Electrical equipment intermittent breakdown fault detection method

Abstract

The invention discloses an intermittent breakdown fault detection method for electrical equipment, and belongs to the technical field of power system fault detection, and the method comprises the steps: S1, collecting a current signal of the electrical equipment, and adjusting a filter based on the frequency of the current signal and a preset parameter; s2, inputting the current signal into the adjusted filter to obtain a decomposition mode; s3, iteratively updating the coefficient of the filter based on the decomposition mode and the iteration threshold to obtain a final decomposition mode; s4, performing safety analysis according to the final decomposition mode and a correlation coefficient criterion to obtain a safety analysis result; s5, determining the operation state of the corresponding electrical equipment based on the safety analysis result; corresponding current characteristics are introduced in the fault detection process of the electrical equipment, so that the fault detection accuracy of the electrical equipment is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault detection, and specifically to a method for detecting intermittent breakdown faults of electrical equipment. Background Art

[0002] In the early fault stage before an electrical equipment fails due to insulation deterioration, there are often transient signals with intermittent breakdown characteristics. By detecting the transient signals, faults of electrical equipment can be discovered in advance to avoid the chain reaction caused by electrical equipment faults. However, the transient signals with intermittent breakdown characteristics are extremely weak and rich in noise, making it extremely difficult to extract the transient signals, and thus the fault detection accuracy of electrical equipment is low.

[0003] Chinese Patent, Publication No.: CN116401530A, Publication Date: July 7, 2023, discloses a bearing fault diagnosis method based on the optimization of feature mode decomposition by the WOA algorithm, including: obtaining the bearing fault signal to be diagnosed; adaptively searching for the optimal input parameter combination of the FMD algorithm through the WOA algorithm; decomposing the bearing fault signal by the FMD algorithm with determined input parameters to obtain several modal components; calculating the kurtosis value of each modal component and selecting the modal component with the largest kurtosis value for envelope demodulation, and then extracting the corresponding fault feature information to achieve bearing fault diagnosis; however, this invention does not consider the current characteristics, resulting in low fault detection accuracy of electrical equipment. Summary of the Invention

[0004] The object of the present invention is to address the problem of low fault detection accuracy of electrical equipment caused by the prior art not considering current characteristics; a method for detecting intermittent breakdown faults of electrical equipment is proposed, which adjusts a filter based on the current signal frequency and preset parameters, and uses the decomposition mode obtained by processing the current signal with the filter to inversely iterate and trim the filter to obtain the final decomposition mode. Then, based on the final decomposition mode and the correlation coefficient criterion, a safety analysis is performed to obtain a safety analysis result, and based on the safety analysis result, the operating state of the corresponding electrical equipment is determined; by introducing the corresponding current characteristics in the process of electrical equipment fault detection, the accuracy of electrical equipment fault detection is significantly improved.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for detecting intermittent breakdown faults of electrical equipment, including the following steps: S1. Collect the current signal of the electrical equipment, and adjust the filter based on the current signal frequency and preset parameters; S2. Input the current signal into the adjusted filter to obtain a decomposition mode; S3. Iteratively update the coefficients of the filter based on the decomposition mode and the iteration threshold to obtain the final decomposition mode; S4. Perform safety analysis according to the final decomposition mode and the correlation coefficient criterion to obtain the safety analysis result; S5. Determine the operating state of the corresponding electrical equipment based on the safety analysis result.

[0006] In this solution, in order to introduce the current characteristics of the current signal during the fault detection of electrical equipment, the filter can evenly cover the current signal frequency band, and the number of filters is greater than the number of modes in the preset parameters, effectively avoiding filter frequency band overlap or omission; however, at this time, the filter may not be suitable for the specific working environment, which may lead to incorrect fault analysis. Therefore, iterative adjustment is required according to the decomposition mode. By updating the filter during the iterative adjustment process, the decomposition mode obtained by the filter processing the current signal can represent the fault state of the electrical equipment, and then the final decomposition mode is obtained; different final decomposition modes may contain the same components. In order to eliminate the situation of modal aliasing or redundancy, safety analysis is performed according to the final decomposition mode and the correlation coefficient criterion, and the final decomposition modes of the overlapping parts are removed to obtain an accurate safety analysis result. Finally, the operating state of the corresponding electrical equipment is determined based on the safety analysis result, significantly improving the accuracy of electrical equipment fault detection.

