Discharge detection method for surface defects of insulator and related device

Abstract

The embodiment of the application discloses a discharge detection method for surface defects of an insulator and related equipment, through obtaining a plurality of ultrahigh frequency electromagnetic wave signals and low-frequency electric field time-domain waveform signals of the surface defects of the insulator within a preset detection period, and performing eigenvalue extraction to obtain an amplitude mean value, an amplitude variance and a phase variance corresponding to the ultrahigh frequency electromagnetic wave signals, and a voltage zero-crossing moment electric field amplitude, a voltage and electric field waveform cross-correlation coefficient and an electric field waveform kurtosis factor corresponding to the low-frequency electric field time-domain waveform signals, multi-dimensional information reflecting the discharge state of the surface defects of the insulator is comprehensively obtained; a discharge degree evaluation factor is calculated in combination with a preset weight, the influence of various eigenvalues on the discharge degree can be comprehensively considered, and occasional and weak discharge signals can be effectively captured; whether the insulator discharges or not is judged according to the discharge degree evaluation factor and a preset threshold value, and the accuracy and reliability of the discharge detection result are improved.

Description

Technical Field

[0001] This invention relates to the field of discharge monitoring technology for power equipment, and in particular to a method and related equipment for detecting discharge defects on the surface of insulators. Background Technology

[0002] Existing online partial discharge monitoring systems for GIS equipment mainly employ the UHF detection method. However, due to the abundance of UHF electromagnetic interference signals near GIS equipment, the UHF detection method requires acquiring more than a threshold number of UHF electromagnetic wave signals within a fixed time period to determine whether partial discharge has occurred. However, partial discharge induced by metal particles on the insulator surface is sporadic and weak. The UHF detection method based on pulse counting is difficult to detect sporadic and relatively weak partial discharges on the insulator surface, resulting in inaccurate discharge detection results. Summary of the Invention

[0003] In view of this, the present invention provides a method and related equipment for detecting discharge defects on the surface of insulators.

[0004] The specific technical solution of the first embodiment of the present invention is as follows: a discharge detection method for surface defects of an insulator, the method comprising: acquiring multiple ultra-high frequency electromagnetic wave signals and multiple low frequency electric field time-domain waveform signals of the surface defects of the insulator within a preset detection period; extracting feature values ​​from the ultra-high frequency electromagnetic wave signals and the low frequency electric field time-domain waveform signals to obtain feature values ​​of the ultra-high frequency electromagnetic wave signals and the low frequency electric field time-domain waveform signals; the feature values ​​include the mean amplitude, amplitude variance, and phase variance of the ultra-high frequency electromagnetic wave signals, and the electric field amplitude at the voltage zero-crossing moment, the voltage-electric field waveform cross-correlation coefficient, and the electric field waveform kurtosis factor of the low frequency electric field time-domain waveform signals; obtaining a discharge degree evaluation factor of the insulator based on the mean amplitude, amplitude variance, phase variance, electric field amplitude at the voltage zero-crossing moment, voltage-electric field waveform cross-correlation coefficient, electric field waveform kurtosis factor, and a preset weight; the discharge degree evaluation factor being used to characterize the discharge degree of the insulator; and determining whether the insulator has discharged based on the discharge degree evaluation factor and a preset threshold.

[0005] Preferably, the discharge level assessment factor is obtained using the following formula:

[0006] in, The discharge level assessment factor is... Let be the i-th eigenvalue, where The mean amplitude, For amplitude variance, For phase variance, The electric field amplitude at the moment the voltage crosses zero. This is the cross-correlation coefficient between voltage and electric field waveforms. The electric field waveform kurtosis factor, The preset weight for the i-th feature value, For preset coefficients, The eigenvalue is the eigenvalue mean of all eigenvalues.

[0007] Preferably, the preset weight is obtained in the following manner: averaging all feature values ​​to obtain the feature mean; adding the feature mean to the i-th feature value to obtain a first intermediate value for the i-th feature value; obtaining a second intermediate value based on the feature mean and all feature values; and dividing the first intermediate value by the second intermediate value to obtain the preset weight for the i-th feature value.

[0008] Preferably, the preset weights are obtained using the following formula:

[0009] in, The preset weight for the i-th feature value, For the i-th eigenvalue, The mean of the features, Let be the j-th eigenvalue.

