A method, device, terminal equipment and storage medium for fault identification based on transmission line

Abstract

The present invention discloses a fault identification method, device, terminal equipment and storage medium based on a transmission line. The method comprises the following steps: first spectral kurtosis of a vibration signal of the transmission line is used, and then according to the first spectral kurtosis and an adaptive filter, a target filter signal with the largest second spectral kurtosis value is determined, as well as a filter to be optimized for generating the target filter signal. The adaptive filter capable of extracting the target filter signal is then iteratively optimized and used as a target filter for subsequently extracting periodic fault characteristics of the signal. Finally, the vibration signal is filtered using the target filter to generate periodic fault characteristics, and a fault identification result of the transmission line is obtained based on the periodic fault characteristics. Therefore, the present invention overcomes the defects of the current EMD method such as endpoint effect and false components caused by the inability to extract periodic fault characteristics of the vibration signal, thereby effectively improving the accuracy of transmission line fault identification.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a fault identification method, device, terminal equipment and storage medium based on a power transmission line. Background Art

[0002] Conductors, as crucial infrastructure in power grids, are directly related to the stable operation of power grids and the user experience. During the design and selection of high-voltage line materials, designers must have a thorough understanding of the wire's electrical and mechanical specifications, and assess the conductor's endurance and heat resistance. Because conductors, especially ultra-high voltage transmission lines, operate in complex environments for extended periods, exposed conductors can be subject to vibrations caused by wind, ice, lightning, and system shocks, which can cause severe structural damage. Conductor strand breakage caused by line vibration can impact the line's transmission efficiency, and in severe cases, conductor breakage can cause localized power outages. Therefore, identifying transmission line faults is crucial for ensuring the safety of national power transmission.

[0003] Since kurtosis is much more sensitive to a single shock in a signal than to the periodic shock components in the signal, the currently widely used empirical mode decomposition (EMD) method can only deconvolute a few shock components for the periodic shock components in the vibration signal, resulting in problems such as endpoint effects and false components, which affect the accuracy of wire fault identification. Summary of the Invention

[0004] The embodiments of the present invention provide a fault identification method, apparatus, terminal device and storage medium based on transmission line, which overcome the defects of the current EMD method such as endpoint effect and false components caused by the inability to extract periodic fault characteristics of vibration signals, thereby effectively improving the accuracy of transmission line fault identification.

[0005] An embodiment of the present invention provides a method for identifying a fault in a transmission line, comprising:

[0006] Acquire a vibration signal of the transmission line and a first spectral kurtosis corresponding to the vibration signal;

[0007] According to the first spectral kurtosis and a preset adaptive filter, the vibration signal is divided into a plurality of vibration sub-signals, and a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal is generated;

[0008] Determining a filtered signal corresponding to the maximum second spectral kurtosis value as a target filtered signal, and determining a filter that generates the target filtered signal as a filter to be optimized;

[0009] Iteratively optimizing the filter parameters of the filter to be optimized, and in each iterative optimization process, using the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculating the correlation spectrum kurtosis of the new target filtered signal. When it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stopping the iterative optimization operation and generating the target filter;

[0010] Filtering the vibration signal using the target filter to generate a periodic fault signature;

[0011] Outputting a fault identification result of the transmission line according to the periodic fault characteristics.

[0012] Furthermore, the obtaining of the vibration signal of the transmission line and the first spectral kurtosis corresponding to the vibration signal includes:

[0013] Obtaining initial vibration signals of transmission lines;

[0014] Filtering the initial vibration signal to generate a vibration signal after noise removal;

[0015] Converting the vibration signal into a signal time series in a time-frequency form, and decomposing the signal time series to determine a non-stationary subsequence of the signal time series and a 2n-order spectral moment of the signal time series;

[0016] According to the 2n-order spectral moment, time averaging processing is performed on the non-stationary subsequence along the time axis to generate a 2n-order spectral moment average value;

[0017] The fourth-order spectral cumulant of the signal time series is calculated according to the 2n-order spectral moment average value, and the fourth-order spectral cumulant is energy normalized to generate a first spectral kurtosis of the vibration signal.

[0018] Furthermore, performing time averaging processing on the non-stationary subsequence along the time axis according to the 2n-order spectral moment to generate a 2n-order spectral moment average value includes:

[0019] The 2n-order spectral moment average value is calculated according to the following formula:

[0020]

[0021] Among them, S 2nY (f) is the average value of the 2n-order spectral moment, T is time, S 2nY (t,f) is the 2nth order spectral moment of the signal time series at the time-frequency (t,f).

[0022] Furthermore, the method of dividing the vibration signal into a plurality of vibration sub-signals according to the first spectral kurtosis and a preset adaptive filter, and generating a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal, includes:

[0023] Get the preset window function;

[0024] Performing Fourier transform on the vibration signal to generate a signal frequency domain sequence;

[0025] Windowing the signal frequency domain sequence according to the window function and the first spectral kurtosis, dividing the signal frequency domain sequence into a plurality of windowed signal subsequences with the same width, and determining a subsequence kurtosis value of each windowed signal subsequence;

[0026] According to the subsequence kurtosis value and the adaptive filter, a window fusion operation is performed on the windowed signal subsequence to generate a plurality of vibrator signals and a filter corresponding to each vibrator signal, and then a filtered signal corresponding to each vibrator signal and a second spectral kurtosis value corresponding to each filtered signal are generated.