[0007] Preferably, in the S1, the adjustment formula for adjusting the filter based on the current signal frequency and the preset parameters is specifically: , ; , ; , ; In the formula, is the lower cut-off frequency, is the upper cut-off frequency, is the total number of filters, is the current signal frequency, is the window corresponding to the filter, is the time index of the window, is the length of the current signal, , is the filter length in the preset parameters.

[0008] In this solution, based on the adjustment formula, it can be seen that the current signal frequency is evenly divided into In a segment, set corresponding upper and lower cut-off frequencies. At this time, the number of filters can be determined according to the number of segments of the current signal frequency. Use the upper and lower cut-off frequencies to set the frequency range of the filter to achieve coverage of the current signal frequency band, effectively avoiding overlapping or missing of the filter frequency bands, and adjust the window function corresponding to the filter using the filter length and the number of modes in the preset parameters, so as to ensure that the interference information in the current signal, such as noise, can be accurately filtered out, and the decomposition state obtained by processing the current signal through the filter can accurately reflect the characteristics of the original current signal; Secondly, if the current signal is discrete data, the length of the current signal is the number of sampling data points corresponding to the sampling time and sampling frequency of the current signal. If the current signal is continuous data, the length of the current signal is the duration of the current signal. The current signal at least includes power frequency current signal, high-frequency fault characteristics, and noise; The preset parameters at least include the number of modes, filter length, and iteration threshold set based on electrical equipment.

[0009] Preferably, in S2, the decomposition formula corresponding to the decomposition mode obtained by inputting the current signal into the adjusted filter is specifically: ; In the formula, is the time index of the th iteration of the th decomposition mode, is the th iteration of the th decomposition mode, is the current signal, is the th iteration of the th coefficient of the filter corresponding to the decomposition mode.

[0010] In this solution, the time index is actually the output time corresponding to the decomposition mode.

[0011] Preferably, in S3, the specific process of iteratively updating the filter coefficients based on the decomposition mode and the iteration threshold to obtain the final decomposition mode is: S31. Estimate the input period of the decomposition mode based on the autocorrelation function of the decomposition mode, and calculate the correlation kurtosis according to the input period and the decomposition mode; S32. Perform weighted conversion based on the correlation kurtosis to update the filter coefficients. Determine whether to end the update based on the update times of the filter coefficients and the iteration threshold. If the update times are less than the iteration threshold, input the current signal into the updated filter to obtain the iterative decomposition mode, update the decomposition mode based on the iterative decomposition mode and execute S31. If the update times are greater than or equal to the iteration threshold, mark the decomposition mode with the maximum correlation kurtosis as the final decomposition mode.

[0012] In this solution, since fault components will be generated during the failure of electrical equipment, the decomposition mode corresponding to the fault component has the characteristic of the largest relevant kurtosis. By performing weighted conversion to update the filter coefficients based on the relevant kurtosis during the iterative adjustment process, it is ensured that the update of the filter coefficients is based on the largest possible relevant kurtosis, so that the decomposition mode obtained by the filter processing the current signal can be closest to the fault component to the greatest extent. Furthermore, the final decomposition mode can represent the fault state of the electrical equipment, and the coefficients of the filter can also be adapted to the working environment. The relevant kurtosis can be used as a characteristic parameter to measure the degree of outliers of outlier data and can also be used to reflect the periodicity and impact of signals.

