterize temporal concentration; and determining whether the high-frequency current propagation stability is stable or unstable based on the comparison relationship between the amplitude variation coefficient and a first amplitude threshold, and between the time clustering coefficient and a first time clustering threshold.

[0010] Preferably, the analysis of the ultrasonic propagation stability specifically involves: extracting the effective ultrasonic response signal from the ultrasonic detection signal within the time period; calculating the arrival time dispersion coefficient of the effective ultrasonic response signal; calculating the amplitude variation coefficient of the effective ultrasonic response signal; calculating the attenuation coefficient based on the attenuation curve of the effective ultrasonic response signal, and calculating the variation coefficient of the attenuation coefficient; and determining whether the ultrasonic propagation stability is stable or unstable based on the comparison relationship between the arrival time dispersion coefficient, the amplitude variation coefficient, and the variation coefficient of the attenuation coefficient and corresponding set thresholds.

[0011] Preferably, determining the dominant discharge mechanism within the detection period includes: statistically analyzing the number of first time periods determined to be dominated by internal partial discharge, the number of second time periods determined to be dominated by surface discharge, and the number of third time periods determined to be composite characteristics; and determining the dominant discharge mechanism based on the proportional relationship between the number of first time periods, the number of second time periods, and the number of third time periods.

[0012] This application provides a method for detecting partial discharge insulated tools used in live-line working. This method simultaneously acquires high-frequency current detection signals and ultrasonic detection signals, analyzes the propagation stability of the two types of signals across multiple continuous time periods, and combines statistical results from multiple time periods to determine the dominant discharge mechanism. This achieves accurate identification of surface discharge and internal partial discharge coexisting in complex environments. This method effectively solves the problem of criterion failure caused by signal superposition, changes in propagation conditions, and time overlap in traditional methods, avoiding the one-sidedness and randomness of judgment based on a single signal feature or a single time point. It significantly improves the stability and reliability of partial discharge detection results for insulated tools used in live-line working, providing a basis for judgment on the continued use, maintenance, or replacement of insulated tools, and ensuring the safe conduct of live-line working.

[0013] A second aspect provides a partial discharge detection system for insulating tools used in live-line working, the system comprising:

[0014] The system includes a data acquisition module for simultaneously acquiring high-frequency current detection signals and ultrasonic detection signals from the insulating tool during the detection period; a time period division module for dividing the detection period into multiple consecutive time periods, each containing at least one valid discharge event; a stability analysis module for analyzing the high-frequency current propagation stability based on the high-frequency current detection signals and the ultrasonic propagation stability based on the ultrasonic detection signals during each time period; a dominance determination module for determining the dominant discharge propagation characteristics during each time period based on the corresponding high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results, wherein the dominant discharge propagation characteristics include internal partial discharge dominance, surface discharge dominance, or a combination of both; and a dominant discharge mechanism determination module for determining the dominant discharge mechanism within the detection period based on the dominant discharge propagation characteristic determination results for all time periods.

[0015] A third aspect provides a computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method of the first aspect.

[0016] The fourth aspect provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of the first aspect. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0018] Figure 1 A flowchart illustrating the steps of a partial discharge detection method for insulating tools used in live-line working provided in this application;

[0019] Figure 2 A flowchart illustrating the steps of the high-frequency current propagation stability analysis method provided in this application embodiment;

[0020] Figure 3 A flowchart illustrating the steps of the ultrasonic propagation stability analysis method provided in this application embodiment;

[0021] Figure 4 A schematic diagram illustrating the process for determining the dominant discharge mechanism in an embodiment of this application;

[0022] Figure 5 A structural block diagram of a partial discharge detection system for insulating tools used in live-line working provided in this application;

[0023] Figure 6This is a schematic diagram of the structure of a computer system provided in this application. Detailed Implementation

[0024] It should be noted that the user information involved in all embodiments of this application includes, but is not limited to, user device information, user personal information, object information corresponding to device usage data, etc., and the data includes, but is not limited to, data used for analysis, stored data, displayed data, device usage data, etc., all of which are information and data authorized by the user or fully authorized by all parties.

[0025] This method is applicable to the partial discharge detection of insulating tools used in live-line work scenarios during outdoor uninterrupted power supply operations. These insulating tools operate under the influence of operating voltage, and their surfaces are susceptible to outdoor environmental influences, resulting in moisture, condensation, or contaminant buildup. The implementation of this method typically relies on a field-deployed detection system, which must include at least a high-frequency current detection unit and an ultrasonic detection unit. These two types of units must meet the requirement of synchronously detecting the same insulating tool within the same testing cycle. During implementation, the usage status of the insulating tool is not altered, and the normal operation of the uninterrupted power supply is not interfered with. Detection is achieved by collecting signals generated by discharge activity on the insulating tool itself, ensuring that the detection results are consistent with the actual engineering conditions.

[0026] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] Please refer to Figure 1 , Figure 1 This application provides a method for detecting partial discharge insulated tools used in live-line working, the method comprising:

[0029] S1: During the detection cycle, the high-frequency current detection signal and ultrasonic detection signal on the insulating tool are collected simultaneously;

[0030] S2: Divide the detection period into multiple consecutive time periods; wherein each time period contains at least one valid discharge event;

[0031] S3: For each time period, analyze the high-frequency current propagation stability based on the high-frequency current detection signal within that time period, and analyze the ultrasonic propagation stability based on the ultrasonic detection signal within that time period.

[0032] S4: For each time period, based on the corresponding high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results, determine the dominant characteristics of discharge propagation within that time period; wherein, the dominant characteristics of discharge propagation include internal partial discharge dominance, surface discharge dominance, or a combination of both.

[0033] S5: Based on the results of the discharge propagation dominant characteristics determination for all time periods, determine the dominant discharge mechanism within the detection period.