[0027] Furthermore, the performing of a window fusion operation on the windowed signal subsequence according to the subsequence kurtosis value and the adaptive filter to generate a plurality of vibrator subsignals and a filter corresponding to each vibrator subsignal, and then generating a filtered signal corresponding to each vibrator subsignal and a second spectral kurtosis value corresponding to each filtered signal includes:

[0028] Repeating the window fusion operation according to the subsequence kurtosis value and the windowed signal subsequence, and stopping the window fusion operation when it is determined that each of the windowed signal subsequences has undergone the window fusion operation, to generate a plurality of vibration sub-signals;

[0029] sequentially inputting the vibrator signals into the adaptive filter so that the adaptive filter adaptively adjusts its own parameters to generate a filter corresponding to each vibrator signal;

[0030] generating, according to the vibrator signals and the corresponding filters, a filtered signal corresponding to each vibrator signal and a second spectral kurtosis value corresponding to each filtered signal;

[0031] The window fusion operation includes:

[0032] Obtaining a sequence to be fused and a first kurtosis value of the sequence to be fused; wherein, initially, the sequence to be fused is the first windowed signal subsequence on the signal frequency domain sequence;

[0033] Determine whether there is a sequence to be optimized;

[0034] If not, the next windowed signal subsequence of the sequence to be fused is used as the sequence to be evaluated, and the second kurtosis value of the sequence to be evaluated is obtained.

[0035] Fusing the sequence to be fused with the sequence to be evaluated to generate a first fused sequence, and calculating a third kurtosis value of the first fused sequence;

[0036] When it is determined that the third kurtosis value is greater than the first kurtosis value, and the third kurtosis value is greater than the second kurtosis value, using the next windowed signal subsequence of the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation, and using the first fused sequence as the sequence to be optimized in the next round of window fusion operation;

[0037] When it is determined that the third kurtosis value is not greater than the first kurtosis value, or the third kurtosis value is not greater than the second kurtosis value, performing an inverse Fourier transform on the sequence to be fused to generate a vibrator signal, and using the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation;

[0038] If so, obtaining the fourth kurtosis value of the sequence to be optimized, fusing the sequence to be fused and the sequence to be optimized to generate a second fused sequence, and calculating the fifth kurtosis value of the second fused sequence;

[0039] When it is determined that the fourth kurtosis value is greater than the first kurtosis value, and the fourth kurtosis value is greater than the fifth kurtosis value, using the next windowed signal subsequence of the sequence to be fused as the sequence to be fused for the next round of window fusion operation, and using the second fused sequence as the sequence to be optimized for the next round of window fusion operation;

[0040] When it is determined that the fourth kurtosis value is not greater than the first kurtosis value, or the fourth kurtosis value is not greater than the fifth kurtosis value, the sequence to be optimized is extracted and inverse Fourier transform is performed to generate a vibrator signal, and the sequence to be fused is used as the sequence to be fused for the next round of window fusion operation.

[0041] Furthermore, the filter parameters include: deconvolution period, filter length, and displacement number;

[0042] The iterative optimization operation is performed on the filter parameters of the filter to be optimized, and during each iterative optimization operation, the optimized filter to be optimized is used to filter the vibrator signal corresponding to the target filtered signal, and the relevant spectral kurtosis of the new target filtered signal is calculated. When it is determined that the relevant spectral kurtosis is greater than a preset judgment threshold, the iterative optimization operation is stopped to generate the target filter, including:

[0043] Acquiring the sampling frequency of the vibration signal and the initial displacement value of the filter to be optimized;

[0044] Determining an initial deconvolution period of the filter to be optimized according to the sampling frequency of the vibration signal, and generating a plurality of deconvolution periods to be evaluated according to the initial deconvolution period;

[0045] Repeating the iterative optimization operation on the filter to be optimized according to the initial displacement value and a number of deconvolution cycles to be evaluated until a target filter is generated;

[0046] The iterative optimization operation includes:

[0047] Obtaining a displacement value to be evaluated, a deconvolution period to be evaluated, and a filter to be optimized; wherein, initially, the displacement value to be evaluated is an initial displacement value;

[0048] updating the filter to be optimized according to a number of deconvolution cycles to be evaluated and displacement values ​​to be evaluated, to generate a filter to be evaluated;

[0049] Filtering the vibrator signal corresponding to the target filtered signal using the filter to be evaluated to obtain filtered signals to be evaluated generated by the filters to be evaluated with different deconvolution periods to be evaluated under the displacement value to be evaluated, as well as the relevant spectral kurtosis corresponding to each filtered signal to be evaluated;

[0050] sorting the relevant spectrum kurtosis and multiplying the second largest relevant spectrum kurtosis by a preset multiple to generate a judgment threshold;

[0051] Determining whether the maximum correlation spectrum kurtosis is greater than the determination threshold;

[0052] If not, the displacement value to be evaluated is increased by 1 to generate the displacement value to be evaluated required for the next round of iterative optimization operation;

[0053] If so, the deconvolution period to be evaluated corresponding to the maximum correlation spectrum kurtosis is taken as the target deconvolution period, and the target filter length is calculated based on the filtered signal to be evaluated corresponding to the maximum correlation spectrum kurtosis, and then the target displacement number is determined based on the target deconvolution period and the target filter length;

[0054] According to the target deconvolution period, the target filter length, and the target displacement number, the filter parameters of the filter to be optimized are updated to generate a target filter.

[0055] Furthermore, determining an initial deconvolution period of the filter to be optimized according to the sampling frequency of the vibration signal, and generating a plurality of deconvolution periods to be evaluated according to the initial deconvolution period, includes:

[0056] Get the preset frequency range and the preset period value range;

[0057] According to the frequency range and sampling frequency, the initial deconvolution period is calculated according to the following formula:

[0058]

[0059] Among them, T f is the initial deconvolution period, f s is the sampling frequency, f fault is the frequency range;

[0060] A plurality of deconvolution cycles to be evaluated are generated according to the cycle value range and the initial deconvolution cycle.