[0013] Preferably, in S31, the autocorrelation function is specifically: ; In the formula, is the autocorrelation function value at , is the time index of the corresponding decomposition mode, is the length of the current signal corresponding to the decomposition mode, is the current signal at is the current signal at

[0014] In this solution, when the autocorrelation function value is at its maximum value, the corresponding time point is the input period. Therefore, the time point corresponding to the maximum value after the first zero crossing is used as the input period.

[0015] Preferably, in S31, the relevant kurtosis formula corresponding to the relevant kurtosis is specifically: ; In the formula, is the relevant kurtosis corresponding to the decomposition mode , is the time index of the corresponding decomposition mode, is the length of the current signal corresponding to the decomposition mode, is the input period.

[0016] Preferably, in S32, the update formula for updating the filter coefficients based on the relevant kurtosis through weighted conversion is: ; ; In the formula, is the relevant kurtosis, is the filter coefficient matrix, is a current signal matrix, is a weighting matrix, represents the conjugate transpose operation, , is the updated coefficient, , is the eigenvalue.

[0017] Preferably, in the step S4, the specific process of performing the security analysis according to the final decomposition mode and the correlation coefficient criterion to obtain the security analysis result is as follows: S41. Input the final decomposition mode into the correlation coefficient formula to calculate the correlation coefficient, and sort the correlation coefficients in descending order to obtain a correlation coefficient sequence; S42. Based on the correlation coefficient sequence, extract the correlation kurtosis corresponding to the final decomposition mode of the largest correlation coefficient to obtain the largest correlation kurtosis, and based on the correlation coefficient sequence, extract the correlation kurtosis corresponding to the final decomposition mode of the second largest correlation coefficient to obtain the second largest correlation kurtosis; S43. Compare the magnitudes of the largest correlation kurtosis and the second largest correlation kurtosis. If the largest correlation kurtosis is less than the second largest correlation kurtosis, then eliminate the final decomposition mode corresponding to the largest correlation kurtosis, subtract one from the total number of filters corresponding to the final decomposition mode, and execute S44. If the largest correlation kurtosis is greater than the second largest correlation kurtosis, then eliminate the final decomposition mode corresponding to the second largest correlation kurtosis, subtract one from the total number of filters corresponding to the final decomposition mode, and execute S44. If the largest correlation kurtosis is equal to the second largest correlation kurtosis, then establish a security label and end the analysis; S44. Based on the total number of filters and the number of modes in the preset parameters, determine whether to end the analysis. If the total number of filters is greater than the number of modes, then execute S41. If the total number of filters is less than or equal to the number of modes, then mark the corresponding final decomposition mode as a fault component and end the analysis; S45. After the analysis is completed, organize the security label and the fault component to obtain the security analysis result.

[0018] In this solution, it is necessary to calculate the correlation coefficients of different modes. When the correlation coefficient is too large, the corresponding two modes have a high similarity, and there is a situation of mode aliasing or redundancy. At this time, the correlation kurtosis of the two modes is compared. Based on the characteristic that the correlation kurtosis corresponding to the fault component appears to be the largest when the electrical equipment fails, the final decomposition mode with a smaller correlation kurtosis is removed. At the same time, the total number of filters is reduced by one, and when the total number of filters is greater than the number of modes in the preset parameters, the correlation coefficient is calculated cyclically and the final decomposition mode is deleted until the total number of filters is less than or equal to the number of modes. Secondly, when the correlation kurtosis corresponding to the two modes with the largest correlation coefficient is equal, it proves that the two modes are exactly the same, and when the correlation kurtosis does not appear to be the largest, it proves that the corresponding electrical equipment has not failed. At this time, a safety label needs to be established and the analysis ends. Otherwise, if a larger correlation kurtosis can always be compared until there is still a largest correlation kurtosis at the end of the analysis, it proves that the corresponding electrical equipment has failed, and the corresponding final decomposition mode is marked as the fault component.

