Signal filtering method and electronic equipment

Abstract

The invention provides a signal filtering method and electronic equipment. Relates to the technical field of signal processing. The method comprises the following steps: acquiring a first traveling wave signal and a second traveling wave signal which are respectively acquired at two ends of a to-be-detected cable; performing frequency domain transformation on the first traveling wave signal and the second traveling wave signal to obtain first frequency spectrum data corresponding to the first traveling wave signal and second frequency spectrum data corresponding to the second traveling wave signal; determining a center frequency of band-pass filtering based on the first spectrum data and the second spectrum data; generating a filter coefficient according to the center frequency and a preset window function; and filtering the first traveling wave signal and the second traveling wave signal based on the filtering coefficient to obtain a filtered first traveling wave signal and a filtered second traveling wave signal. According to the invention, the central frequency and the filtering coefficient of band-pass filtering are determined by performing spectral analysis on the acquired traveling wave signals at the two ends of the to-be-detected cable, so that the traveling wave signals are filtered, and the accuracy of traveling wave feature extraction is improved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a signal filtering method and electronic equipment. Background Art

[0002] With the rapid development of power systems, cables are increasingly used. However, various faults may occur during the operation of cables, such as short circuits, open circuits, insulation damage, etc. These faults may cause the power system to fail to operate normally and may even cause safety accidents. Therefore, it is very important to quickly and accurately locate cable faults. At present, traveling wave cable fault distance measurement is a commonly used cable fault location method, which uses high-frequency transient current or voltage signals when a cable system fails to locate the fault.

[0003] However, the existing traveling wave cable fault location method is easily affected by factors such as noise and traveling wave transmission attenuation, resulting in low accuracy in the extraction of traveling wave features of the fault signal, which in turn affects the accuracy of subsequent positioning analysis. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a signal filtering method and an electronic device for performing spectrum analysis on the traveling wave signals collected at both ends of the cable to be detected, determining the center frequency and filter coefficient of the bandpass filter, so as to filter the traveling wave signals and improve the accuracy of traveling wave feature extraction.

[0005] In a first aspect, an embodiment of the present application provides a signal filtering method, the method comprising: acquiring a first traveling wave signal and a second traveling wave signal respectively collected at both ends of a cable to be detected; performing frequency domain transformation on the first traveling wave signal and the second traveling wave signal respectively to obtain first spectrum data corresponding to the first traveling wave signal and second spectrum data corresponding to the second traveling wave signal; determining a center frequency of a bandpass filter based on the first spectrum data and the second spectrum data; generating a filtering coefficient according to the center frequency and a preset window function; filtering the first traveling wave signal and the second traveling wave signal respectively based on the filtering coefficient to obtain a filtered first traveling wave signal and a filtered second traveling wave signal.

[0006] The embodiment of the present application obtains the traveling wave signals at both ends of the cable to be detected, performs spectrum analysis on the traveling wave signals at both ends, and determines the center frequency and filtering parameters of the bandpass filter based on the results of the spectrum analysis at both ends of the cable to be detected. In this process, since the center frequency of the bandpass filter is combined with the energy characteristics of the traveling wave signals at both ends of the cable to be detected, the influence of single-ended noise is eliminated. In addition, the filter coefficient obtained based on the center frequency is used to filter the signals at both ends of the cable to be detected, so that only the traveling wave signals near the center frequency are extracted, thereby improving the accuracy of feature extraction.

[0007] In some embodiments, determining the center frequency of a bandpass filter based on first spectrum data and second spectrum data includes: dividing the first spectrum data into a plurality of first frequency band windows according to a preset window size and step size, and dividing the second spectrum data into the same number of second frequency band windows as the first frequency band windows; the first frequency band window includes first sub-spectrum data intercepted from the first spectrum data; the second frequency band window includes second sub-spectrum data intercepted from the second spectrum data; respectively calculating the energy value of each first frequency band window and the energy value of each second frequency band window; determining a target frequency band window based on the energy value of each first frequency band window and the energy value of each second frequency band window; and using the center frequency of the target frequency band window as the center frequency of the bandpass filter.