[0010] Preferably, determining whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold includes: when the discharge degree evaluation factor is greater than the preset threshold, the insulator discharges; when the discharge degree evaluation factor is less than or equal to the preset threshold, the insulator does not discharge.

[0011] Preferably, the method further includes: when the discharge degree assessment factor is greater than the preset threshold, obtaining the discharge degree of the insulator according to the discharge degree assessment factor and the preset discharge severity determination rule; wherein, the preset discharge severity determination rule includes different discharge degrees corresponding to different values ​​of the discharge degree assessment factor.

[0012] Preferably, after obtaining the discharge level assessment factor of the insulator, the method further includes: obtaining a temperature correction factor for the current temperature and a humidity correction factor for the current humidity; correcting the discharge level assessment factor using the temperature correction factor and the humidity correction factor to obtain a corrected discharge level assessment factor; then, determining whether the insulator has discharged based on the discharge level assessment factor and a preset threshold includes: determining whether the insulator has discharged based on the corrected discharge level assessment factor and the preset threshold.

[0013] The specific technical solution of the second embodiment of the present invention is as follows: a discharge detection system for surface defects of insulators, the system comprising: a signal acquisition module, a feature extraction module, an evaluation factor calculation module, and a judgment module; the signal acquisition module is used to acquire multiple ultra-high frequency electromagnetic wave signals and multiple low-frequency electric field time-domain waveform signals of surface defects of insulators within a preset detection period; the feature extraction module is used to extract feature values ​​from the ultra-high frequency electromagnetic wave signals and the low-frequency electric field time-domain waveform signals to obtain feature values ​​of the ultra-high frequency electromagnetic wave signals and the low-frequency electric field time-domain waveform signals; the feature values ​​include the average amplitude value corresponding to the ultra-high frequency electromagnetic wave signal, ... The evaluation factor calculation module is used to obtain the discharge degree evaluation factor of the insulator based on the mean amplitude, the variance of the amplitude, the variance of the phase, the amplitude of the electric field at the zero-crossing time of the voltage, the cross-correlation coefficient between the voltage and the electric field waveform, the kurtosis factor of the electric field waveform, and a preset weight. The discharge degree evaluation factor is used to characterize the discharge degree of the insulator. The judgment module is used to judge whether the insulator has discharged based on the discharge degree evaluation factor and a preset threshold.

[0014] The specific technical solution of the third embodiment of the present invention is as follows: a discharge detection device for surface defects of insulators, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0015] The specific technical solution of the fourth embodiment of the present invention is as follows: a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0016] Implementing the embodiments of the present invention will have the following beneficial effects: This invention acquires multiple ultra-high frequency electromagnetic wave signals and low-frequency electric field time-domain waveform signals of an insulator within a preset detection period. It then extracts feature values ​​to obtain the mean amplitude, amplitude variance, and phase variance of the ultra-high frequency electromagnetic wave signals, and the electric field amplitude at the voltage zero-crossing moment, the voltage-electric field waveform cross-correlation coefficient, and the electric field waveform kurtosis factor of the low-frequency electric field time-domain waveform signals. This comprehensively obtains multi-dimensional information reflecting the insulator's discharge state. Combined with preset weights, a discharge degree assessment factor is calculated, which comprehensively considers the influence of various feature values ​​on the discharge degree, effectively capturing sporadic and weak discharge signals. Based on the discharge degree assessment factor and a preset threshold, it determines whether the insulator is discharging, thereby improving the accuracy and reliability of discharge detection results. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of a first embodiment of a discharge detection method for surface defects in insulators; Figure 2 A flowchart illustrating the steps of a first embodiment of a discharge detection method for surface defects in insulators; Figure 3 This is a schematic diagram of a discharge detection system for an insulator. Among them, 201 is the signal acquisition module; 202 is the feature extraction module; 203 is the evaluation factor calculation module; and 204 is the judgment module. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or apparatus.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Please see Figure 1The above is a flowchart of the steps of a discharge detection method for surface defects of an insulator according to the first embodiment of this application, in order to improve the accuracy and reliability of the discharge detection results. The method includes: Step 101: Acquire multiple ultra-high frequency electromagnetic wave signals and multiple low frequency electric field time-domain waveform signals of insulator surface defects within a preset detection period; Step 102: Extract feature values ​​from the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal to obtain feature values ​​of the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal; the feature values ​​include the amplitude mean, amplitude variance and phase variance of the ultra-high frequency electromagnetic wave signal, and the electric field amplitude at the voltage zero crossing time, the voltage-electric field waveform cross-correlation coefficient and the electric field waveform kurtosis factor of the low frequency electric field time-domain waveform signal. Step 103: Obtain the discharge degree evaluation factor of the insulator based on the mean amplitude, the variance of the amplitude, the variance of the phase, the electric field amplitude at the voltage zero crossing, the cross-correlation coefficient between the voltage and the electric field waveform, the kurtosis factor of the electric field waveform, and the preset weight; the discharge degree evaluation factor is used to characterize the discharge degree of the insulator. Step 104: Determine whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold.