[0019] Preferably, in S41, the correlation coefficient formula is specifically: ; In the formula, is the final decomposition mode and the final decomposition mode The correlation coefficient between them, is The final decomposition mode at , is the length of the corresponding current signal, is the time period All final decomposition modes within The average value of, is The final decomposition mode at , is the time period All final decomposition modes within The average value of.

[0020] Preferably, in S5, the specific process of determining the operating state of the corresponding electrical equipment based on the safety analysis result is: Count the number of safety labels in the safety analysis result and count the number of fault components in the safety analysis result; Based on the number of safety labels and the number of fault components, judge the fault state of the corresponding electrical equipment. If the number of safety labels is greater than the safety threshold and the number of fault components is less than the fault threshold, it is determined that the corresponding electrical equipment is in a normal state. If the number of safety labels is less than or equal to the safety threshold and the number of fault components is greater than or equal to the fault threshold, it is determined that the corresponding electrical equipment has failed.

[0021] Advantages of the present invention: (1) In this application, the frequency of the current signal is evenly divided into segments, and corresponding upper cut-off frequency and lower cut-off frequency are set. At this time, the number of filters can be determined according to the number of segments of the current signal frequency. The frequency range of the filter is set by using the upper cut-off frequency and the lower cut-off frequency to achieve coverage of the current signal frequency band, effectively avoiding overlap or omission of the filter frequency band, and adjusting the window function corresponding to the filter by using the filter length and the number of modes in the preset parameters, so as to ensure that the interference information in the current signal, such as noise, can be accurately filtered out, and the decomposition state obtained by processing the current signal through the filter can accurately reflect the characteristics of the original current signal; (2) In this application, the filter is iteratively adjusted according to the relevant kurtosis of the decomposition mode, and based on the characteristic that the relevant kurtosis corresponding to the fault component is the largest when the electrical equipment fails. By performing weighted conversion and updating the filter coefficients based on the relevant kurtosis during the iterative adjustment process, it is ensured that the update of the filter coefficients is based on the relevant kurtosis being as large as possible, so that the decomposition mode obtained by the filter processing the current signal can be closest to the fault component to the greatest extent, and the coefficients of the filter can also fit the working environment, thereby obtaining the final decomposition mode corresponding to the fault component, effectively improving the comprehensiveness of extracting the corresponding fault component when the electrical equipment fails; (3) In this application, safety analysis is performed according to the final decomposition mode and the correlation coefficient criterion to obtain a safety analysis result. During the safety analysis process, the relevant kurtosis of the two modes with high correlation coefficients is compared. If the relevant kurtosis is equal, based on the characteristic that the relevant kurtosis corresponding to the fault component is the largest when the electrical equipment fails, the absence of the largest relevant kurtosis proves that the electrical equipment has no fault, and a safety label is established. If the relevant kurtosis is not equal, the final decomposition mode with small relevant kurtosis is excluded, and the number of modes is adjusted until the fault component corresponding to the electrical equipment is extracted. Finally, the number of occurrences of the safety label and the fault component in the safety analysis result are counted to accurately judge the operating state of the corresponding electrical equipment, significantly improving the accuracy of electrical equipment fault detection. Description of the Drawings

[0022] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives and advantages of the present invention will become more obvious. The drawings are only used for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0023] Figure 1 It is a schematic flow chart of a method for detecting intermittent breakdown faults of electrical equipment; Figure 2 It is a waveform schematic diagram of a current signal. Detailed Embodiments

[0024] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only the best embodiments of the present invention, which are only used to explain the present invention and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0025] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] Embodiment 1: As Figure 1 shown, this embodiment provides a method for detecting intermittent breakdown faults of electrical equipment, including the following steps: S1. Collect the current signal of the electrical equipment, and adjust the filter based on the current signal frequency and preset parameters; The corresponding adjustment formula is specifically: ; ; ; In the formula, is the lower cut-off frequency, is the upper cut-off frequency, is the total number of filters, is the current signal frequency, is the window corresponding to the filter, is the time index of the window, is the length of the current signal, , is the filter length in the preset parameters.