[0034] In some embodiments, for step S1, during the detection cycle, high-frequency current detection signals and ultrasonic detection signals on the insulating tool are simultaneously acquired.

[0035] Specifically, the detection system first confirms that the insulating tool is in a powered state, consistent with the actual operating state of uninterrupted power supply (UDF) work. A high-frequency current detection unit is deployed at a location where the insulating tool forms an electrical connection with the detection system or where it can sense changes in high-frequency current. For example, it can be deployed in current-sensitive areas such as the grounding terminal of the insulating tool or the connection point of the operating lever. The response frequency of this detection unit can cover the frequency range of high-frequency currents generated by discharge activity, ensuring complete capture of high-frequency current pulse signals caused by surface discharge and internal partial discharge. During the acquisition process, the high-frequency current detection unit converts current changes into electrical signals according to a preset sampling frequency, forming a high-frequency current detection signal.

[0036] The ultrasonic detection unit is deployed at a preset detection position on the surface of the insulating tool. This position avoids areas prone to operational interference and facilitates the capture of ultrasonic signals generated by discharge. For example, it can be deployed in key areas such as the middle or end of the operating rod of the insulating tool. Its detection frequency must be adapted to the frequency range of the ultrasonic signals generated by the discharge to ensure effective response to ultrasonic vibrations caused by both types of discharge. During the acquisition process, the ultrasonic detection unit captures ultrasonic signals according to the preset sampling frequency, forming an ultrasonic detection signal.

[0037] Both high-frequency current detection signals and ultrasonic detection signals are stored in time-series format. The stored data structure includes at least time stamp information and signal amplitude information. To achieve time correspondence between the two types of signals, the same time reference is used to mark both types of signals during the acquisition process. The accuracy of the time reference must meet the requirements of subsequent signal correspondence analysis to ensure that the high-frequency current detection signal and ultrasonic detection signal generated by the same discharge event can be accurately aligned on the time axis, avoiding subsequent analysis errors due to time reference deviation.

[0038] The sampling frequency can be set in combination with the frequency characteristics of the discharge signal. Optionally, the sampling frequency of the high-frequency current detection signal is not less than, for example, 100 MSps, and the sampling frequency of the ultrasonic detection signal is not less than, for example, 10 MSps. The specific values ​​can be adjusted by those skilled in the art according to the actual detection environment and signal characteristics to ensure the integrity and accuracy of signal acquisition.

[0039] In some embodiments, for step S2, the detection period is divided into multiple consecutive time periods. The purpose is to achieve time segment consistency analysis, and to eliminate the randomness of signal characteristics at a single time point through independent analysis of multiple consecutive time periods, thereby providing support for the accurate determination of the dominant discharge mechanism.

[0040] Specifically, after completing signal acquisition in step S1, the detection system divides the entire detection cycle into time periods. The division process follows these principles: first, each divided time period must be a continuous and non-overlapping interval, fully covering the entire detection cycle without any time omissions; second, the duration of each time period can be set according to detection requirements, with the core requirement being that each time period contains at least one valid discharge event. This ensures that the signal characteristics within that time period reflect the discharge propagation characteristics of the corresponding period, avoiding insufficient sample size of discharge events due to excessively short time periods, thus preventing effective propagation stability analysis.

[0041] The time periods can be divided using either equal-length or dynamic duration division. For example, if the frequency of discharge events is relatively stable, equal-length division can be used, with the duration of each time period configurable from 10 to 60 seconds. If the frequency of discharge events is unstable, the time period length can be dynamically adjusted based on the distribution of discharge pulses in the signal acquired in step S1, ensuring that each time period contains at least one valid discharge event. After division, the detection system logically establishes an independent data processing unit for each time period to store the corresponding high-frequency current detection signal segment and ultrasonic detection signal segment, achieving independence between the analysis processes of each time period and avoiding interference between signal characteristics from different time periods.

[0042] In some embodiments, for step S3, the propagation stability of high-frequency current and ultrasonic waves is analyzed for each time period. By quantitatively analyzing the propagation characteristics of high-frequency current detection signals and ultrasonic detection signals, an objective and accurate propagation stability judgment standard is established, solving the problem of insufficient identification accuracy caused by traditional methods relying on single signal features or subjective judgment, and effectively dealing with signal aliasing interference when surface discharge and internal partial discharge coexist.

[0043] Please see Figure 2 , Figure 2This is a flowchart illustrating the steps of the high-frequency current propagation stability analysis method provided in this application embodiment. The high-frequency current propagation stability analysis extracts effective discharge pulses from the high-frequency current detection signal within a time period, calculates the amplitude variation coefficient (characterizing amplitude stability) and the time clustering coefficient (characterizing temporal concentration), and determines the high-frequency current propagation stability by combining these with a set threshold.

[0044] Specifically, in S201, effective discharge pulses are extracted. The purpose of extracting effective discharge pulses is to separate the pulse signals related to discharge activity from the high-frequency current detection signal segment, eliminate the influence of background noise and other interference signals, and provide reliable samples for subsequent feature parameter calculations.

[0045] Specifically, the detection system first performs background noise analysis on the high-frequency current detection signal segment within the current time period, and calculates the average amplitude of the background noise. and standard deviation The pulse extraction threshold was set based on the background noise analysis results. Optional, ,in The threshold coefficient can be configured from 3 to 5, and those skilled in the art can adjust it according to the noise level of the actual detection environment. The specific value can be appropriately increased when the noise level is high. The value can be appropriately reduced when the noise level is low. value.