[0023] Specifically, 10 cycles of UHF electromagnetic wave signals and low-frequency electric field time-domain waveform signals are acquired using UHF and electric field sensors. These signals are then normalized, and 10 sets of feature values ​​are extracted. The feature values ​​for the UHF electromagnetic wave signals are the mean amplitude, amplitude variance, and phase variance. The feature values ​​for the low-frequency electric field time-domain waveform signals are the electric field amplitude at the voltage zero-crossing point, the cross-correlation coefficient between voltage and electric field waveforms, and the kurtosis factor of the electric field waveform. These feature values ​​are then used to calculate a discharge degree assessment factor. If the discharge degree assessment factor for the insulator is 0.72 (assuming a preset threshold of 0.65), then discharge is determined to have occurred.

[0024] In a specific embodiment, the discharge level assessment factor is obtained using the following formula:

[0025] in, The discharge level assessment factor is... Let be the i-th eigenvalue, where The mean amplitude, For amplitude variance, For phase variance, The electric field amplitude at the moment the voltage crosses zero. This is the cross-correlation coefficient between voltage and electric field waveforms. The electric field waveform kurtosis factor, The preset weight for the i-th feature value, For preset coefficients, The eigenvalue is the eigenvalue mean of all eigenvalues.

[0026] Specifically, the absolute value of each feature is extracted, and the discharge degree assessment factor is calculated using this absolute value. The unit of the feature value does not affect the calculation result. The discharge degree assessment factor comprehensively considers multiple features, including the mean amplitude, amplitude variance, and phase variance of the ultra-high frequency electromagnetic wave signal, as well as the electric field amplitude at the voltage zero-crossing moment, the cross-correlation coefficient between voltage and electric field waveforms, and the kurtosis factor of the electric field waveform in the low-frequency electric field time-domain waveform signal. These features reflect the discharge state of the insulator from different perspectives. By integrating them into a single assessment factor, the discharge degree of the insulator can be comprehensively and accurately characterized, avoiding the limitations of single-feature assessment. By setting preset weights for each feature value, its contribution ratio in the assessment factor can be reasonably adjusted according to the magnitude of its influence on the discharge degree. The exponential function part in the formula performs nonlinear processing on the feature mean, which can better capture the complex relationship between feature values ​​and discharge degree. It can more sensitively reflect some small feature changes in the assessment factor, which helps to detect insulator discharge anomalies early. The final discharge degree assessment factor is a quantitative value, which facilitates intuitive judgment of the discharge degree of the insulator. By comparing with a preset threshold, it is possible to quickly and clearly determine whether an insulator has discharged, providing a simple and effective basis for insulator condition monitoring and fault diagnosis.

[0027] In a specific embodiment, the preset weight is obtained as follows: averaging all feature values ​​to obtain the feature mean; adding the feature mean to the i-th feature value to obtain a first intermediate value for the i-th feature value; obtaining a second intermediate value based on the feature mean and all feature values; and dividing the first intermediate value by the second intermediate value to obtain the preset weight for the i-th feature value.

[0028] Specifically, the average value of these six features is calculated to obtain the feature mean. For each eigenvalue (i=1,2,…,6), and compare it with the characteristic mean. Add them together to obtain the first intermediate value for the i-th eigenvalue. Calculate the second intermediate value based on the eigenvalue mean and all eigenvalues, specifically by applying the values ​​for each eigenvalue... and After summing, sum all the results again to obtain the second intermediate value. Divide the first and second intermediate values ​​to obtain the preset weight of the i-th feature value, and normalize the weights to ensure that the sum of the six weight values ​​is 1.