[0027] In this embodiment, as Figure 2 shown, the length is 1000, which fluctuates in the range of 2A to -2A, and it can be clearly seen from the waveform of the current signal that pulse-like fault characteristics appear in the second half of each half-cycle. There is also a large amount of noise in the current signal; and parameters for adjusting the filter are preset. Specifically, the number of modes is 1, the filter length is 40, and the iteration threshold is set to 20. Then, the frequency of the current signal is evenly divided into 5 segments using the adjustment formula, and a FIR filter is set for each segment. A total of 5 FIR filters are set, and the 5 FIR filters together form a filter bank. At this time, each filter also corresponds to an initial coefficient. Secondly, the window function is selected as the Hamming window, which can be expressed as: , .

[0028] S2. Input the current signal into the adjusted filter to obtain the decomposed modes; The corresponding decomposition formula is specifically: ; In the formula, is the time index of the th iteration of the th decomposed mode, is the th iteration of the th decomposed mode, is the current signal, is the th iteration of the th decomposed mode corresponding to the filter coefficient.

[0029] S3. Iteratively update the coefficients of the filter based on the decomposed modes and the iteration threshold to obtain the final decomposed modes; S31. Estimate the input period of the decomposed mode based on the autocorrelation function of the decomposed mode, and calculate the correlation kurtosis according to the input period and the decomposed mode; The specific autocorrelation function is: ; In the formula, is the autocorrelation function value at , is the time index of the corresponding decomposed mode, is the length of the current signal corresponding to the decomposed mode, is the current signal at is the current signal at The corresponding correlation kurtosis formula for the correlation kurtosis is: ; Wherein, is the decomposition mode corresponding relevant kurtosis, is the time index of the corresponding decomposition mode, is the length of the current signal corresponding to the decomposition mode, is the input period; S32. Update the filter coefficients based on the relevant kurtosis for weighted conversion, and determine whether to end the update based on the update times of the filter coefficients and the iteration threshold. If the update times are less than the iteration threshold, input the current signal into the updated filter to obtain the iterative decomposition mode, update the decomposition mode based on the iterative decomposition mode and execute S31. If the update times are greater than or equal to the iteration threshold, mark the decomposition mode with the maximum relevant kurtosis as the final decomposition mode; The update formula for updating the filter coefficients based on the relevant kurtosis is: ; ; Wherein, is the relevant kurtosis, is the filter coefficient matrix, is the current signal matrix, is the weighted matrix, represents the conjugate transpose operation, , is the updated coefficient, , is the eigenvalue.

[0030] In this embodiment, the basis for updating the filter coefficients is the relevant kurtosis of the decomposition mode. The relevant kurtosis is a characteristic parameter for measuring the outlier degree of outlier data, which can reflect the periodicity and impact of the current signal. Generally, the relevant kurtosis corresponding to the fault component generated when an electrical equipment fails is the largest; for the convenience of calculation, the decomposition formula and the relevant kurtosis formula can be converted into matrix forms. The matrix form of the decomposition formula is specifically: ; Wherein, is the th iteration of the th decomposition mode, is the current signal, is the coefficient of the filter corresponding to the th iteration of the th decomposition mode; The matrix form of the relevant kurtosis formula is specifically: ; Wherein, is the decomposition mode The corresponding correlation kurtosis is, For the The iteration decomposition mode, represents the conjugate transpose operation, is the weighted correlation matrix; Specifically, the decomposed mode, coefficient, and current signal can be expressed as: ; ; ; The weighted correlation matrix It can be specifically expressed as: ; The simultaneous equations associate the correlation kurtosis with the filter coefficients to obtain an update formula. When solving the update formula, in order to make the decomposition mode obtained by the filter processing the current signal close to the fault component, or even directly filter out the fault component, its objective function, that is, the correlation kurtosis corresponding to the decomposition mode should be as large as possible. Therefore, in the iterative update process, each iteration solves the filter coefficient corresponding to the maximum correlation kurtosis value, which is equivalent to solving the eigenvector related to the maximum eigenvalue in the generalized eigenvalue problem. Therefore, the solution method of the update formula is the same as that of solving the generalized eigenvalue problem. After the solution is completed, the filter coefficients and the corresponding decomposition modes, weighting matrices, etc. are changed to achieve gradual updates accompanying the iterative process until the update is terminated when the number of updates is greater than or equal to the iteration threshold, and the decomposition mode with the largest correlation kurtosis is marked as the final decomposition mode.