[0008] In the embodiment of the present application, the first spectrum data and the second spectrum data are divided into the same number of frequency band windows according to the preset window size and step size, so that the frequency components of the signal can be analyzed more finely. In addition, by calculating the energy value of each frequency band window and determining the target frequency band window based on the energy value, it is helpful to identify the area where the energy is concentrated. Finally, the center frequency of the target frequency band window is used as the center frequency of the bandpass filter, so that when filtering the first traveling wave signal or the second traveling wave signal of the cable to be detected, it focuses on the most important frequency range in the signal, thereby improving the accuracy of the traveling wave feature extraction.

[0009] In some embodiments, the energy value of each first frequency band window and the energy value of each second frequency band window are calculated respectively, including: calculating the energy spectrum of the corresponding first frequency band window using the first sub-spectrum data, and calculating the energy spectrum of the corresponding second frequency band window using the second sub-spectrum data; normalizing the energy spectrum of each first frequency band window and the energy spectrum of each second frequency band window respectively to obtain the energy value of each first frequency band window and the energy value of each second frequency band window.

[0010] The embodiment of the present application normalizes the energy value of each frequency band window, thereby eliminating the influence caused by the difference in signal strength in different frequency bands, so that the first traveling wave signal and the second traveling wave signal have a consistent evaluation standard for comparative analysis, which is convenient for understanding and comparing the relative energy distribution of the first traveling wave signal and the second traveling wave signal in different frequency bands, and also avoids calculation problems caused by too large or too small values, and improves numerical stability. This helps to improve the accuracy and efficiency of traveling wave signal processing and analysis.

[0011] In some embodiments, each first frequency band window corresponds to each second frequency band window one by one; determining the target frequency band window based on the energy value of each first frequency band window and the energy value of each second frequency band window includes: calculating the total energy value of each first frequency band window and the energy value of the corresponding second frequency band window to obtain multiple total energy values; taking the first frequency band window or the second frequency band window corresponding to the maximum total energy value among the multiple total energy values ​​as the target frequency band window.

[0012] The embodiment of the present application calculates the total energy value of the first frequency band window and the corresponding second frequency band window, and uses the first frequency band window or the second frequency band window with the maximum total energy value as the target frequency band window, so that the finally determined target frequency band window can better reflect the most important frequency range in the signal, thereby improving the accuracy of the center frequency, thereby improving the accuracy of the traveling wave feature extraction.

[0013] In some embodiments, before calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window, the method also includes: if the relationship between the energy value of each first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, then the first frequency band window is used as the first target frequency band window, and the corresponding second frequency band window is used as the second target frequency band window; accordingly, calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: calculating the total energy value of the energy value of each first target frequency band window and the energy value of the corresponding second target frequency band window.

[0014] The embodiment of the present application excludes the frequency band windows without commonality in the first spectrum data and the second spectrum data by first determining whether the relationship between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, so as to reduce the calculation cost. At the same time, since the frequency band windows without commonality in the first spectrum data and the second spectrum data are eliminated, the accuracy of the target frequency band window finally determined is also improved, and the accuracy of the center frequency is further improved, thereby improving the accuracy of the traveling wave feature extraction.

[0015] In some embodiments, the relationship between the energy value of each first frequency band window and the energy value of the corresponding second frequency band window meets a preset relationship, including: the ratio of the energy value of the first frequency band window to the energy value of the corresponding second frequency band window meets a preset ratio; or, the absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets a preset threshold.

[0016] The embodiment of the present application uses the ratio or absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window as the judgment basis, which can reflect the difference in energy distribution of the first frequency band window and the corresponding second frequency band window in the frequency spectrum, and helps to more intuitively understand the energy relationship between the first frequency band window and the corresponding second frequency band window, thereby making it easier to determine the target frequency band window.