[0029] In a specific embodiment, the preset weight is obtained using the following formula:

[0030] in, The preset weight for the i-th feature value, For the i-th eigenvalue, The mean of the features, Let be the j-th eigenvalue. Specifically, the weight is calculated by using the sum of the i-th eigenvalue and the mean of the eigenvalues ​​as the numerator, and the sum of all eigenvalues ​​and the mean of the eigenvalues ​​as the denominator. Essentially, this measures the relative magnitude of each eigenvalue with respect to the overall eigenvalue level. This calculation method highlights features that have a more critical impact on the assessment of discharge levels, making the weight allocation more aligned with the actual importance of each feature in reflecting the insulator's discharge state. Using the sum of eigenvalues ​​and the mean eliminates, to some extent, the influence of differences in magnitude between different eigenvalues. Whether the eigenvalue is large or small, it can participate fairly in the weight allocation within this calculation framework, ensuring the equality of each feature in the assessment and preventing some features from being overlooked due to significant differences in magnitude.

[0031] In a specific embodiment, determining whether the insulator has discharged based on the discharge degree assessment factor and a preset threshold includes: when the discharge degree assessment factor is greater than the preset threshold, the insulator has discharged; when the discharge degree assessment factor is less than or equal to the preset threshold, the insulator has not discharged. Specifically, using a simple comparison between the discharge degree assessment factor and the preset threshold as the judgment basis, the result has only two clear states: "discharge occurred" and "no discharge occurred." This clear judgment method allows staff to quickly understand the status of the insulator, providing concise and effective information for subsequent decision-making and processing. After obtaining the discharge degree assessment factor, a judgment can be made immediately based on the preset threshold without complex analysis and calculation processes, greatly improving decision-making efficiency. For large-scale insulator inspection work, insulators with discharge problems can be quickly screened out, and timely maintenance can be arranged to ensure the stable operation of the power system. The preset threshold can be reasonably set and adjusted according to different insulator types, operating environments, and historical data. This makes the judgment method adaptable to various complex actual situations, improving the versatility and applicability of the method.

[0032] In a specific embodiment, the method further includes: when the discharge degree evaluation factor is greater than the preset threshold, obtaining the discharge degree of the insulator according to the discharge degree evaluation factor and the preset discharge severity determination rule; wherein, the preset discharge severity determination rule includes different discharge degrees corresponding to different values ​​of the discharge degree evaluation factor.

[0033] Specifically, following a predetermined procedure, ultra-high frequency electromagnetic wave signals and low-frequency electric field time-domain waveform signals are collected, and discharge degree assessment factors are obtained through steps such as feature extraction and weight calculation. Meanwhile, based on long-term accumulated insulator discharge data and industry experience, a pre-defined rule for judging the severity of discharge was formulated. This rule divides the discharge severity into three levels: slight, moderate, and severe. When 0.65 < When the value is ≤0.75, the insulator discharge level is judged to be slight; when 0.75 < When the value is ≤0.85, it is judged as a normal discharge level; when A value greater than 0.85 is considered a severe discharge level. If the discharge level assessment factor for a certain insulator is... =0.82. Since 0.75 < 0.82 ≤ 0.85, according to the preset discharge severity judgment rule, the discharge severity of this insulator is determined to be moderate. Subsequent staff can use this result, combined with the actual operating conditions of the substation, to formulate corresponding maintenance plans, such as focusing on observing this insulator and scheduling maintenance at appropriate times. This embodiment goes beyond simply determining whether the insulator is discharging; it further obtains the specific discharge severity based on the discharge severity assessment factor. This helps staff gain a deeper understanding of the insulator's operating status and to manage the discharge situation more precisely. Different discharge severity levels correspond to different maintenance strategies. By clearly defining the discharge severity of the insulator, staff can formulate more accurate and reasonable maintenance plans. For example, for insulators with slight discharge, the maintenance cycle can be appropriately extended; while for insulators with severe discharge, maintenance must be arranged immediately to prevent the fault from escalating and to improve maintenance efficiency and resource utilization.

[0034] In a specific embodiment, after obtaining the discharge degree evaluation factor of the insulator, the method further includes: obtaining a temperature correction factor for the current temperature and a humidity correction factor for the current humidity; correcting the discharge degree evaluation factor using the temperature correction factor and the humidity correction factor to obtain a corrected discharge degree evaluation factor; then, determining whether the insulator has discharged based on the discharge degree evaluation factor and a preset threshold includes: determining whether the insulator has discharged based on the corrected discharge degree evaluation factor and the preset threshold.