[0061] Another embodiment of the present invention provides a transmission line-based fault identification device, comprising:

[0062] a data acquisition module, configured to acquire a vibration signal of the transmission line and a first spectral kurtosis corresponding to the vibration signal;

[0063] an adaptive filtering module, configured to segment the vibration signal into a plurality of vibration sub-signals based on the first spectral kurtosis and a preset adaptive filter, and generate a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal;

[0064] a filter determination module, configured to determine a filtered signal corresponding to a maximum second spectral kurtosis value as a target filtered signal, and to determine a filter that generates the target filtered signal as a filter to be optimized;

[0065] a filter optimization module, configured to iteratively optimize the filter parameters of the filter to be optimized, and during each iterative optimization operation, use the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculate the correlation spectrum kurtosis of the new target filtered signal; when it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stop the iterative optimization operation and generate the target filter;

[0066] A feature generation module, configured to filter the vibration signal using the target filter to generate a periodic fault feature;

[0067] A fault identification module is used to output a fault identification result of the transmission line according to the periodic fault characteristics.

[0068] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a fault identification method based on a transmission line as described in any one of the embodiments.

[0069] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a fault identification method based on a transmission line as described in any of the above embodiments.

[0070] The following beneficial effects are achieved by implementing the present invention:

[0071] The present invention discloses a fault identification method, device, terminal equipment and storage medium based on a transmission line. The first spectral kurtosis of the vibration signal of the transmission line is used, and then according to the first spectral kurtosis and an adaptive filter, a target filter signal with the largest second spectral kurtosis value is determined, as well as a filter to be optimized for generating the target filter signal. It can be understood that the spectral kurtosis value is very sensitive to the transient impact component in the signal. By comparing the second spectral kurtosis value of each filtered signal, the filter signal that is most likely to contain fault information is determined, and then the adaptive filter that can extract the target filter signal is iteratively optimized as the target filter for subsequently extracting the periodic fault characteristics of the signal. It can be understood that the relevant spectral kurtosis is an important indicator for measuring the periodic impact characteristics of the signal. By determining in the iterative optimization process that the correlation spectrum kurtosis is greater than the judgment threshold, it can be determined that the filter to be optimized can completely extract the periodic impact characteristics of the signal, and the filter to be optimized at this time is used as the target filter. Finally, the target filter is used to filter the vibration signal to generate a periodic fault feature, and the fault identification result of the transmission line is obtained based on the periodic fault feature. Therefore, the present invention overcomes the defects of the current EMD method such as endpoint effect and false components caused by the inability to extract the periodic fault features of the vibration signal, thereby effectively improving the accuracy of transmission line fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 The present invention is a flowchart of a method for identifying a fault based on a transmission line provided by an embodiment of the present invention.

[0073] Figure 2 The figure is a schematic structural diagram of a fault identification device based on a power transmission line provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0074] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0076] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0077] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0078] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0079] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0080] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0081] See also Figure 1 , is a flow chart of a method for identifying a fault based on a transmission line according to an embodiment of the present invention, comprising:

[0082] S1. Acquire a vibration signal of a transmission line and a first spectral kurtosis corresponding to the vibration signal;

[0083] Preferably, the obtaining of the vibration signal of the transmission line and the first spectral kurtosis corresponding to the vibration signal includes:

[0084] S11, obtaining an initial vibration signal of the transmission line;

[0085] S12, filtering the initial vibration signal to generate a vibration signal after noise removal;

[0086] S13, converting the vibration signal into a signal time series in a time-frequency form, and decomposing the signal time series to determine a non-stationary subsequence of the signal time series and a 2n-order spectral moment of the signal time series;

[0087] S14, performing time averaging processing on the non-stationary subsequence along the time axis according to the 2n-order spectral moment to generate a 2n-order spectral moment average value;

[0088] Preferably, performing time averaging processing on the non-stationary subsequence along the time axis according to the 2n-order spectral moment to generate the 2n-order spectral moment average value includes:

[0089] S141. Calculate the 2n-order spectral moment average value according to the following formula:

[0090]

[0091] Among them, S 2nY (f) is the average value of the 2n-order spectral moment, T is time, S 2nY (t,f) is the 2nth order spectral moment of the signal time series at the time-frequency (t,f).

[0092] S15. Calculate the fourth-order spectral cumulant of the signal time series according to the 2n-order spectral moment average value, and perform energy normalization on the fourth-order spectral cumulant to generate a first spectral kurtosis of the vibration signal.

[0093] In a preferred embodiment of the present invention, kurtosis is defined as the normalized fourth-order center distance. In transmission line fault diagnosis, kurtosis reflects the degree to which the vibration signal deviates from the Gaussian process. It is defined as:

[0094]

[0095] Where K is the kurtosis, σ is the standard deviation of the signal x(t), is the mean of the signal x(t), and N is the signal length.

[0096] Spectral kurtosis is defined as the energy-normalized fourth-order spectral cumulant, which essentially measures the average time dispersion of the signal's time-frequency energy distribution over all frequencies. For a time-varying system h(t,s), where s is a time imaginary variable and the signal y(t) is the system response to the input x(t), the Wold-Cramer decomposition of y(t) is:

[0097]

[0098] Where H(t,f) is the Fourier transform of h(t,s), which can be expressed as the complex envelope of the signal y(t) at frequency f. X(f) is the frequency domain representation of the signal x(t), and dX(f) is the spectral process of x(t).

[0099] From the Wold-Cramer decomposition of the signal, we can see that the complex envelope H(t,f) describes the characteristic information of the non-stationary process y(t), and the information contained in H(t,f) is measured by the statistic - spectral moment. The 2nth order spectral moment of the signal is expressed as:

[0100] s 2nY (t,f)=E<|H(t,f)| 2n >*S 2nx ;

[0101] where s 2nY (t,f) is the 2nth order spectral moment of the signal, E<> is the mean operation, S 2nx is the input signal. Since the complex envelope H(t,f) is time-stationary and independent of the spectral process dX(f), s 2nY (t,f) can measure the information contained in the complex envelope H(t,f), which measures the energy intensity of the signal at the time-frequency (t,f).

[0102] For non-stationary processes, the spectral moment is a statistical indicator that characterizes its characteristic information. In order to accurately obtain the spectral moment value of the signal in practice, it is necessary to perform time averaging processing on the instantaneous moment along the time axis, that is:

[0103]

[0104] Among them S 2nY (f) is the average value of the 2n-order spectral moment, and T is time.