[0046] When the amplitude of a certain signal in a high-frequency current detection signal segment exceeds the threshold At this point, the signal is initially identified as a potential discharge pulse. To avoid false positives, morphological verification of the potential discharge pulse can be performed. Discharge pulses typically exhibit a rapid rise and slow fall pattern. The detection system extracts morphological parameters such as the rise time, fall time, and pulse width of the potential discharge pulse and matches these parameters with a preset discharge pulse morphological feature library. When the matching degree exceeds a set threshold, such as 80%, the potential discharge pulse is confirmed as a valid discharge pulse; if the matching degree does not reach the set threshold, it is judged as an interference signal and discarded. This combination of threshold judgment and morphological verification ensures the accuracy of valid discharge pulse extraction and provides a reliable basis for subsequent feature parameter calculations.

[0047] In S202, the amplitude variation coefficient is calculated. The amplitude variation coefficient is used to quantify the amplitude stability of the effective discharge pulse. Its core principle is to reflect the stability of the high-frequency current propagation path by calculating the relative dispersion of the amplitude. That is, the smaller the amplitude variation coefficient, the higher the amplitude stability, and the more stable the corresponding high-frequency current propagation path. Conversely, the larger the amplitude variation coefficient, the more unstable the propagation path.

[0048] Specifically, the detection system first counts the amplitude data of all valid discharge pulses within the current time period, assuming the number of valid discharge pulses is... The amplitudes of each effective discharge pulse are as follows: Calculate the arithmetic mean of the amplitude data set. The calculation formula is as follows:

[0049]

[0050] in, The arithmetic mean of the amplitudes of the effective discharge pulses. to The amplitude of each effective discharge pulse, The number of effective discharge pulses.

[0051] Then the standard deviation of the amplitude data was calculated. The standard deviation reflects the degree of dispersion of amplitude data relative to the mean, and the calculation formula is as follows:

[0052]

[0053] in, This represents the standard deviation of the effective discharge pulse amplitude; the meanings of the other parameters are consistent with the formula for calculating the arithmetic mean of the amplitude as described above.

[0054] Based on the arithmetic mean of amplitude and standard deviation Calculate the amplitude variation coefficient The coefficient of variation is the ratio of the standard deviation to the arithmetic mean, expressed as a percentage, and is calculated using the following formula:

[0055]

[0056] in, The amplitude variation coefficient of the effective discharge pulse is smaller. The smaller the value, the more concentrated the amplitude distribution of the effective discharge pulse and the higher the amplitude stability.

[0057] The calculation of this parameter directly addresses the difference in amplitude characteristics between surface discharge and internal partial discharge. Surface discharge is affected by environmental factors, and its propagation path is constantly changing, resulting in large fluctuations in the amplitude of the high-frequency current pulses it generates, and a high amplitude variation coefficient. In contrast, the propagation path of internal partial discharge is relatively fixed, and the amplitude of the high-frequency current pulses it generates is highly stable, with a low amplitude variation coefficient. By calculating this parameter, the amplitude characteristic differences between the two types of discharge can be effectively distinguished, solving the problem of identification difficulties caused by the surface discharge amplitude masking the internal discharge amplitude in traditional methods.

[0058] In S203, the temporal clustering coefficient is calculated. The temporal clustering coefficient is used to quantify the concentration of the occurrence time of effective discharge pulses. Its core principle is to reflect the stability of the high-frequency current propagation path by analyzing the degree of concentration of the distribution of effective discharge pulses on the time axis. That is, the closer the temporal clustering coefficient is to 1, the more concentrated the occurrence time is, and the more stable the corresponding high-frequency current propagation path is. Conversely, the closer the temporal clustering coefficient is to 1, the more concentrated the occurrence time is, and the more stable the corresponding high-frequency current propagation path is.

[0059] Specifically, the detection system first records the occurrence time of all valid discharge pulses within the current time period, assuming the occurrence time of each valid discharge pulse is as follows: Cluster analysis was conducted based on this time data. The K-means clustering algorithm was used to cluster the occurrence times, with a cluster size of 1, assuming that the occurrence times of all valid discharge pulses are concentrated in one time cluster. The cluster centers of this time cluster were calculated using this algorithm. .

[0060] Calculate the occurrence time of each effective discharge pulse to the cluster center. distance ,in ( Then, the arithmetic mean of these distances is calculated, which is the average distance. The calculation formula is as follows:

[0061]

[0062] in, The average distance from the occurrence time of the effective discharge pulse to the cluster center. For the first The distance from the occurrence time of each effective discharge pulse to the cluster center The number of effective discharge pulses.

[0063] Let the duration of the current time period be . Based on average distance Duration of time period Calculate the time clustering coefficient The calculation formula is as follows:

[0064]

[0065] in, This is the time-based clustering coefficient, which ranges from 0 to 1. This represents the duration of the current time period. When effective discharge pulses occur in a highly concentrated timeframe, Smaller Approaching 1; when the effective discharge pulses are time-dispersed, Larger Approaching 0.

[0066] The calculation of this parameter addresses the temporal differences between the two types of discharges. Specifically, the discharge point of internal partial discharge is fixed, and the occurrence time of the high-frequency current pulses it generates has a certain concentration, resulting in a high temporal clustering coefficient. On the other hand, the discharge path of surface discharge is constantly adjusted due to micro-changes in the environment, and the pulse occurrence time is dispersed, resulting in a lower temporal clustering coefficient. This parameter can effectively distinguish the temporal characteristics of the two types of discharges, solving the problem of criterion failure caused by the overlap of the two types of discharge times in traditional methods.

[0067] In S204, the stability of high-frequency current propagation is determined. The determination of high-frequency current propagation stability is based on the amplitude variation coefficient calculated above. and time clustering coefficient Combined with the preset first amplitude threshold and the first-time clustering threshold accomplish.

[0068] Specifically, the preset first amplitude threshold and the first-time clustering threshold The threshold value needs to be determined based on factors such as the type of insulating tool, operating voltage level, and testing environment. For example, the first amplitude threshold value... Configurable to 15%, the first-time clustering threshold. It can be configured to 0.7, and those skilled in the art can adjust it according to the actual application scenario.