[0031] S4. Perform safety analysis according to the final decomposition mode and correlation coefficient criterion to obtain safety analysis results; S41, inputting the final decomposition mode into the correlation coefficient formula to calculate the correlation coefficient, and sorting the correlation coefficients to obtain a correlation coefficient sequence; The correlation coefficient formula is specifically: ; In the formula, The final decomposition mode With the final decomposition mode The correlation coefficient between for The final decomposition mode when , is the length of the corresponding current signal, For time period All final decomposition modes The average value of for The final decomposition mode when , is the average value of all final decomposition modes within a time period; S42. Extract the relevant kurtosis of the final decomposition mode corresponding to the first largest correlation coefficient from the correlation coefficient sequence to obtain the first largest relevant kurtosis, and extract the relevant kurtosis of the final decomposition mode corresponding to the second largest correlation coefficient from the correlation coefficient sequence to obtain the second largest relevant kurtosis; S43. Compare the magnitudes of the first largest relevant kurtosis and the second largest relevant kurtosis. If the first largest relevant kurtosis is less than the second largest relevant kurtosis, then eliminate the final decomposition mode corresponding to the first largest relevant kurtosis, subtract one from the total number of filters corresponding to the final decomposition mode, and execute S44. If the first largest relevant kurtosis is greater than the second largest relevant kurtosis, then eliminate the final decomposition mode corresponding to the second largest relevant kurtosis, subtract one from the total number of filters corresponding to the final decomposition mode, and execute S44. If the first largest relevant kurtosis is equal to the second largest relevant kurtosis, then establish a safety label and end the analysis; S44. Determine whether to end the analysis based on the total number of filters and the number of modes in the preset parameters. If the total number of filters is greater than the number of modes, then execute S41. If the total number of filters is less than or equal to the number of modes, then mark the corresponding final decomposition mode as a fault component and end the analysis; S45. After the analysis ends, organize the safety label and the fault component to obtain a safety analysis result. S5. Determine the operating state of the corresponding electrical equipment based on the safety analysis result;

[0032] Count the number of safety labels in the safety analysis result, and count the number of fault components in the safety analysis result; Judge the fault state of the corresponding electrical equipment based on the number of safety labels and the number of fault components. If the number of safety labels is greater than the safety threshold and the number of fault components is less than the fault threshold, then determine that the corresponding electrical equipment is in a normal state. If the number of safety labels is less than or equal to the safety threshold and the number of fault components is greater than or equal to the fault threshold, then determine that the corresponding electrical equipment has a fault.