[0105] Since the value of the fourth-order spectrum cumulant increases as the signal deviates from the Gaussian process, it is necessary to perform energy normalization on the fourth-order spectrum cumulant, that is:

[0106]

[0107] where K Y (f) is the spectral kurtosis, which describes the average time dispersion of the signal at frequency f, s4Y (f) is the spectral moment of the 4th order spectrum, c 4Y (f) is the cumulative amount of the fourth-order spectrum, which is more practical than the kurtosis index and can accurately determine the position of the non-stationary signal in the frequency domain.

[0108] It's important to note that applying ASK filtering to transmission line vibration signals and applying kurtosis to bearing vibration signal analysis is particularly useful for detecting surface damage or pitting in conductors, as kurtosis is independent of factors like conductor size and is sensitive only to the impact component of the signal. This characteristic of kurtosis makes it suitable as a time-domain parameter for early fault detection in transmission line vibration signals.

[0109] S2. Divide the vibration signal into a plurality of vibration sub-signals according to the first spectral kurtosis and a preset adaptive filter, and generate a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal.

[0110] Preferably, the step of dividing the vibration signal into a plurality of vibration sub-signals according to the first spectral kurtosis and a preset adaptive filter, and generating a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal, includes:

[0111] S21. Obtain a preset window function;

[0112] S22. Performing Fourier transform on the vibration signal to generate a signal frequency domain sequence;

[0113] S23. Windowing the signal frequency domain sequence according to the window function and the first spectral kurtosis, dividing the signal frequency domain sequence into a plurality of windowed signal subsequences of the same width, and determining a subsequence kurtosis value of each windowed signal subsequence;

[0114] S24. Perform a window fusion operation on the windowed signal subsequence based on the subsequence kurtosis value and the adaptive filter to generate a plurality of vibration sub-signals and a filter corresponding to each vibration sub-signal, and then generate a filtered signal corresponding to each vibration sub-signal and a second spectral kurtosis value corresponding to each filtered signal.

[0115] Preferably, the performing a window fusion operation on the windowed signal subsequence according to the subsequence kurtosis value and the adaptive filter to generate a plurality of vibrator subsignals and a filter corresponding to each vibrator subsignal, and then generating a filtered signal corresponding to each vibrator subsignal and a second spectral kurtosis value corresponding to each filtered signal, includes:

[0116] S241, repeatedly performing a window fusion operation based on the subsequence kurtosis value and the windowed signal subsequence, and when it is determined that each of the windowed signal subsequences has undergone the window fusion operation, stopping the window fusion operation to generate a plurality of vibration sub-signals;

[0117] S242. Input the vibrator signals into the adaptive filter in sequence, so that the adaptive filter adaptively adjusts its own parameters to generate a filter corresponding to each vibrator signal;

[0118] S243. Generate, based on the vibrator signals and the corresponding filters, a filtered signal corresponding to each vibrator signal and a second spectral kurtosis value corresponding to each filtered signal;

[0119] The window fusion operation includes:

[0120] S2411. Obtain a sequence to be fused and a first kurtosis value of the sequence to be fused; wherein, initially, the sequence to be fused is the first windowed signal subsequence on the signal frequency domain sequence;

[0121] S2412. Determine whether there is a sequence to be optimized.

[0122] S2413: If not, use the next windowed signal subsequence of the sequence to be fused as the sequence to be evaluated, and obtain a second kurtosis value of the sequence to be evaluated.

[0123] S2414. Fusing the sequence to be fused with the sequence to be evaluated to generate a first fused sequence, and calculating a third kurtosis value of the first fused sequence;

[0124] S2415. When it is determined that the third kurtosis value is greater than the first kurtosis value, and the third kurtosis value is greater than the second kurtosis value, use the next windowed signal subsequence of the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation, and use the first fused sequence as the sequence to be optimized in the next round of window fusion operation;

[0125] S2416. When it is determined that the third kurtosis value is not greater than the first kurtosis value, or the third kurtosis value is not greater than the second kurtosis value, perform an inverse Fourier transform on the sequence to be fused to generate a oscillator signal, and use the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation.

[0126] S2417: If yes, obtain the fourth kurtosis value of the sequence to be optimized, fuse the sequence to be fused and the sequence to be optimized to generate a second fused sequence, and calculate the fifth kurtosis value of the second fused sequence;

[0127] S2418. When it is determined that the fourth kurtosis value is greater than the first kurtosis value, and the fourth kurtosis value is greater than the fifth kurtosis value, use the next windowed signal subsequence of the sequence to be fused as the sequence to be fused for the next round of window fusion operation, and use the second fused sequence as the sequence to be optimized for the next round of window fusion operation;

[0128] S2419. When it is determined that the fourth kurtosis value is not greater than the first kurtosis value, or the fourth kurtosis value is not greater than the fifth kurtosis value, extract the sequence to be optimized and perform an inverse Fourier transform to generate a vibrator signal, and use the sequence to be fused as the sequence to be fused for the next round of window fusion operation.

[0129] In a preferred embodiment of the present invention, the adaptive spectral kurtosis method determines the center frequency and bandwidth through a greedy algorithm, and fuses adjacent window functions on the frequency axis according to the principle of maximum kurtosis, thereby traversing the window functions on the entire frequency axis, and finally determines the center frequency and bandwidth of the optimal filter based on the final spectral kurtosis diagram.

[0130] First, the adaptive spectral kurtosis method is to window the signal in the frequency domain, so the signal is first transformed into the frequency domain through Fourier transform;

[0131]

[0132] in, is the Fourier transform sequence of the signal, N is the signal length, k is the current time domain, x[k] is the current time domain signal, and n is the order of the spectrum.