[0035] Specifically, the current ambient temperature T is obtained in real time using temperature sensors installed within the substation, and the temperature correction factor K is obtained based on a pre-established temperature-correction factor relationship model. T For example, the model shows that when the temperature is between 20-30℃, temperature has a relatively small effect on the degree of discharge, K T The value ranges from 0.95 to 1.05; when the temperature is below 20℃ or above 30℃, K... T It will change accordingly based on the degree of temperature deviation. The current ambient humidity H is obtained through a humidity sensor, and the humidity correction factor K is obtained according to a preset humidity-correction factor mapping table. H When the humidity is between 40% and 70%, K H Take 1; when the humidity is below 40% or above 70%, K H It will be adjusted according to certain rules. Then, formula F is used. d cor =F d ×K T ×K H The discharge level assessment factor is corrected to obtain the corrected discharge level assessment factor F. d cor Previously, the determination of whether an insulator was discharging was based on the uncorrected discharge level assessment factor and a preset threshold. Now, it is based on the corrected discharge level assessment factor F. d cor And a preset threshold is used for judgment. If F before correction d =0.68, but the current temperature is low, K T =0.9, humidity is high, K H =1.1, then F d cor =0.68×0.9×1.1=0.6732, the preset threshold is 0.65. If only the original Fd is used, discharge will be determined. However, after correction, it is necessary to re-determine the Fd. The cor value is re-compared with the threshold for judgment. Temperature and humidity have a significant impact on the discharge characteristics of insulators. By introducing temperature correction factors and humidity correction factors to correct the discharge degree assessment factors, the changes in environmental factors are fully considered, making the assessment results closer to the actual discharge state of the insulator and improving the accuracy and reliability of the assessment.

[0036] In a specific embodiment, please refer to Figure 2Furthermore, 10 cycles of UHF and LHF electric field time-domain waveform signals can be acquired using UHF and electric field sensors. These 10 cycles are then divided into 10 groups based on their period. The UHF electromagnetic wave signals and LHF electric field time-domain waveform signals are normalized, and feature values ​​are extracted for each group. The feature values ​​for the UHF electromagnetic wave signals are the mean amplitude, amplitude variance, and phase variance. The feature values ​​for the LHF electric field time-domain waveform signals are the electric field amplitude at the voltage zero-crossing point, the cross-correlation coefficient between voltage and electric field waveforms, and the kurtosis factor of the electric field waveform. A decision tree classifier for the severity of partial discharge is constructed based on the coupled UHF and LHF electric field time-domain waveform signals. 600 sets of UHF and electric field data from simulated partial discharge experiments induced by metal particles on the surface of insulators are acquired, and the discharges are classified into strong discharge, weak discharge, and no discharge based on their severity. Then, based on the simulated experimental data and the corresponding discharge severity, a decision tree-based classifier is established. By inputting the feature values ​​of 10 sets of field measurement signals into the established classifier, the discharge severity corresponding to each set of data can be obtained. The discharge severity with the highest frequency in the detection results is extracted. If the frequency is greater than 5, the detection is considered valid, and the discharge severity with the highest frequency is the detection result. If the frequency is less than or equal to 5, the detection is invalid, and further signal acquisition is needed to detect partial discharge on the GIS insulator surface. First, by using an electric field measurement method based on the Pockels effect, low-frequency electric field measurement of the space charge induced by partial discharge from metal particles on the GIS insulator surface is achieved, expanding the detection range for partial discharge. Second, detection is performed by using feature values ​​of coupled UHF and LHF electric field time-domain waveform signals, resulting in higher detection accuracy. Using only UHF electromagnetic wave signals, the detection effectiveness for partial discharge on the GIS insulator surface is 69%, while the detection effectiveness of coupled UHF and LHF electric field time-domain waveform signals is 96%.

[0037] In a specific embodiment, eight characteristic values ​​are pre-selected from the UHF electromagnetic wave signal: mean amplitude and variance, sum of phases, mean phase and variance, mean and variance of discharge interval time, and number of discharges. Seven characteristic values ​​are selected from the low-frequency electric field time-domain waveform signal: peak factor, skewness, electric field amplitude at voltage zero-crossing moment, kurtosis factor, cross-correlation coefficient, mean, and variance. Then, four characteristic selection algorithms—Kendall, maximum correlation minimum redundancy (mRMR), RandomForest, and ReliefF—are used to evaluate the correlation between these 15 characteristic values ​​of the UHF and low-frequency electric field time-domain waveform signals and the severity of partial discharge induced by metal particles on the insulator surface. The evaluation value of each characteristic value is obtained by multiplying the ranking of the four algorithm results by the corresponding weight coefficient. The three characteristic values ​​most correlated with the severity of discharge in the UHF electromagnetic wave signal are selected as mean amplitude, amplitude variance, and phase variance. Three features most relevant to the severity of discharge were selected from the low-frequency electric field time-domain waveform signal: electric field amplitude at voltage zero crossing, cross-correlation coefficient between voltage and electric field waveforms, and kurtosis factor of electric field waveform. A decision tree classifier was established to couple six features from the UHF and low-frequency electric field time-domain waveform signals with the severity of partial discharge induced by metal particles on the insulator surface. The discharge severity was classified into at least three categories: no discharge, weak discharge, and strong discharge.