[0069] When satisfied and When the high-frequency current propagation stability within the current time period is determined to be stable; when the condition is met... and If the above two conditions are not met, i.e., the determination results of amplitude variation coefficient and time clustering coefficient are contradictory, the high-frequency current propagation stability will not be determined separately for the time being, but can be comprehensively evaluated in combination with the analysis results of subsequent ultrasonic propagation stability.

[0070] This judgment process establishes an objective standard for judging the stability of high-frequency current propagation by comparing quantitative parameters with thresholds, avoiding errors caused by subjective judgment in traditional methods. The principle is that the propagation path of internal partial discharge is fixed, and it will inevitably exhibit the characteristics of stable amplitude and concentrated timing at the same time. On the other hand, the propagation path of surface discharge is unstable, and it will inevitably exhibit the characteristics of amplitude fluctuation and dispersed timing at the same time. By jointly judging with two parameters, the accuracy of propagation stability identification is further improved.

[0071] In one embodiment, see Figure 3 , Figure 3The flowchart illustrates the steps of the ultrasonic propagation stability analysis method provided in this application embodiment. Ultrasonic propagation stability analysis extracts the effective ultrasonic response signal from the ultrasonic detection signal within a time period, calculates the arrival time dispersion coefficient (representing the concentration of arrival time distribution), the amplitude variation coefficient (representing amplitude stability), and the attenuation variation coefficient (representing the consistency of attenuation patterns), and determines ultrasonic propagation stability by combining these with a set threshold. The specific implementation process includes the following steps:

[0072] In S301, the effective ultrasonic response signal is extracted. The purpose of extracting the effective ultrasonic response signal is to separate the ultrasonic signal related to the discharge activity from the ultrasonic detection signal segment, eliminate interference from environmental noise such as wind noise and operation noise, and provide a reliable sample for subsequent characteristic parameter calculation.

[0073] Specifically, the detection system first performs background noise analysis on the ultrasonic detection signal segments within the current time period, and calculates the average amplitude of the background noise. and standard deviation The threshold for ultrasonic signal extraction was set based on the background noise analysis results. Optional, ,in The threshold coefficient can be configured from 4 to 6, and can be adjusted by those skilled in the art according to the noise level of the ultrasonic testing environment. The specific value should be increased appropriately when the noise level is high. Value, appropriately reduced when noise level is low. value.

[0074] When the amplitude of a signal in an ultrasonic detection signal segment exceeds a threshold At this point, the signal is initially identified as a potential ultrasonic response signal. To avoid misjudgment, morphological verification of the potential ultrasonic response signal can be performed. The ultrasonic signal generated by the discharge typically possesses specific frequency and duration characteristics. The detection system converts the potential ultrasonic response signal to the frequency domain using a Fast Fourier Transform (FFT) to analyze whether its frequency distribution matches the typical frequency range of the discharge ultrasonic signal. Simultaneously, the duration of the potential ultrasonic response signal is extracted to verify whether it falls within the typical duration range of the discharge ultrasonic signal. When both the frequency matching degree and the duration matching degree exceed a set threshold, such as 85%, the potential ultrasonic response signal is confirmed as a valid ultrasonic response signal; if the matching degree does not reach the set threshold, it is determined to be an interference signal and is discarded.

[0075] In S302, the arrival time dispersion coefficient is calculated. The arrival time dispersion coefficient is used to quantify the concentration of the arrival time distribution of the effective ultrasonic response signal. Its core principle is to reflect the stability of the ultrasonic propagation path by calculating the relative dispersion of the arrival time. That is, the smaller the arrival time dispersion coefficient, the more concentrated the arrival time distribution and the more stable the ultrasonic propagation path, and vice versa.

[0076] Specifically, the detection system first records the arrival time of all valid ultrasonic response signals within the current time period. The arrival time is defined as the time point corresponding to the peak amplitude of the valid ultrasonic response signal. This time point is recorded based on the unified time reference set in step S1 to ensure consistency with the time marker of the high-frequency current detection signal. Let the number of valid ultrasonic response signals be... The arrival times of each effective ultrasonic response signal are as follows: Calculate the arithmetic mean of the arrival time data for this set. The calculation formula is as follows:

[0077]

[0078] in, The arithmetic mean of the arrival times of the effective ultrasonic response signal. to The arrival time of each effective ultrasonic response signal, The number of effective ultrasonic response signals.

[0079] Then the standard deviation of the arrival time data was calculated. The standard deviation reflects the dispersion of arrival time data relative to the mean, and is calculated using the following formula:

[0080]

[0081] in, The standard deviation of the arrival time of the effective ultrasonic response signal is given; the meanings of the other parameters are consistent with the formula for calculating the arithmetic mean of the arrival time.

[0082] Based on the arithmetic mean of arrival times and standard deviation Calculate the coefficient of variation of arrival time. The coefficient of variation for arrival time is the ratio of the standard deviation to the arithmetic mean, expressed as a percentage, and is calculated using the following formula:

[0083]

[0084] in, The arrival time dispersion coefficient of the effective ultrasonic response signal is smaller, indicating that the arrival time distribution of the effective ultrasonic response signal is more concentrated.

[0085] The calculation of this parameter addresses the difference in ultrasonic propagation paths between the two types of discharges. Specifically, the ultrasonic signals generated by internal partial discharge mainly propagate along the interior of the insulating material, with a fixed propagation path and a relatively concentrated arrival time at the detection unit, resulting in a small arrival time dispersion coefficient. In contrast, the ultrasonic signals generated by surface discharge mainly propagate along the air or a thin surface medium, with the propagation path constantly changing due to environmental influences, resulting in a dispersed arrival time and a larger arrival time dispersion coefficient. This parameter can effectively distinguish the ultrasonic propagation time characteristics of the two types of discharges and solve the signal distortion problem caused by changes in ultrasonic propagation boundary conditions due to surface water films and contamination layers.