[0133] The frequency domain sequence after being windowed by the current window function and the next adjacent window function is:

[0134]

[0135] in The windowed sequence for the current window, is the sequence after adding window to adjacent window functions, τ and l are the coefficients of the current window and the adjacent window respectively, n is the order of the spectrum, Add window function to the current window, T La is the windowing function for adjacent windows, and a is the interval between adjacent windows.

[0136] The signal frequency domain sequence is fused with the above two window functions to form a window function, which is expressed as follows after windowing:

[0137]

[0138] in is the sequence after window fusion, is the window fusion function, w[] is the basic window function, and i is the index value of the final window.

[0139] Assume that the initial width of the window function is M. The range of the sequence on the frequency axis after windowing is [ria, (r + ri) a + M / 2]. Similarly, the range of the sequence on the frequency axis is [(r + ri) a, (r + ri) a + M], and the range of the sequence on the frequency axis is [ria, la + M / 2]. The filtered signal can be obtained by inverse Fourier transform:

[0140]

[0141] Among them, w ξ Represents w r , w Tl and w l G(λ,r) is the amplitude coefficient of the superimposed window function filter, and λ is the window overlap rate. The window overlap rate is related to the window length M and the interval a between adjacent windows.

[0142] Calculate the kurtosis value after bandpass filtering with a window function;

[0143] Definition of coincidence rate:

[0144]

[0145] Where λ is the window overlap rate, M is the window length, and a is the interval between adjacent windows.

[0146] When the window function is a Hanning window:

[0147]

[0148] Where G(λ,r) is the amplitude coefficient.

[0149] Therefore, the kurtosis value after bandpass filtering with the window function is:

[0150]

[0151] where w ξ Represents w r , w Tl and w l <> indicates the mean operation, kri is the kurtosis value of the ri-th adaptive window function, and the constant "-2" is due to is a complex number. The kurtosis value in the above formula comes from the signal The signal It comes from the signal frequency domain window filtering, that is, the kurtosis value is related to the position of the window function in the frequency domain.

[0152] In the adaptive spectral kurtosis method, the fusion operation of the window function depends on the kurtosis value of the signal after windowing. Window fusion is performed only when the kurtosis value after fusion is greater than or equal to the maximum kurtosis of the two window functions before fusion.

[0153]

[0154] Among them, w r , w Tl and w l Represent the current window, the next adjacent window, and the window after the two windows are fused; repeat the above process until all windows have been tried to be fused.

[0155] S3, determining the filtered signal corresponding to the largest second spectral kurtosis value as the target filtered signal, and determining the adaptive filter that generates the target filtered signal as the filter to be optimized;

[0156] S4, performing an iterative optimization operation on the filter parameters of the filter to be optimized, and in each iterative optimization operation, using the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculating the correlation spectrum kurtosis of the new target filtered signal, and when it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stopping the iterative optimization operation and generating the target filter;

[0157] Preferably, the filter parameters include: deconvolution period, filter length, and displacement number;

[0158] The iterative optimization operation is performed on the filter parameters of the filter to be optimized, and during each iterative optimization operation, the optimized filter to be optimized is used to filter the vibrator signal corresponding to the target filtered signal, and the relevant spectral kurtosis of the new target filtered signal is calculated. When it is determined that the relevant spectral kurtosis is greater than a preset judgment threshold, the iterative optimization operation is stopped to generate the target filter, including:

[0159] S41, obtaining the sampling frequency of the vibration signal and the initial displacement value of the filter to be optimized;

[0160] S42, repeatedly performing an iterative optimization operation on the filter to be optimized according to the initial displacement value and a number of deconvolution cycles to be evaluated until a target filter is generated;

[0161] The iterative optimization operation includes:

[0162] S421, obtaining a displacement value to be evaluated, a deconvolution period to be evaluated, and a filter to be optimized; wherein, initially, the displacement value to be evaluated is an initial displacement value;

[0163] S422, updating the filter to be optimized according to a number of deconvolution cycles to be evaluated and the displacement values ​​to be evaluated, to generate a filter to be evaluated;

[0164] S423: Filter the vibrator signal corresponding to the target filtered signal using the filter to be evaluated to obtain filtered signals to be evaluated generated by the filter to be evaluated with different deconvolution periods to be evaluated under the displacement value to be evaluated, and the associated spectral kurtosis corresponding to each filtered signal to be evaluated;

[0165] S424, sorting the relevant spectrum kurtosis, and multiplying the second largest relevant spectrum kurtosis by a preset multiple to generate a judgment threshold;

[0166] S425, determining whether the maximum correlation spectrum kurtosis is greater than the determination threshold;

[0167] S426: If not, add 1 to the displacement value to be evaluated to generate the displacement value to be evaluated required for the next round of iterative optimization operation;

[0168] S427. If yes, then use the deconvolution period to be evaluated corresponding to the maximum correlation spectrum kurtosis as the target deconvolution period, and calculate the target filter length based on the filtered signal to be evaluated corresponding to the maximum correlation spectrum kurtosis, and then determine the target displacement number based on the target deconvolution period and the target filter length;

[0169] S428. Update the filter parameters of the filter to be optimized according to the target deconvolution period, the target filter length, and the target displacement number to generate a target filter.

[0170] In a preferred embodiment of the present invention, MCKD parameter selection analysis and fault diagnosis are adopted; since the effect of the maximum correlation kurtosis deconvolution algorithm in extracting fault features is related to its parameters, before using the maximum correlation kurtosis to extract the transmission line vibration signal features, the MCKD algorithm parameters must first be analyzed and determined.

[0171] First, calculate the range of the deconvolution period. The MCKD algorithm is affected by parameters such as the filter length L, the deconvolution period T, the number of displacements M, and the number of iterations N. The deconvolution period T is determined by the characteristic frequency of the transmission line fault and the sampling frequency. Since the fault characteristic frequency fluctuates within a range according to theoretical calculations, the range of the deconvolution period can be calculated as follows:

[0172]

[0173] Where T f To calculate the value of the unwinding period, f s is the sampling frequency of the vibration signal, f fault is the theoretically calculated fault characteristic frequency range.