[0038] The cross-correlation coefficient between voltage and electric field waveforms can be obtained as follows: when the voltage signal and electric field waveform are considered as two time series, the cross-correlation coefficient can quantify the relationship between them. For example, if there is a voltage signal and an electric field waveform, their cross-correlation coefficient can be calculated to determine whether they change synchronously or whether one signal lags behind the other.

[0039] In a specific embodiment, please refer to Figure 3This is a schematic diagram of a discharge detection system for an insulator according to a second embodiment of this application. The system includes: a signal acquisition module 201, a feature extraction module 202, an evaluation factor calculation module 203, and a judgment module 204. The signal acquisition module 201 is used to acquire multiple ultra-high frequency electromagnetic wave signals and multiple low-frequency electric field time-domain waveform signals of surface defects of the insulator within a preset detection period. The feature extraction module 202 is used to extract feature values ​​from the ultra-high frequency electromagnetic wave signals and the low-frequency electric field time-domain waveform signals to obtain feature values ​​of the ultra-high frequency electromagnetic wave signals and the low-frequency electric field time-domain waveform signals. The feature values ​​include the amplitude corresponding to the ultra-high frequency electromagnetic wave signal. The evaluation factor calculation module 203 is used to obtain the discharge degree evaluation factor of the insulator based on the mean amplitude, amplitude variance, phase variance, electric field amplitude at the voltage zero crossing time corresponding to the low-frequency electric field time-domain waveform signal, the voltage-electric field waveform cross-correlation coefficient, and the electric field waveform kurtosis factor; the discharge degree evaluation factor is used to characterize the discharge degree of the insulator; the judgment module 204 is used to judge whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold.

[0040] Specifically, the signal acquisition module 201 can be used to simultaneously acquire multiple sets of ultra-high frequency electromagnetic wave signals and multiple sets of low-frequency electric field time-domain waveform signals generated by surface defect discharge of insulators within a preset detection period. By combining the ultra-high frequency electromagnetic wave signals radiated during the discharge transient process and the low-frequency electric field signals formed by the charge accumulated on the surface of the insulator, the collaborative detection of surface defect discharge of insulators can be achieved from both transient and steady-state characteristics.

[0041] In a specific embodiment, the third embodiment of this application provides a discharge detection device for surface defects of insulators, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0042] Specifically, the detection equipment includes a partial discharge module, a UHF (ultra-high frequency) detection device, and an electric field detection device. The partial discharge module simulates partial discharge signals in GIS (Gas Insulator System), simulating surface particle defects by placing an insulator with adhered metal particles between high and low electrodes. The UHF detection device uses an UHF sensor installed on the GIS basin insulator, with its output signal connected to an oscilloscope. The electric field detection device places an electric field probe on the insulator surface, with its output signal also connected to an oscilloscope. Feature extraction is performed on the acquired UHF and electric field sensor output signals; the extracted features are then used to classify different discharge severity levels.

[0043] The detection equipment operates on the Pockels effect principle and includes an electric field probe made of BGO crystal, optical fiber, photodetector, and light source. The electric field probe can sense the electric field generated by space charge, thereby measuring the partial discharge generated by metal particles on the surface of the insulator.

[0044] In a specific embodiment, the fourth embodiment of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as described in any one of the first embodiments of this application.