[0086] In S303, the amplitude variation coefficient is calculated. The amplitude variation coefficient of the ultrasonic response signal is used to quantify the amplitude stability of the effective ultrasonic response signal. Its calculation logic is consistent with that of the amplitude variation coefficient of the effective discharge pulse in the high-frequency current detection signal. The core principle is to reflect the stable state of the ultrasonic propagation path by calculating the relative dispersion of the amplitude.

[0087] Specifically, the detection system collects the amplitude data of all valid ultrasonic response signals within the current time period, assuming that the amplitude of each valid ultrasonic response signal is as follows: Calculate the arithmetic mean of the amplitude data set. The calculation formula is as follows:

[0088]

[0089] in, The effective ultrasonic response signal amplitude is the arithmetic mean. to The amplitude of each effective ultrasonic response signal, The number of effective ultrasonic response signals.

[0090] Then the standard deviation of the amplitude data was calculated. The calculation formula is as follows:

[0091]

[0092] in, This represents the standard deviation of the effective ultrasonic response signal amplitude; the meanings of the other parameters are consistent with the formula for calculating the arithmetic mean of the ultrasonic amplitude described above.

[0093] Based on the arithmetic mean of amplitude and standard deviation Calculate the coefficient of variation of ultrasonic amplitude. The calculation formula is as follows:

[0094]

[0095] in, The amplitude variation coefficient of the effective ultrasonic response signal is denoted by . The smaller the value, the higher the amplitude stability of the effective ultrasonic response signal.

[0096] The calculation of this parameter also takes into account the differences in ultrasonic propagation between the two types of discharges. Specifically, the ultrasonic propagation path of internal partial discharge is stable, with small amplitude fluctuations and a low amplitude variation coefficient; while the ultrasonic propagation path of surface discharge is affected by the environment, with large amplitude fluctuations and a high amplitude variation coefficient. This parameter can further verify the stable state of the ultrasonic propagation path and complements the arrival time dispersion coefficient.

[0097] In S404, the coefficient of variation of the attenuation coefficient is calculated. The coefficient of variation of the attenuation coefficient is used to quantify the consistency of the attenuation law of the effective ultrasonic response signal. Its core principle is to reflect the stability of the ultrasonic propagation path by analyzing the differences in the attenuation characteristics of different effective ultrasonic response signals. That is, the smaller the coefficient of variation of the attenuation coefficient, the more consistent the attenuation law and the more stable the ultrasonic propagation path, and vice versa.

[0098] Specifically, the detection system first extracts a segment of the signal amplitude changing over time for each valid ultrasonic response signal within the current time period, starting from its arrival time, thus forming the amplitude decay curve of that valid ultrasonic response signal. An exponential decay model is then used to fit the amplitude decay curve. The expression for the exponential decay model is as follows:

[0099]

[0100] in, for The amplitude of the ultrasonic signal at time t. The initial amplitude of the effective ultrasonic response signal (i.e., the amplitude at the arrival time). The attenuation coefficient is... The time is calculated from the arrival time. The fitting error needs to be controlled within a set range (for example, the absolute value of the fitting error should not exceed 5% of the initial amplitude) to ensure the reliability of the fitting results.

[0101] Through the above fitting process, the corresponding attenuation coefficient is obtained for each effective ultrasonic response signal. ( (The number of effective ultrasonic response signals). Calculate the arithmetic mean of the attenuation coefficients for this group. The calculation formula is as follows:

[0102]

[0103] in, The arithmetic mean of the attenuation coefficients. to The attenuation coefficient of each effective ultrasonic response signal, The number of effective ultrasonic response signals.

[0104] Calculate the standard deviation of the attenuation coefficient The calculation formula is as follows:

[0105]

[0106] in, This represents the standard deviation of the attenuation coefficient; the meanings of the other parameters are consistent with the formula for calculating the arithmetic mean of the attenuation coefficient.

[0107] Based on the arithmetic mean of the attenuation coefficient and standard deviation Calculate the attenuation coefficient and the coefficient of variation. The calculation formula is as follows:

[0108]

[0109] in, The coefficient of variation of the attenuation coefficient is denoted by . The smaller the value, the more consistent the attenuation pattern of each effective ultrasonic response signal.

[0110] The calculation of this parameter is crucial for the analysis of ultrasonic propagation stability. The principle is that the ultrasonic signal of internal partial discharge propagates along the solid insulating material. The propagation medium is uniform and stable, and the attenuation law has a high degree of consistency with a small coefficient of variation of the attenuation coefficient. On the other hand, the ultrasonic signal of surface discharge propagates along the air or a thin surface medium. The propagation medium is affected by factors such as environmental humidity and pollutant distribution, resulting in a large difference in the attenuation law and a high coefficient of variation of the attenuation coefficient. This parameter can effectively distinguish the ultrasonic attenuation characteristics of the two types of discharge, solving the problem that traditional methods cannot cope with the changes in ultrasonic signal attenuation characteristics.

[0111] In S305, the determination of ultrasonic propagation stability is based on the arrival time dispersion coefficient calculated above. Amplitude variation coefficient and attenuation coefficient and coefficient of variation Combined with preset arrival time discrete thresholds Ultrasonic amplitude threshold and attenuation coefficient threshold accomplish.

[0112] Specifically, the three preset thresholds need to be determined based on factors such as the material and structure of the insulating tool and the testing environment. For example, the time-discrete threshold... Configurable to 10%, ultrasonic amplitude threshold Configurable to 12%, attenuation coefficient threshold It can be configured to 10%, and those skilled in the art can adjust it according to the actual application scenario.