[0017] In some embodiments, calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: calculating the energy product of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window; or, calculating the energy sum of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window.

[0018] In the embodiment of the present application, by calculating the energy product or the energy sum, the joint distribution of energy between the two frequency band windows corresponding to the first spectrum data and the second spectrum data can be comprehensively measured, thereby further improving the accuracy of the center frequency.

[0019] In some embodiments, the method further includes: determining the spectrum resolution according to the sampling rates corresponding to the first traveling wave signal and the second traveling wave signal and the data points of the first traveling wave signal; and determining the preset window size and step size according to the spectrum resolution.

[0020] In the embodiment of the present application, the spectrum resolution can be obtained by the sampling rate and length of the traveling wave signal, and the preset window size and step length are determined to distinguish the close frequency components and avoid frequency confusion. The preset window size and step length are optimized to reduce the spectrum leakage phenomenon.

[0021] In some embodiments, generating filter coefficients according to a center frequency and a preset window function includes: determining a unit impulse response of a bandpass filter according to the center frequency; and generating filter coefficients based on the preset window function and the unit impulse response.

[0022] The embodiment of the present application determines the unit impulse response of the bandpass filter based on the center frequency, and generates filter coefficients based on a preset window function and the unit impulse response, thereby reducing spectrum leakage and improving frequency resolution.

[0023] In some embodiments, the filtered first traveling wave signal includes a first first wave signal; the filtered second traveling wave signal includes a second first wave signal; after obtaining the filtered first traveling wave signal and the filtered second traveling wave signal, the method further includes: calculating the time difference between the arrival time of the first first wave signal and the arrival time of the second first wave signal; calculating the fault point position of the cable to be detected based on the time difference and the transmission speed of the first traveling wave signal or the second traveling wave signal in the cable to be detected.

[0024] The embodiment of the present application determines the position of the fault point of the cable to be detected by using the filtered first traveling wave signal and the second traveling wave signal, thereby improving the accuracy of fault point positioning.

[0025] In a second aspect, an embodiment of the present application provides a signal filtering device, which includes: an acquisition module, used to acquire a first traveling wave signal and a second traveling wave signal respectively collected at both ends of a cable to be detected; a transformation module, used to perform frequency domain transformation on the first traveling wave signal and the second traveling wave signal respectively, to obtain first spectrum data corresponding to the first traveling wave signal and second spectrum data corresponding to the second traveling wave signal; a determination module, used to determine the center frequency of the bandpass filter based on the first spectrum data and the second spectrum data; a generation module, used to generate a filter coefficient according to the center frequency and a preset window function; a filtering module, used to filter the first traveling wave signal and the second traveling wave signal respectively based on the filter coefficient, to obtain a filtered first traveling wave signal and a filtered second traveling wave signal.

[0026] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a storage medium and a bus, wherein the processor and the memory communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method steps of the first aspect.

[0027] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, comprising: the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method steps of the first aspect.

[0028] In a fifth aspect, an embodiment of the present application provides a computer program product, including: a computer program, which executes the method steps of the first aspect when executed by a processor.

[0029] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or will be understood by implementing the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a flow chart of a signal filtering method provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of the structure of a signal filtering device provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0035] It should be noted that all technical and scientific terms used in this document have the same meanings as those generally understood by technicians in the technical field to which this application belongs; the terms used in this document are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0036] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0037] During the research, the researchers found that the traveling wave method of cable fault distance measurement uses the high-frequency transient current or voltage signal when a cable system fails to identify the reflected wave head and thus accurately locate the fault. However, since the period of the high-frequency transient signal is very short, and there are multiple components of different frequencies and phases superimposed, and it is also affected by noise interference, the traditional traveling wave feature extraction method has the problem of difficulty in extracting the traveling wave features of the fault signal.