[0174] According to the above formula, T f ,Pick:

[0175] T fc =[[min(T f )]-5,[min(T f )]-5];

[0176] Where T fc T f range, take T fc The relevant kurtosis of the signal is calculated for each value in , and the deconvolution period T is determined according to the maximum value of the relevant kurtosis.

[0177] Secondly, determine the optimal deconvolution period T. In practical applications, due to the influence of various noises, the calculation of the correlation kurtosis under different periods cannot produce a unique and obvious maximum value, which makes it impossible to accurately determine the deconvolution period T. According to the calculation formula of the correlation kurtosis, the correlation kurtosis value is related to the value of M. The larger the value of M, the more accurate the correlation kurtosis calculation, and the more accurate the deconvolution period T parameter determined by the maximum correlation kurtosis. However, the larger the value of M, the longer the calculation time. In order to accurately determine the optimal deconvolution period T in the shortest time, specifically:

[0178] 1. M is set to 1, and T is determined according to the characteristic frequency calculation formula. fc ;

[0179] 2. Calculate T fc Each value in corresponds to the M displacement number related kurtosis value ckM of the signal, and the ckM sequence is arranged from large to small to obtain the ck_sort sequence;

[0180] 3. If the first value in the ck_sort sequence is greater than three times the second value, the optimal deconvolution period T is determined based on the location of the maximum correlation kurtosis value in ckM, and the algorithm ends. Otherwise, M = M + 1, and go to the previous step.

[0181] Regarding the filter length L parameter, according to relevant theories, the shorter the filter length, the worse the filtering effect; however, since the shorter the filter length, the faster the calculation speed, the selection of the filter length L should be based on the premise of ensuring the calculation speed as much as possible, and increase the filter length as much as possible to ensure the accuracy and speed of the algorithm.

[0182] For the displacement number M, the larger the value of M, the longer the algorithm calculation time; in the vibration signal analysis of the transmission line, according to the determined filter length L and deconvolution period T, different M values ​​are taken to extract the vibration signal MCKD algorithm respectively, and the displacement number related kurtosis value ck1 is calculated for the extracted signal, and the optimal parameter M is determined according to the maximum value of ck1.

[0183] S5. Filtering the vibration signal using the target filter to generate a periodic fault feature;

[0184] In a preferred embodiment of the present invention, because kurtosis is much more sensitive to individual impulses in a signal than to periodic impulses, the MED method can only deconvolute a few of these components. For transmission line vibration signals, once a fault point exists, it persists, resulting in periodic impulse vibrations. To address this issue, the maximum correlation kurtosis deconvolution method is proposed, which uses correlation kurtosis to extract the signal's periodic impulses.

[0185] First, determine the deconvolution period T, the number of shifts M, and the filter length L, and initialize the filter f; extract the periodic impulses of the signal through the correlation kurtosis, which is defined as follows:

[0186]

[0187] Where M is the number of shifts, T is the deconvolution period, and N is the length of the input signal. When T = 0 and M = 1, the correlation kurtosis degenerates to kurtosis.

[0188] The maximum correlation kurtosis deconvolution algorithm uses the maximization of correlation kurtosis as the optimization principle. Similar to the MED algorithm, it also extracts the fault impact component from the vibration signal obtained by the sensor by iteratively determining the optimal filter f. The objective function of the MCKD algorithm is defined as:

[0189]

[0190] Where CK is the maximum correlation kurtosis, and f is the filter coefficient corresponding to CK.

[0191] Secondly, calculate the output y of the filter f after filtering the signal x. In order to solve the filter coefficient f corresponding to the maximum correlation kurtosis CK, the following conditions must be met:

[0192]

[0193] Among them, x n-k+1 is the signal to be processed, and yn is the signal after x is filtered by filter f.

[0194] Then, calculate the change in the kurtosis before and after deconvolution, ΔCKM(T), and determine whether it is less than a given threshold, or whether the number of iterations reaches the specified maximum value. If so, stop the iteration; otherwise, return to the previous step to continue.

[0195] Calculate the first-order derivatives of the numerator and denominator of CK with respect to fk respectively:

[0196]

[0197] Where M is the number of displacements, N is the signal length, xn-k+1 is the signal before processing, and yn is the signal after xn-k+1 is filtered by filter f. is the matrix corresponding to yn.

[0198] Convert to matrix form:

[0199]

[0200] in:

[0201]

[0202] Where xr is the matrix corresponding to the signal before processing, x0 is the initial signal parameter, and xmT is each element in xr. is the matrix corresponding to yn, r represents different deconvolution cycles, and are two matrices related to yn respectively.

[0203] Finally, the signal y is extracted using the correlation spectral kurtosis convolution algorithm;

[0204]

[0205] Also because Therefore, the above formula can be transformed into:

[0206]

[0207] S6. Outputting a fault identification result of the transmission line according to the periodic fault characteristics.

[0208] This embodiment provides a fault identification method based on a transmission line. The first spectral kurtosis of the vibration signal of the transmission line is used, and then based on the first spectral kurtosis and the adaptive filter, a target filter signal with the largest second spectral kurtosis value is determined, as well as a filter to be optimized for generating the target filter signal. It can be understood that the spectral kurtosis value is very sensitive to the transient impact component in the signal. By comparing the second spectral kurtosis value of each filtered signal, the filter signal that is most likely to contain fault information is determined, and then the adaptive filter that can extract the target filter signal is iteratively optimized as the target filter for subsequently extracting the periodic fault characteristics of the signal. It can be understood that the relevant spectral kurtosis is an important indicator for measuring the periodic impact characteristics of the signal. By determining in the iterative optimization process that the correlation spectrum kurtosis is greater than the judgment threshold, it can be determined that the filter to be optimized can completely extract the periodic impact characteristics of the signal, and the filter to be optimized at this time is used as the target filter. Finally, the target filter is used to filter the vibration signal to generate a periodic fault feature, and the fault identification result of the transmission line is obtained based on the periodic fault feature. Therefore, this embodiment overcomes the defects of the current EMD method such as endpoint effect and false components caused by the inability to extract the periodic fault feature of the vibration signal, thereby effectively improving the accuracy of transmission line fault identification.