[0113] When satisfied , and When the current time period is considered stable, the stability of ultrasound propagation is determined to be stable; when the following conditions are met... , and If the above three conditions are not met simultaneously, i.e. some parameters meet the stability conditions while others meet the instability conditions, the stability of ultrasound propagation is not determined separately, but can be comprehensively evaluated in combination with the analysis results of the aforementioned high-frequency current propagation stability.

[0114] This determination process establishes a comprehensive and objective standard for judging the stability of ultrasonic propagation through joint verification of three parameters, avoiding the one-sidedness caused by single-parameter judgment. The principle is that the ultrasonic propagation path of internal partial discharge is fixed, and it will necessarily exhibit the characteristics of concentrated arrival time, stable amplitude and consistent attenuation law. On the other hand, the ultrasonic propagation path of surface discharge is unstable, and it will necessarily exhibit the characteristics of discrete arrival time, amplitude fluctuation and inconsistent attenuation law. Through joint judgment of multiple parameters, the accuracy of ultrasonic propagation stability identification is further improved.

[0115] In some embodiments, for step S4, the dominant characteristics of discharge propagation within each time period are determined. Based on the high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results obtained in step S3, the dominant characteristics of discharge propagation within each time period are determined according to a preset logic, solving the technical problem that traditional methods cannot identify the dominant discharge mechanism when surface discharge and internal partial discharge coexist.

[0116] Specifically, the dominant characteristics of discharge propagation include three categories: internal partial discharge dominance, surface discharge dominance, and composite characteristics. The specific implementation process is as follows:

[0117] When the high-frequency current propagation stability analysis result in step S3 is stable, and the ultrasonic propagation stability analysis result is also stable, the detection system determines that the dominant characteristic of discharge propagation in the current time period is internal partial discharge.

[0118] The core principle of this judgment logic lies in the fact that the discharge point of internal partial discharge is relatively fixed, and the high-frequency current it generates forms a stable propagation path within the insulating material. Therefore, the high-frequency current detection signal exhibits stable amplitude and concentrated timing, indicating stable high-frequency current propagation stability. Simultaneously, the ultrasonic signal generated by internal partial discharge mainly propagates along the solid insulating structure, with stable propagation boundary conditions. Therefore, the ultrasonic detection signal exhibits concentrated arrival time, stable amplitude, and consistent attenuation patterns, indicating stable ultrasonic propagation stability. This combination of stable propagation stability for both types of signals uniquely indicates that internal partial discharge dominates during this time period. This judgment effectively eliminates interference from surface discharge and solves the identification difficulty caused by surface discharge signals masking internal discharge signals.

[0119] When the high-frequency current propagation stability analysis result in step S3 is unstable, and the ultrasonic propagation stability analysis result is also unstable, the detection system determines that the dominant characteristic of discharge propagation in the current time period is surface discharge.

[0120] The core principle of this judgment logic lies in the fact that the current path of surface discharge is affected by environmental factors such as water film thickness and pollution distribution, constantly adjusting with slight environmental changes, resulting in an unstable propagation path. Therefore, the high-frequency current detection signal exhibits characteristics of large amplitude fluctuations and dispersed timing, indicating unstable high-frequency current propagation stability. Simultaneously, the ultrasonic signal generated by surface discharge mainly relies on air or a thin surface medium for propagation. The propagation boundary conditions are highly unstable due to environmental influences, thus the ultrasonic detection signal exhibits characteristics of discrete arrival times, inconsistent amplitude fluctuations, and inconsistent attenuation patterns, indicating unstable ultrasonic propagation stability. This combination of unstable propagation stability for both types of signals uniquely indicates that surface discharge dominates during that time period. This judgment effectively distinguishes the propagation differences between surface discharge and internal discharge, solving the problem that traditional methods cannot accurately identify the dominance of surface discharge.

[0121] In all combinations other than the two mentioned above, the detection system determines that the dominant characteristic of discharge propagation in the current time period is a composite characteristic. Specifically, this includes the following scenarios: high-frequency current propagation stability is stable while ultrasonic propagation stability is unstable; high-frequency current propagation stability is unstable while ultrasonic propagation stability is stable; and both high-frequency current propagation stability and ultrasonic propagation stability are not determined separately, meaning some parameters meet the stability condition while others meet the instability condition.

[0122] The core principle of this judgment logic is that the occurrence of composite characteristics is usually due to the simultaneous existence and mutual influence of surface discharge and internal partial discharge within a certain time period, or significant environmental interference signals, which prevent the propagation stability characteristics of the two types of signals from forming a unified direction. For example, when internal partial discharge and surface discharge coexist, the high-frequency current detection signal may exhibit a certain amplitude stability due to the influence of internal discharge, while exhibiting temporal dispersion due to the influence of surface discharge. This makes it impossible to determine the high-frequency current propagation stability as stable or unstable on its own. In this case, if the analysis results of ultrasonic propagation stability also fail to form a unified direction, it is judged as a composite characteristic. This judgment result comprehensively covers the complex scenario of the coexistence and mutual influence of the two types of discharge, avoiding a one-sided judgment of either / or.

[0123] During the determination process, the detection system generates a record of the dominant characteristics of discharge propagation for each time period. This record includes information such as time period identifier, high-frequency current propagation stability results, ultrasonic propagation stability results, and conclusions on the dominant characteristics of discharge propagation. It is stored in the system database to provide standardized and statistical intermediate data for comprehensive analysis in subsequent steps.

[0124] In some embodiments, for step S5, the dominant discharge mechanism within the detection period is determined based on the determination results of the dominant discharge propagation characteristics corresponding to all time periods. This is achieved by statistically analyzing the determination results of the dominant discharge propagation characteristics for all time periods and determining the dominant discharge mechanism within the detection period based on the proportional relationship of the number of time periods corresponding to various characteristics. This solves the problem of random errors caused by the traditional method's reliance on single-time-point feature judgment and ensures the reliability of the dominant discharge mechanism identification.