[0033] This embodiment has at least the following substantial effects: (1) In this application, the current signal frequency is evenly divided into Segment, set corresponding upper cut-off frequency and lower cut-off frequency. At this time, the number of filters can be determined according to the number of segments of the current signal frequency. The frequency range of the filter is set by using the upper cut-off frequency and the lower cut-off frequency to achieve coverage of the current signal frequency band, effectively avoiding overlap or omission of the filter frequency band, and adjusting the window function corresponding to the filter by using the filter length and the number of modes in the preset parameters, so as to ensure that the interference information in the current signal, such as noise, can be accurately filtered out, and the decomposition state obtained by processing the current signal through the filter can accurately reflect the characteristics of the original current signal; (2) In this application, the filter is iteratively adjusted according to the relevant kurtosis of the decomposition mode, and based on the characteristic that the relevant kurtosis corresponding to the fault component is the largest when the electrical equipment fails. By performing weighted conversion to update the filter coefficients based on the relevant kurtosis during the iterative adjustment process, it is ensured that the update of the filter coefficients is based on the relevant kurtosis being as large as possible, so that the decomposition mode obtained by the filter processing the current signal can be closest to the fault component to the greatest extent, and the coefficients of the filter can also fit the working environment, thereby obtaining the final decomposition mode corresponding to the fault component, effectively improving the comprehensiveness of the extraction of the fault component corresponding to the electrical equipment failure; (3) In this application, safety analysis is performed according to the final decomposition mode and the correlation coefficient criterion to obtain a safety analysis result. During the safety analysis process, the relevant kurtosis of the two modes with high correlation coefficients is compared. If the relevant kurtosis is equal, based on the characteristic that the relevant kurtosis corresponding to the fault component is the largest when the electrical equipment fails, the absence of the largest relevant kurtosis proves that the electrical equipment has not failed, and a safety label is established. If the relevant kurtosis is not equal, the final decomposition mode with a small relevant kurtosis is removed, and the number of modes is adjusted until the fault component corresponding to the electrical equipment is extracted. Finally, the number of occurrences of the safety label and the fault component in the safety analysis result is counted to accurately judge the operating state of the corresponding electrical equipment, significantly improving the accuracy of electrical equipment fault detection.

[0034] The above specific implementation manners are the preferred implementation manners of the present invention, which do not limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation manner. All equivalent changes made according to the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for detecting intermittent breakdown faults of electrical equipment, characterized in that: The following steps are involved: S1. Collect the current signal of the electrical equipment and adjust the filter based on the current signal frequency and preset parameters; S2, inputting the current signal into the adjusted filter to obtain the decomposed mode; S3, iteratively updating the coefficients of the filter based on the decomposed mode and the iterative threshold to obtain the final decomposed mode; S4. Perform safety analysis according to the final decomposition mode and correlation coefficient criterion to obtain safety analysis results; S5. Determine the operating status of the corresponding electrical equipment based on the safety analysis results.

2. A method for detecting intermittent breakdown faults of electrical equipment according to claim 1, characterized in that: In S1, the adjustment formula corresponding to the filter adjustment based on the current signal frequency and the preset parameters is specifically: ， ； ， ； ， ； In the formula, is the lower cutoff frequency, is the upper cutoff frequency, is the total number of filters, is the current signal frequency, is the window corresponding to the filter, is the time index of the window, is the length of the current signal, , is the filter length in the preset parameters.

3. A method for detecting intermittent breakdown faults of electrical equipment according to claim 1, characterized in that: In S2, the current signal is input into the adjusted filter to obtain the decomposition formula corresponding to the decomposition mode: ； In the formula, For the The iteration The time index of the decomposed modes, For the The iteration decomposition mode, is the current signal, For the The first iteration The coefficients of the filter corresponding to the decomposed modes.

4. A method for detecting intermittent breakdown faults of electrical equipment according to claim 1, characterized in that: In S3, the specific process of iteratively updating the coefficients of the filter based on the decomposed mode and the iterative threshold to obtain the final decomposed mode is: S31, estimating the input period of the decomposition mode based on the autocorrelation function of the decomposition mode, and calculating the correlation kurtosis according to the input period and the decomposition mode; S32. Update the filter coefficients by weighted transformation based on the correlation kurtosis. Determine whether to end the update based on the number of updates of the filter coefficients and the iteration threshold. If the number of updates is less than the iteration threshold, input the current signal into the updated filter to obtain the iterative decomposition mode. Update the decomposition mode based on the iterative decomposition mode and execute S31. If the number of updates is greater than or equal to the iteration threshold, mark the decomposition mode with the largest correlation kurtosis as the final decomposition mode.