[0038] For example, when the empirical mode decomposition method processes complex signals, it is easy to encounter mode aliasing problems, and the noise reduction effect is not ideal. Wavelet transform requires the selection of appropriate wavelet basis functions and scales. Since high-voltage cable fault current signals are difficult to predict directly, traditional phase mode transform is difficult to apply.

[0039] Therefore, how to improve the accuracy of feature extraction of fault signals under the influence of noise and traveling wave transmission attenuation, so as to perform positioning analysis, is an important issue facing the current traveling wave cable fault location technology.

[0040] Based on this, the present application provides a signal filtering method for performing spectrum analysis on the traveling wave signals collected at both ends of the cable to be detected, determining the center frequency and filter coefficient of the bandpass filter, so as to filter the traveling wave signals and improve the accuracy of traveling wave feature extraction.

[0041] Figure 1 A flow chart of a signal filtering method provided in an embodiment of the present application. It can be understood that the signal filtering method provided in an embodiment of the present application can be applied to a terminal device (also referred to as an electronic device) and a server; wherein the terminal device can specifically be a smart phone, a tablet computer, a computer, a personal digital assistant (PDA), etc.; the server can specifically be an application server or a Web server.

[0042] To facilitate understanding of the technical solution provided in the embodiment of the present application, taking the server as the execution subject as an example, the application scenario of the signal filtering method provided in the embodiment of the present application is introduced.

[0043] like Figure 1 As shown, the method includes:

[0044] Step S101: The server obtains a first traveling wave signal and a second traveling wave signal respectively collected at two ends of the cable to be detected.

[0045] In step S101, measuring equipment is installed at both ends of the cable to be detected, so as to collect traveling wave signals at both ends of the cable to be detected by using the measuring equipment, and the collected traveling wave signals are uploaded to a server.

[0046] For example, a Field-Programmable Gate Array (FPGA) is used to collect high-frequency traveling wave signals of the three-phase circuit ABC of the cable to be detected in parallel and upload them to a data service platform via a microcontroller or processor based on an ARM architecture.

[0047] When a cable fails, it will generate a traveling wave pulse signal with a large amplitude change. In order to avoid signal distortion and high-frequency signal loss, the high-frequency traveling wave sampling rate is 100MHz, and the traveling wave signal duration is 1ms.

[0048] It should be noted that the sampling rate may be set to other values ​​based on different analysis scenarios. Generally, the sampling rate may be higher than 10 MHz.

[0049] Since the traveling wave signals at both ends of the cable to be detected are collected, the traveling wave signals acquired by the server include a first traveling wave signal and a second traveling wave signal.

[0050] For ease of understanding, one of the two ends of the cable to be tested is called the master end, and the other end is called the slave end. The traveling wave signal collected by the master end is taken as the first traveling wave signal, which is a discrete time series traveling wave signal x m [n] The traveling wave signal collected from the end is used as the second traveling wave signal, and the second traveling wave signal is a traveling wave signal x of a discrete time series. s [n].

[0051] Optionally, the traveling wave signal collected by the master end may be used as the second traveling wave signal, and the traveling wave signal collected by the slave end may be used as the first traveling wave signal.

[0052] Step S102: The server performs frequency domain transformation on the first traveling wave signal and the second traveling wave signal respectively to obtain first spectrum data corresponding to the first traveling wave signal and second spectrum data corresponding to the second traveling wave signal.

[0053] Since the traveling wave signal of the cable to be tested collected by the measuring equipment is a time domain signal, in order to more intuitively observe and extract the traveling wave information, the time domain signal is converted into the frequency domain to obtain the corresponding frequency domain information.

[0054] For example, through Fourier transform, the first traveling wave signal is converted into the corresponding first spectrum data, and the second traveling wave signal is converted into the second spectrum data, so that different frequency components and their amplitudes can be clearly seen, which helps to identify and separate the frequency components of the traveling wave signal, and facilitates the subsequent extraction of traveling wave features.