[0209] See also Figure 2 , is a schematic structural diagram of a fault identification device based on a transmission line provided by an embodiment of the present invention, comprising:

[0210] a data acquisition module, configured to acquire a vibration signal of the transmission line and a first spectral kurtosis corresponding to the vibration signal;

[0211] an adaptive filtering module, configured to segment the vibration signal into a plurality of vibration sub-signals based on the first spectral kurtosis and a preset adaptive filter, and generate a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal;

[0212] a filter determination module, configured to determine a filtered signal corresponding to a maximum second spectral kurtosis value as a target filtered signal, and to determine a filter that generates the target filtered signal as a filter to be optimized;

[0213] a filter optimization module, configured to iteratively optimize the filter parameters of the filter to be optimized, and during each iterative optimization operation, use the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculate the correlation spectrum kurtosis of the new target filtered signal; when it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stop the iterative optimization operation and generate the target filter;

[0214] A feature generation module, configured to filter the vibration signal using the target filter to generate a periodic fault feature;

[0215] A fault identification module is used to output a fault identification result of the transmission line according to the periodic fault characteristics.

[0216] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0217] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0218] Another preferred embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a fault identification method based on a transmission line as described in any one of the above embodiments.

[0219] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0220] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0221] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart EMDia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0222] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0223] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A fault identification method based on a transmission line, characterized in that: include: Acquire a vibration signal of the transmission line and a first spectral kurtosis corresponding to the vibration signal; According to the first spectral kurtosis and a preset adaptive filter, the vibration signal is divided into a plurality of vibration sub-signals, and a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal is generated; Determining a filtered signal corresponding to the maximum second spectral kurtosis value as a target filtered signal, and determining a filter that generates the target filtered signal as a filter to be optimized; Iteratively optimizing the filter parameters of the filter to be optimized, and in each iterative optimization process, using the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculating the correlation spectrum kurtosis of the new target filtered signal. When it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stopping the iterative optimization operation and generating the target filter; Filtering the vibration signal using the target filter to generate a periodic fault signature; Outputting a fault identification result of the transmission line according to the periodic fault characteristics.

2. A method for identifying a fault in a transmission line according to claim 1, characterized in that: The obtaining of the vibration signal of the transmission line and the first spectral kurtosis corresponding to the vibration signal includes: Obtaining initial vibration signals of transmission lines; Filtering the initial vibration signal to generate a vibration signal after noise removal; Converting the vibration signal into a signal time series in a time-frequency form, and decomposing the signal time series to determine a non-stationary subsequence of the signal time series and a 2n-order spectral moment of the signal time series; According to the 2n-order spectral moment, time averaging processing is performed on the non-stationary subsequence along the time axis to generate a 2n-order spectral moment average value; The fourth-order spectral cumulant of the signal time series is calculated according to the 2n-order spectral moment average value, and the fourth-order spectral cumulant is energy normalized to generate a first spectral kurtosis of the vibration signal.

3. A method for identifying faults based on transmission lines according to claim 2, characterized in that: The step of performing time averaging processing on the non-stationary subsequence along the time axis according to the 2n-order spectral moment to generate a 2n-order spectral moment average value includes: The 2n-order spectral moment average value is calculated according to the following formula: Among them, S 2nY (f) is the average value of the 2n-order spectral moment, T is time, S 2nY (t,f) is the 2nth order spectral moment of the signal time series at the time-frequency (t,f).

4. A method for identifying a fault based on a transmission line according to claim 3, characterized in that: The method of dividing the vibration signal into a plurality of vibration sub-signals according to the first spectral kurtosis and a preset adaptive filter, and generating a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal, includes: Get the preset window function; Performing Fourier transform on the vibration signal to generate a signal frequency domain sequence; Windowing the signal frequency domain sequence according to the window function and the first spectral kurtosis, dividing the signal frequency domain sequence into a plurality of windowed signal subsequences with the same width, and determining a subsequence kurtosis value of each windowed signal subsequence; According to the subsequence kurtosis value and the adaptive filter, a window fusion operation is performed on the windowed signal subsequence to generate a plurality of vibrator signals and a filter corresponding to each vibrator signal, and then a filtered signal corresponding to each vibrator signal and a second spectral kurtosis value corresponding to each filtered signal are generated.