[0125] Specifically, please refer to Figure 4 , Figure 4 This is a schematic diagram illustrating the process for determining the dominant discharge mechanism in an embodiment of this application. The implementation process includes the following steps:

[0126] In S401, the number of time periods is counted. The detection system first extracts the discharge propagation dominant characteristic determination records for all time periods from the database, and excludes the time periods determined to be "without effective discharge events". That is, if the proportion of such time periods without effective discharge events in all divided time periods exceeds a set threshold, such as 50%, it is determined that the sample size of discharge events in the detection period is insufficient, and effective identification of the dominant discharge mechanism cannot be carried out. The detection and analysis process of steps S1 to S4 needs to be repeated.

[0127] The valid judgment records, i.e., the time periods judged to be dominated by internal partial discharge, surface discharge, or a combination of both, are classified and statistically analyzed: the number of time periods judged to be dominated by internal partial discharge is counted and recorded as the first time period number. The number of time periods determined to be dominated by surface discharge is recorded as the number of the second time period. The number of time periods that are statistically determined to have composite characteristics is recorded as the number of the third time period. ; Calculate the total number of valid time periods .

[0128] To ensure the traceability of statistical results, the detection system generates statistical reports that clearly record the specific identifiers, quantities, and percentages of each type of time period within the total number of valid time periods, including the percentage dominated by internal partial discharge. The proportion dominated by surface discharge The proportion of composite properties .

[0129] In S402, the dominant discharge mechanism is determined based on the proportional relationship. The detection system uses the first time period quantity obtained from the above statistics. Quantity in the second time period and the number of the third time period The proportional relationship between them, combined with the preset proportional threshold, determines the dominant discharge mechanism within the detection cycle.

[0130] The preset ratio thresholds include the first ratio threshold. Second proportional threshold ,in For example, the first proportional threshold Configurable to 50%, second proportional threshold It can be configured to 30%, and those skilled in the art can adjust the threshold value according to the detection accuracy requirements and actual application scenarios.

[0131] The specific judgment logic is as follows: (1) When When the detection period is determined, the dominant discharge mechanism is internal partial discharge. The core basis for this determination is that the propagation stability characteristics of internal partial discharge are persistent. If it dominates for more than half of the effective time period, it indicates that internal partial discharge is the main discharge mechanism in that detection period, and the signal characteristics it generates are not completely masked by the surface discharge signal, and can be highlighted through statistical analysis over multiple time periods.

[0132] (2) When When the dominant discharge mechanism within the detection period is determined to be surface discharge, the core basis for this determination is that although the propagation instability characteristics of surface discharge are affected by the environment, if it dominates for more than half of the effective time period, it indicates that surface discharge is the main discharge mechanism within that detection period, and the signal characteristics it generates are significantly more significant than those of internal discharge signals.

[0133] (3) When and And satisfy , At that time, the dominant discharge mechanism during the detection period was determined to be a composite discharge state dominated by the environment. The core basis for this determination is that the proportions of internal partial discharge and surface discharge are similar, and the proportion of composite characteristics reaches a certain level, indicating that the two types of discharge alternate during the detection period, neither of which has formed a sustained dominant position, and the discharge characteristics are significantly affected by environmental factors.

[0134] (4) When and , If the signal characteristics of the composite characteristic time period are analyzed further, and the high-frequency current propagation stability is stable for more than 60% of the time period, then the dominant discharge mechanism is determined to be internal partial discharge; otherwise, the detection process needs to be repeated.

[0135] (5) When and , If the signal characteristics of the composite characteristic time period are further analyzed, and the proportion of unstable high-frequency current propagation in the composite characteristic time period exceeds 60%, then the dominant discharge mechanism is determined to be surface discharge; otherwise, the detection process needs to be repeated.

[0136] This judgment process effectively eliminates the randomness of judgment results in a single time period through statistical analysis over multiple time periods and dual verification using proportional thresholds, ensuring the accuracy and reliability of the identification of the dominant discharge mechanism. The principle is that the propagation characteristics of the dominant discharge mechanism must be continuously presented over most time periods, while the discharge characteristics that appear by chance cannot be verified by the proportional threshold. This achieves the transformation from "instantaneous feature judgment" to "continuous feature analysis," solving the limitations of traditional methods in discharge identification under complex environments.

[0137] This method simultaneously acquires high-frequency current detection signals and ultrasonic detection signals, analyzes the propagation stability of the two types of signals across multiple continuous time periods, and determines the dominant discharge mechanism by combining statistical results from multiple time periods. This enables accurate identification of surface discharge and internal partial discharge coexisting in complex environments. This method effectively solves the problem of criterion failure caused by signal superposition, changes in propagation conditions, and time overlap in traditional methods, avoiding the one-sidedness and randomness of judgment based on a single signal feature or a single time point. It significantly improves the stability and reliability of partial discharge detection results for insulated tools used in live-line work, providing a basis for judgment on the continued use, maintenance, or replacement of insulated tools, and ensuring the safe conduct of live-line work.

[0138] It should be noted that although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart can be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0139] Further reference Figure 5 The diagram illustrates an exemplary structural block diagram of a partial discharge detection system 500 for live-line working insulating tools according to an embodiment of this application. The system 500 includes:

[0140] The acquisition module 501 is used to simultaneously acquire the high-frequency current detection signal and ultrasonic detection signal on the insulating tool during the detection cycle;

[0141] The time period division module 502 is used to divide the detection period into multiple consecutive time periods; wherein each time period contains at least one valid discharge event;

[0142] The stability analysis module 503 is used to analyze the high-frequency current propagation stability based on the high-frequency current detection signal within each time period, and to analyze the ultrasonic propagation stability based on the ultrasonic detection signal within the time period.