[0055] The Fourier transform formula is as follows:

[0056]

[0057] Where x[n] is the first traveling wave signal x m [n] or the second traveling wave signal x s The nth data point of [n], n∈[0,N-1], N represents the number of data points of the first traveling wave signal or the second traveling wave signal collected within the duration of the traveling wave signal based on the sampling rate, X[k] is the first traveling wave signal x m [n] or the second traveling wave signal x s [n] is the frequency domain signal after Fourier transform, k is the index of the frequency component, k∈[0,N-1], j is an imaginary number, and π is the ratio of pi.

[0058] The first spectrum data and the second spectrum data are obtained based on the frequency domain signal obtained by Fourier transform.

[0059] Step S103: The server determines a center frequency of the bandpass filter based on the first spectrum data and the second spectrum data.

[0060] In order to effectively filter the noise in the traveling wave signal and reduce the influence of the noise on the feature extraction of the traveling wave signal, it is usually necessary to design a correlation filter to filter the traveling wave signal. Optionally, the embodiment of the present application filters the traveling wave signal by designing a bandpass filter.

[0061] Among them, bandpass filtering is a signal processing technology used to extract components within a specific frequency range from a signal while suppressing or rejecting frequency components outside the range.

[0062] Since it is necessary to extract components within a specific frequency range, it is necessary to determine the center frequency and bandwidth of the bandpass filter so as to extract the components within the specific frequency range in the traveling wave signal based on the center frequency and bandwidth.

[0063] The center frequency refers to the center of the frequency range that the bandpass filter allows to pass, that is, the main oscillation frequency. Bandwidth refers to the frequency range. These two parameters determine the signal frequency band that the filter allows to pass, and directly affect the application effect of the filter.

[0064] Based on this, after obtaining the first spectrum data and the second spectrum data, the server performs spectrum analysis based on the first spectrum data and the second spectrum data to extract energy spectrum feature information, thereby determining the center frequency of the bandpass filter.

[0065] When the server determines the center frequency of the bandpass filter based on the first spectrum data and the second spectrum data, the determination may be performed in the following manner:

[0066] Method 1: Use a machine learning algorithm to classify and regress the first spectrum data and the second spectrum data to automatically identify and determine the center frequency. For example, by training a support vector machine (SVM) or a neural network, the first spectrum data and the second spectrum data are input and the center frequency is output. Since the machine learning algorithm can automatically adapt to different signal environments and can analyze the input spectrum data in a targeted manner, the accuracy of determining the center frequency can be improved, thereby improving the accuracy of filtering.

[0067] Method 2: According to a preset window size and step size, the first spectrum data is divided into a number of first frequency band windows, and the second spectrum data is divided into the same number of second frequency band windows as the first frequency band windows; the first frequency band window includes first sub-spectrum data intercepted from the first spectrum data; the second frequency band window includes second sub-spectrum data intercepted from the second spectrum data; the energy value of each first frequency band window and the energy value of each second frequency band window are calculated respectively; the target frequency band window is determined based on the energy value of each first frequency band window and the energy value of each second frequency band window; the center frequency of the target frequency band window is used as the center frequency of the bandpass filter.

[0068] In mode 2, by dividing the first spectrum data and the second spectrum data into the same number of frequency band windows according to the preset window size and step size, the frequency components of the signal can be analyzed more finely. In addition, by calculating the energy value of each frequency band window and determining the target frequency band window based on the energy value, it is helpful to identify the area where the energy is concentrated. Finally, the center frequency of the target frequency band window is used as the center frequency of the bandpass filter, so that when filtering the first traveling wave signal or the second traveling wave signal of the cable to be detected, the most important frequency range in the signal is focused, thereby improving the accuracy of the traveling wave feature extraction.

[0069] In the specific implementation process, the preset window size T = [f 1 ,f 2 ] and step size f can be preset values. For example: window size T = [1KHz, 10KHz], step size f = 2KHz.