[0045] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for detecting discharge defects on the surface of an insulator, characterized in that, The method includes: Acquire multiple ultra-high frequency electromagnetic wave signals and multiple low frequency electric field time-domain waveform signals of surface defects of insulators within a preset detection period; Feature value extraction is performed on the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal to obtain the feature values ​​of the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal; the feature values ​​include the amplitude mean, amplitude variance and phase variance of the ultra-high frequency electromagnetic wave signal, and the electric field amplitude at the voltage zero crossing time, the voltage-electric field waveform cross-correlation coefficient and the electric field waveform kurtosis factor of the low frequency electric field time-domain waveform signal. The discharge degree evaluation factor of the insulator is obtained based on the mean amplitude, the variance of amplitude, the variance of phase, the electric field amplitude at the voltage zero crossing, the cross-correlation coefficient between voltage and electric field waveforms, the kurtosis factor of electric field waveform, and a preset weight; the discharge degree evaluation factor is used to characterize the discharge degree of the insulator. The discharge level assessment factor and the preset threshold are used to determine whether the insulator has discharged.

2. The discharge detection method for insulator surface defects as described in claim 1, characterized in that, The discharge level assessment factor is obtained using the following formula: in, The discharge level assessment factor is... Let be the i-th eigenvalue, where The mean amplitude, For amplitude variance, For phase variance, The electric field amplitude at the moment the voltage crosses zero. This is the cross-correlation coefficient between voltage and electric field waveforms. The electric field waveform kurtosis factor, The preset weight for the i-th feature value, For preset coefficients, The eigenvalue is the eigenvalue mean of all eigenvalues.

3. The discharge detection method for surface defects of insulators as described in claim 1, characterized in that, The preset weights are obtained in the following manner: The mean value of the features is obtained by averaging all the feature values. Add the mean of the features to the i-th feature value to obtain the first intermediate value of the i-th feature value; Based on the mean value of the features and all the feature values, a second intermediate value is obtained; Divide the first intermediate value and the second intermediate value to obtain the preset weight of the i-th feature value.

4. The discharge detection method for insulator surface defects as described in claim 3, characterized in that, The preset weights are obtained using the following formula: in, The preset weight for the i-th feature value, For the i-th eigenvalue, The mean of the features, Let j be the j-th feature value.

5. The discharge detection method for surface defects of insulators as described in claim 1, characterized in that, The step of determining whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold includes: When the discharge level assessment factor is greater than the preset threshold, the insulator discharges. When the discharge level evaluation factor is less than or equal to the preset threshold, the insulator does not discharge.

6. The discharge detection method for surface defects of insulators as described in claim 5, characterized in that, The method further includes: When the discharge level assessment factor is greater than the preset threshold, the discharge level of the insulator is obtained according to the discharge level assessment factor and the preset discharge severity determination rule; wherein, the preset discharge severity determination rule includes different discharge levels corresponding to different values ​​of the discharge level assessment factor.

7. The discharge detection method for surface defects of insulators as described in claim 1, characterized in that, After obtaining the discharge level assessment factor of the insulator, the method further includes: Get the temperature correction factor for the current temperature and the humidity correction factor for the current humidity; The discharge level assessment factor is corrected using the temperature correction factor and the humidity correction factor to obtain the corrected discharge level assessment factor. The step of determining whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold includes: The insulator is judged to have discharged based on the modified discharge level evaluation factor and the preset threshold.

8. A discharge detection system for surface defects of insulators, characterized in that, The system includes: a signal acquisition module, a feature extraction module, an evaluation factor calculation module, and a judgment module; The signal acquisition module is used to acquire multiple ultra-high frequency electromagnetic wave signals and multiple low frequency electric field time-domain waveform signals of surface defects of insulators within a preset detection period. The feature extraction module is used to extract feature values ​​from the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal to obtain feature values ​​of the ultra-high frequency electromagnetic wave signal and the low frequency electric field time-domain waveform signal; the feature values ​​include the amplitude mean, amplitude variance and phase variance of the ultra-high frequency electromagnetic wave signal, and the electric field amplitude at the voltage zero crossing time, the voltage-electric field waveform cross-correlation coefficient and the electric field waveform kurtosis factor of the low frequency electric field time-domain waveform signal; The evaluation factor calculation module is used to obtain the discharge degree evaluation factor of the insulator based on the mean amplitude, the variance of amplitude, the variance of phase, the electric field amplitude at the voltage zero crossing, the cross-correlation coefficient between voltage and electric field waveforms, the kurtosis factor of electric field waveform, and a preset weight; the discharge degree evaluation factor is used to characterize the discharge degree of the insulator. The judgment module is used to determine whether the insulator has discharged based on the discharge degree evaluation factor and the preset threshold.

9. A discharge detection device for surface defects of insulators, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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