[0143] The dominance determination module 504 is used to determine the dominant characteristics of discharge propagation within each time period based on the corresponding high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results; wherein, the dominant characteristics of discharge propagation include internal partial discharge dominance, surface discharge dominance, or a combination of both.

[0144] The dominant discharge mechanism determination module 505 is used to determine the dominant discharge mechanism within the detection period based on the discharge propagation dominant characteristic determination results corresponding to all time periods.

[0145] It should be understood that the units or modules described in System 500 are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations and features described above for the method also apply to system 500 and the units or modules contained therein, and will not be repeated here. System 500 can be pre-implemented in the browser or other security applications of an electronic device, or it can be loaded into the browser or other security applications of an electronic device by means of downloading. The corresponding units or modules in system 500 can cooperate with the units in the electronic device to implement the solution of the embodiments of this application.

[0146] The following is for reference. Figure 6It shows a schematic diagram of the structure of a computer device 600 suitable for implementing the embodiments of this application.

[0147] like Figure 6 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0148] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0149] Specifically, according to embodiments of this application, the above references Figure 1-4 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing instructions for performing... Figure 1-4 The program code for the method. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0151] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor; for example, a processor can be described as including XX unit, YY unit, and ZZ unit. The names of these units or modules do not necessarily limit the unit or module itself; for example, XX unit can also be described as "a unit for XX".

[0152] In another aspect, this application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into the device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the partial discharge detection method for live-line working insulating tools described in this application.

[0153] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for detecting partial discharge insulated tools used in live-line working, characterized in that, The method includes: During the detection period, high-frequency current detection signals and ultrasonic detection signals on the insulating tool are collected simultaneously. The detection period is divided into multiple consecutive time periods; each time period contains at least one valid discharge event. For each time period, the high-frequency current propagation stability is analyzed based on the high-frequency current detection signal within that time period, and the ultrasonic propagation stability is analyzed based on the ultrasonic detection signal within that time period. For each time period, based on the corresponding high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results, the dominant characteristics of discharge propagation within that time period are determined; wherein, the dominant characteristics of discharge propagation include internal partial discharge dominance, surface discharge dominance, or a combination of both. Based on the results of the dominant discharge propagation characteristics determination for all time periods, the dominant discharge mechanism within the detection period is determined.

2. The method according to claim 1, characterized in that, The analysis of the high-frequency current propagation stability includes: analyzing the amplitude stability and the concentration of occurrence time of the discharge pulses in the high-frequency current detection signal; The analysis of the stability of ultrasonic propagation includes: analyzing the arrival time distribution, amplitude variation, and attenuation consistency of the ultrasonic response signal in the ultrasonic detection signal.

3. The method according to claim 2, characterized in that, The dominant characteristic of the discharge propagation is determined based on the following logic: If both the high-frequency current propagation stability analysis results and the ultrasonic propagation stability analysis results indicate stability, then it is determined that internal partial discharge dominates. If both the high-frequency current propagation stability analysis results and the ultrasonic propagation stability analysis results indicate instability, then it is determined that surface discharge dominates. Otherwise, it is determined to be a composite characteristic.

4. The method according to claim 2, characterized in that, The specific analysis of the high-frequency current propagation stability is as follows: Extract effective discharge pulses from the high-frequency current detection signal within the time period; Calculate the amplitude variation coefficient of the effective discharge pulse to characterize the amplitude stability; Calculate the occurrence time clustering coefficient of the effective discharge pulses to characterize the temporal concentration; Based on the comparison between the amplitude variation coefficient and the first amplitude threshold, and the time clustering coefficient and the first time clustering threshold, the stability of the high-frequency current propagation is determined to be stable or unstable.

5. The method according to claim 2, characterized in that, The specific analysis of the stability of ultrasonic propagation is as follows: Extract the effective ultrasonic response signal from the ultrasonic detection signal within the time period; Calculate the time-of-arrival discrepancy coefficient of the effective ultrasonic response signal; Calculate the amplitude variation coefficient of the effective ultrasonic response signal; The attenuation coefficient is calculated based on the attenuation curve of the effective ultrasonic response signal, and the coefficient of variation of the attenuation coefficient is also calculated. The stability of ultrasonic propagation is determined to be stable or unstable based on the comparison between the arrival time dispersion coefficient, the amplitude variation coefficient, and the attenuation coefficient variation coefficient and the corresponding set thresholds.

6. The method according to claim 1, characterized in that, Determining the dominant discharge mechanism within the detection period includes: statistically analyzing the number of first time periods determined to be dominated by internal partial discharge, the number of second time periods determined to be dominated by surface discharge, and the number of third time periods determined to be characterized by composite features. The dominant discharge mechanism is determined based on the proportional relationship between the number of items in the first time period, the number of items in the second time period, and the number of items in the third time period.

7. A partial discharge detection system for insulating tools used in live-line working, characterized in that, The system includes: The acquisition module is used to simultaneously acquire high-frequency current detection signals and ultrasonic detection signals on the insulating tool during the detection cycle; The time period segmentation module is used to divide the detection period into multiple consecutive time periods; wherein each time period contains at least one valid discharge event; The stability analysis module is used to analyze the high-frequency current propagation stability based on the high-frequency current detection signal within each time period, and to analyze the ultrasonic propagation stability based on the ultrasonic detection signal within the time period. The dominance determination module is used to determine the dominant characteristics of discharge propagation within each time period based on the corresponding high-frequency current propagation stability analysis results and ultrasonic propagation stability analysis results; wherein, the dominant characteristics of discharge propagation include internal partial discharge dominance, surface discharge dominance, or a combination of both. The dominant discharge mechanism determination module is used to determine the dominant discharge mechanism within the detection period based on the discharge propagation dominant characteristic determination results corresponding to all time periods.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

Images