[0070] The default window size T = [f 1 ,f 2 ] and the step size f can also be determined based on the spectral resolution of the traveling wave sampling rate as a reference, where T = [f 1 ,f 2 ] 1 represents the starting frequency of window T, f 2 represents the end frequency of window T. The specific process of how to determine the window size and step length based on the spectrum resolution of the traveling wave sampling rate as a reference is shown in the following embodiment and will not be described in detail here.

[0071] After determining the window size and step size, the first spectrum data is divided into a plurality of first frequency band windows, and the second spectrum data is divided into a plurality of second frequency band windows having the same number as the first frequency band windows. Furthermore, for the first spectrum data and the second spectrum data, respectively, the energy distribution within each frequency band window is calculated in a window sliding manner.

[0072] When calculating the energy distribution within each frequency band window, the amplitude spectrum within the frequency band window can be squared, and the square values ​​of all frequency points within the frequency band window can be accumulated to obtain the total energy of the frequency band window. Among them, f 1i represents the starting frequency of the ith frequency band window, f 2i represents the end frequency of the ith frequency band window, E i Represents the energy value of the i-th frequency band window.

[0073] Therefore, when the frequency band window T slides over the entire spectrum with a step length of f, multiple energy values ​​can be obtained according to the first spectrum data and the second spectrum data. The energy value of each frequency band window of the first spectrum data is represented as Em , the energy value of each frequency band window of the second spectrum data is represented as E s .

[0074] In this process, since the first spectrum data and the second spectrum data are divided into the same number of frequency band windows according to the same window size and step size, by respectively calculating the energy in different frequency band windows at the master and slave ends of the cable to be detected, it can be identified whether the spectra of the master and slave waveforms are consistent.

[0075] After calculating and obtaining the energy value of each first frequency band window and the energy value of each second frequency band window, the target frequency band window can be determined based on the energy value of each first frequency band window and the energy value of each second frequency band window, so that the center frequency of the target frequency band window is used as the center frequency of the bandpass filter, that is, in, is the starting frequency of the target frequency band window, is the end frequency of the target frequency band window.

[0076] Step S104: The server generates filter coefficients according to the center frequency and a preset window function.

[0077] In step 104, the filter coefficients generally refer to parameters or weights used to construct filters in digital signal processing. The filter coefficients determine the frequency selectivity of the filter, that is, the ability of the filter to pass certain frequency components and suppress other frequency components.

[0078] Therefore, in order to effectively filter the collected first traveling wave signal and the second traveling wave signal, a filter coefficient is generated according to the center frequency and a preset window function.

[0079] Among them, the preset window function is: Among them, α=0.54, β=0.46, N is the number of data of the first wave signal or the second wave signal, and n is the sample index. The frequency domain signal is truncated by a preset window function to reduce spectrum energy leakage and improve the effect of spectrum analysis, thereby improving the accuracy of spectrum analysis.

[0080] Based on the center frequency and the given ideal frequency response H d (e jw ), the unit impulse response r(n) is obtained as follows:

[0081]

[0082] Among them, w c1 is the starting frequency, w c2 is the end frequency, where the starting frequency w c1 =f center -f, end frequency w c2 =fcenter +f, f represents the step size. Therefore, the bandwidth of the bandpass filter designed based on this center frequency is 2f, and its filtering frequency range is [f center -f,f center +f].

[0083] The unit impulse response refers to the output response of the system when it is a sequence of 1 in discrete time, only takes the value of 1 at time 0 and is 0 at other times.

[0084] Finally, the filter coefficient h(n) is generated based on the preset window function and unit impulse response as follows:

[0085] h(n)=w(n)*r(n)

[0086] Therefore, the server generates the filter coefficient according to the center frequency and the preset window function, including: the server determines the unit impulse response of the bandpass filter according to the center frequency; and generates the filter coefficient based on the preset window function and the unit impulse response.

[0087] Step S105: The server filters the first traveling wave signal and the second traveling wave signal respectively based on the filtering coefficient to obtain a filtered first traveling wave signal and a filtered second traveling wave signal.