5. A method for detecting intermittent breakdown faults of electrical equipment according to claim 4, characterized in that: In S31, the autocorrelation function is specifically: ； In the formula, for The autocorrelation function value at is the time index of the corresponding decomposed mode, is the length of the current signal corresponding to the decomposed mode, for The current signal when for The current signal when .

6. A method for detecting intermittent breakdown faults of electrical equipment according to claim 4, characterized in that: In S31, the correlation kurtosis formula corresponding to the correlation kurtosis is specifically: ； In the formula, To decompose the mode The corresponding correlation kurtosis is, is the time index of the corresponding decomposed mode, is the length of the current signal corresponding to the decomposed mode, is the input cycle.

7. A method for detecting intermittent breakdown faults of electrical equipment according to claim 4, characterized in that: In S32, the update formula corresponding to the weighted conversion update filter coefficient based on the correlation kurtosis is: ； ； In the formula, is the correlation kurtosis, is the filter coefficient matrix, is the current signal matrix, is the weighting matrix, represents the conjugate transpose operation, , is the updated coefficient, , is the characteristic value.

8. The method for detecting intermittent breakdown faults of electrical equipment according to claim 1, characterized in that: In S4, the specific process of performing safety analysis according to the final decomposition mode and correlation coefficient criterion to obtain the safety analysis result is: S41, inputting the final decomposition mode into the correlation coefficient formula to calculate the correlation coefficient, and sorting the correlation coefficients to obtain a correlation coefficient sequence; S42, extracting the correlation kurtosis of the final decomposition mode corresponding to the first largest correlation coefficient based on the correlation coefficient sequence to obtain the first largest correlation kurtosis, and extracting the correlation kurtosis of the final decomposition mode corresponding to the second largest correlation coefficient based on the correlation coefficient sequence to obtain the second largest correlation kurtosis; S43, based on the comparison of the first largest correlation kurtosis and the second largest correlation kurtosis, if the first largest correlation kurtosis is smaller than the second largest correlation kurtosis, the final decomposition mode corresponding to the first largest correlation kurtosis is eliminated, the total number of filters corresponding to the final decomposition mode is reduced by one, and S44 is executed; if the first largest correlation kurtosis is larger than the second largest correlation kurtosis, the final decomposition mode corresponding to the second largest correlation kurtosis is eliminated, the total number of filters corresponding to the final decomposition mode is reduced by one, and S44 is executed; if the first largest correlation kurtosis is equal to the second largest correlation kurtosis, a safety label is established and the analysis ends; S44, judging whether to end the analysis based on the total number of filters and the number of modes in the preset parameters, if the total number of filters is greater than the number of modes, executing S41, if the total number of filters is less than or equal to the number of modes, marking the corresponding final decomposed mode as a fault component and ending the analysis; S45. After the analysis is completed, the safety labels and fault components are sorted to obtain safety analysis results.

9. A method for detecting intermittent breakdown faults of electrical equipment according to claim 8, characterized in that: In S41, the correlation coefficient formula is specifically: ； In the formula, The final decomposition mode With the final decomposition mode The correlation coefficient between for The final decomposition mode when , is the length of the corresponding current signal, For time period All final decomposition modes The average value of for The final decomposition mode when , For time period All final decomposition modes within The average value of .

10. The method for detecting intermittent breakdown faults of electrical equipment according to claim 1, characterized in that: In S5, the specific process of determining the operating status of the corresponding electrical equipment based on the safety analysis result is: Count the number of safety labels in the safety analysis results, and count the number of fault components in the safety analysis results; The fault status of the corresponding electrical equipment is judged based on the number of safety tags and the number of fault components. If the number of safety tags is greater than the safety threshold and the number of fault components is less than the fault threshold, the corresponding electrical equipment is judged to be in a normal state. If the number of safety tags is less than or equal to the safety threshold and the number of fault components is greater than or equal to the fault threshold, the corresponding electrical equipment is judged to be faulty.

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