5. The method for identifying a fault based on a transmission line according to claim 4, wherein: The method further comprises performing a window fusion operation on the windowed signal subsequence according to the subsequence kurtosis value and the adaptive filter to generate a plurality of vibrator subsignals and a filter corresponding to each vibrator subsignal, and then generating a filtered signal corresponding to each vibrator subsignal and a second spectral kurtosis value corresponding to each filtered signal, including: Repeating the window fusion operation according to the subsequence kurtosis value and the windowed signal subsequence, and stopping the window fusion operation when it is determined that each of the windowed signal subsequences has undergone the window fusion operation, to generate a plurality of vibration sub-signals; sequentially inputting the vibrator signals into the adaptive filter so that the adaptive filter adaptively adjusts its own parameters to generate a filter corresponding to each vibrator signal; generating, according to the vibrator signals and the corresponding filters, a filtered signal corresponding to each vibrator signal and a second spectral kurtosis value corresponding to each filtered signal; The window fusion operation includes: Obtaining a sequence to be fused and a first kurtosis value of the sequence to be fused; wherein, initially, the sequence to be fused is the first windowed signal subsequence on the signal frequency domain sequence; Determine whether there is a sequence to be optimized; If not, the next windowed signal subsequence of the sequence to be fused is used as the sequence to be evaluated, and the second kurtosis value of the sequence to be evaluated is obtained; Fusing the sequence to be fused with the sequence to be evaluated to generate a first fused sequence, and calculating a third kurtosis value of the first fused sequence; When it is determined that the third kurtosis value is greater than the first kurtosis value, and the third kurtosis value is greater than the second kurtosis value, using the next windowed signal subsequence of the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation, and using the first fused sequence as the sequence to be optimized in the next round of window fusion operation; When it is determined that the third kurtosis value is not greater than the first kurtosis value, or the third kurtosis value is not greater than the second kurtosis value, performing an inverse Fourier transform on the sequence to be fused to generate a vibrator signal, and using the sequence to be evaluated as the sequence to be fused in the next round of window fusion operation; If so, obtaining the fourth kurtosis value of the sequence to be optimized, fusing the sequence to be fused and the sequence to be optimized to generate a second fused sequence, and calculating the fifth kurtosis value of the second fused sequence; When it is determined that the fourth kurtosis value is greater than the first kurtosis value, and the fourth kurtosis value is greater than the fifth kurtosis value, using the next windowed signal subsequence of the sequence to be fused as the sequence to be fused for the next round of window fusion operation, and using the second fused sequence as the sequence to be optimized for the next round of window fusion operation; When it is determined that the fourth kurtosis value is not greater than the first kurtosis value, or the fourth kurtosis value is not greater than the fifth kurtosis value, the sequence to be optimized is extracted and inverse Fourier transform is performed to generate a vibrator signal, and the sequence to be fused is used as the sequence to be fused for the next round of window fusion operation.

6. A method for identifying faults based on transmission lines according to claim 5, characterized in that: The filter parameters include: deconvolution period, filter length, and displacement number; The iterative optimization operation is performed on the filter parameters of the filter to be optimized, and during each iterative optimization operation, the optimized filter to be optimized is used to filter the vibrator signal corresponding to the target filtered signal, and the relevant spectral kurtosis of the new target filtered signal is calculated. When it is determined that the relevant spectral kurtosis is greater than a preset judgment threshold, the iterative optimization operation is stopped to generate the target filter, including: Acquiring the sampling frequency of the vibration signal and the initial displacement value of the filter to be optimized; Determining an initial deconvolution period of the filter to be optimized according to the sampling frequency of the vibration signal, and generating a plurality of deconvolution periods to be evaluated according to the initial deconvolution period; Repeating the iterative optimization operation on the filter to be optimized according to the initial displacement value and a number of deconvolution cycles to be evaluated until a target filter is generated; The iterative optimization operation includes: Obtaining a displacement value to be evaluated, a deconvolution period to be evaluated, and a filter to be optimized; wherein, initially, the displacement value to be evaluated is an initial displacement value; updating the filter to be optimized according to a number of deconvolution cycles to be evaluated and displacement values ​​to be evaluated, to generate a filter to be evaluated; Filtering the vibrator signal corresponding to the target filtered signal using the filter to be evaluated to obtain filtered signals to be evaluated generated by the filters to be evaluated with different deconvolution periods to be evaluated under the displacement value to be evaluated, as well as the relevant spectral kurtosis corresponding to each filtered signal to be evaluated; sorting the relevant spectrum kurtosis and multiplying the second largest relevant spectrum kurtosis by a preset multiple to generate a judgment threshold; Determining whether the maximum correlation spectrum kurtosis is greater than the determination threshold; If not, the displacement value to be evaluated is increased by 1 to generate the displacement value to be evaluated required for the next round of iterative optimization operation; If so, the deconvolution period to be evaluated corresponding to the maximum correlation spectrum kurtosis is taken as the target deconvolution period, and the target filter length is calculated based on the filtered signal to be evaluated corresponding to the maximum correlation spectrum kurtosis, and then the target displacement number is determined based on the target deconvolution period and the target filter length; According to the target deconvolution period, the target filter length, and the target displacement number, the filter parameters of the filter to be optimized are updated to generate a target filter.

7. A method for identifying faults based on power transmission lines according to claim 6, characterized in that: The step of determining an initial deconvolution period of the filter to be optimized according to the sampling frequency of the vibration signal, and generating a plurality of deconvolution periods to be evaluated according to the initial deconvolution period, comprises: Get the preset frequency range and the preset period value range; According to the frequency range and sampling frequency, the initial deconvolution period is calculated according to the following formula: Among them, T f is the initial deconvolution period, f s is the sampling frequency, f fault is the frequency range; A plurality of deconvolution cycles to be evaluated are generated according to the cycle value range and the initial deconvolution cycle.

8. A fault identification device based on a transmission line, characterized in that: include: A data acquisition module, configured to acquire a vibration signal of the transmission line and a first spectral kurtosis corresponding to the vibration signal; an adaptive filtering module, configured to segment the vibration signal into a plurality of vibration sub-signals based on the first spectral kurtosis and a preset adaptive filter, and generate a filter corresponding to each vibration sub-signal, a filtered signal, and a second spectral kurtosis value corresponding to each filtered signal; a filter determination module, configured to determine a filtered signal corresponding to a maximum second spectral kurtosis value as a target filtered signal, and to determine a filter that generates the target filtered signal as a filter to be optimized; a filter optimization module, configured to iteratively optimize the filter parameters of the filter to be optimized, and during each iterative optimization operation, use the optimized filter to be optimized to filter the vibrator signal corresponding to the target filtered signal, and calculate the correlation spectrum kurtosis of the new target filtered signal; when it is determined that the correlation spectrum kurtosis is greater than a preset judgment threshold, stop the iterative optimization operation and generate the target filter; A feature generation module, configured to filter the vibration signal using the target filter to generate a periodic fault feature; A fault identification module is used to output a fault identification result of the transmission line according to the periodic fault characteristics.

9. A terminal device, characterized in that: The system comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for fault identification based on a transmission line according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the fault identification method based on the transmission line according to any one of claims 1 to 7.

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