[0088] In step S105, after determining the filter coefficient, the server filters the first traveling wave signal and the second traveling wave signal respectively by using the filter coefficient. For example, the first traveling wave signal or the second traveling wave signal is obtained by performing a discrete convolution operation on the filter coefficient and the first traveling wave signal or the second traveling wave signal, thereby achieving the purpose of filtering.

[0089] The discrete convolution operation of the filter coefficient and the first wave signal or the second wave signal is as follows:

[0090] y(n)=h(n)*x[n]

[0091] Wherein, h(n) is the filter coefficient, x[n] is the input signal, which represents the first wave signal or the second wave signal of the cable to be detected, y(n) is the output signal, which represents the filtered first wave signal or the filtered second wave signal obtained by the convolution operation, and the symbol * represents a discrete convolution operation.

[0092] The embodiment of the present application obtains the traveling wave signals at both ends of the cable to be detected, performs spectrum analysis on the traveling wave signals at both ends, and determines the center frequency and filtering parameters of the bandpass filter based on the results of the spectrum analysis at both ends of the cable to be detected. In this process, since the center frequency of the bandpass filter is combined with the energy characteristics of the traveling wave signals at both ends of the cable to be detected, the influence of single-ended noise is eliminated. In addition, the filter coefficient obtained based on the center frequency is used to filter the signals at both ends of the cable to be detected, so that only the traveling wave signals near the center frequency are extracted, thereby improving the accuracy of feature extraction.

[0093] In some embodiments, the server calculates the energy value of each first frequency band window and the energy value of each second frequency band window respectively, including: the server calculates the energy spectrum of the corresponding first frequency band window using the first sub-spectrum data, and calculates the energy spectrum of the corresponding second frequency band window using the second sub-spectrum data; the server normalizes the energy spectrum of each first frequency band window and the energy spectrum of each second frequency band window respectively to obtain the energy value of each first frequency band window and the energy value of each second frequency band window.

[0094] In the specific implementation process, the influence of signal strength differences in different frequency band windows on the calculation results is taken into account. Therefore, after the energy value of each frequency band window is calculated, the energy value of each frequency band window is normalized to obtain the normalized energy value of each frequency band window. The calculation formula for the normalized energy value of each frequency band window is as follows:

[0095]

[0096] Among them, E i represents the energy value of the i-th frequency band window, E T Indicates the total energy value of the first spectrum data or the second spectrum data, E i_1 Represents the normalized energy value of the i-th frequency band window.

[0097] By normalizing the energy value of each frequency band window, the influence caused by the difference in signal strength in different frequency bands can be eliminated, so that the first traveling wave signal and the second traveling wave signal have consistent evaluation standards for comparative analysis, which is convenient for understanding and comparing the relative energy distribution of the first traveling wave signal and the second traveling wave signal in different frequency bands, and also avoids calculation problems caused by too large or too small values, and improves numerical stability. This helps to improve the accuracy and efficiency of traveling wave signal processing and analysis.

[0098] In some embodiments, each first frequency band window corresponds to each second frequency band window one by one; the server determines the target frequency band window based on the energy value of each first frequency band window and the energy value of each second frequency band window, including: calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window to obtain multiple total energy values; taking the first frequency band window or the second frequency band window corresponding to the maximum total energy value among the multiple total energy values ​​as the target frequency band window.

[0099] In the specific implementation process, since the acquired first traveling wave signal and the second traveling wave signal are signals emitted from the same detection point in the cable to be detected to both ends of the cable to be detected, in order to effectively analyze the first traveling wave signal and the second traveling wave signal, it is necessary to divide the first spectrum data and the second spectrum data into the same number of frequency band windows, and each first frequency band window corresponds to each second frequency band window one by one.

[0100] For example, the first traveling wave signal is transformed in the frequency domain to obtain first spectrum data distributed over 0-50 MHz, and the second traveling wave signal is transformed in the frequency domain to obtain second spectrum data distributed over 0-50 MHz.

[0101] The first spectrum data is divided into 10 first frequency band windows with a preset window size of 5MHz, wherein the frequency ranges of the 10 first frequency band windows are [0MHz, 5MHz], [5MHz, 10MHz], [10MHz, 15MHz], [15MHz, 20MHz], [20MHz, 25MHz], [25MHz, 30MHz], [30MHz, 35MHz], [35MHz, 40MHz], [40MHz, 45MHz], [45MHz, 50MHz], respectively.

[0102] The second spectrum data is divided into 10 second frequency band windows with a preset window size of 5MHz, wherein the frequency ranges of the 10 second frequency band windows are [0MHz, 5MHz], [5MHz, 10MHz], [10MHz, 15MHz], [15MHz, 20MHz], [20MHz, 25MHz], [25MHz, 30MHz], [30MHz, 35MHz], [35MHz, 40MHz], [40MHz, 45MHz], [45MHz, 50MHz], respectively.

[0103] That is, the first frequency band window with a frequency range of [0MHz, 5MHz] corresponds to the second frequency band window with a frequency range of [0MHz, 5MHz], the first frequency band window with a frequency range of [5MHz, 10MHz] corresponds to the second frequency band window with a frequency range of [5MHz, 10MHz], and so on, so that each first frequency band window corresponds to each second frequency band window one by one.

[0104] The total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window refers to the comprehensive energy situation of the first frequency band window and the corresponding second frequency band window.

[0105] Calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: calculating the energy product of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window. In this case, the energy product is used as the total energy value of the energy value of the first frequency band window and the energy value of the corresponding second frequency band window.

[0106] By calculating the energy product (ie, the product of the energy values ​​of the first frequency band window and the corresponding second frequency band window), the joint distribution of energy between the two frequency band windows corresponding to the first spectrum data and the second spectrum data can be comprehensively measured.

[0107] The larger the energy product, the more concentrated the signal energy of the corresponding frequency band window is, and it contains the most important features and components of the traveling wave signal. Because the first spectrum data and the second spectrum data are divided into the same number of frequency band windows according to the same window size and step size, the first frequency band window or the second frequency band window corresponding to the maximum energy product among the multiple energy products is used as the target frequency band window.

[0108] The formula for calculating the energy product is:

[0109]

[0110] The formula for the maximum energy product is:

[0111] e max_1 =max(e i_1 )

[0112] in, represents the energy value of the i-th frequency band window in the first spectrum data, represents the energy value of the i-th frequency band window in the second spectrum data, e i_1 represents the energy product, e max_1 represents the maximum energy product.

[0113] Optionally, calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: calculating the energy sum of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window. In this case, the energy sum is used as the total energy value of the energy value of the first frequency band window and the energy value of the corresponding second frequency band window.

[0114] By calculating the energy sum (i.e., the sum of the energy values ​​of the first frequency band window and the corresponding second frequency band window), a measure of the overall energy of the two windows can be quickly given, which helps to exclude those frequency band windows whose energy sum obviously does not meet the requirements.

[0115] The formula for calculating the energy sum is:

[0116]

[0117] The formula for the maximum energy sum is:

[0118] e max_2 =max(e i_2 )

[0119] in, represents the energy value of the i-th frequency band window in the first spectrum data, represents the energy value of the i-th frequency band window in the second spectrum data, e i_2 represents energy and, e max_2 represents the maximum energy and.

[0120] In this process, by calculating the energy product or the energy sum, the joint distribution of energy between the two frequency band windows corresponding to the first spectrum data and the second spectrum data can be comprehensively measured, further improving the accuracy of the center frequency. In addition, the first frequency band window or the second frequency band window with the maximum total energy value is used as the target frequency band window, so that the target frequency band window finally determined can better reflect the most important frequency range in the signal, thereby improving the accuracy of the center frequency, thereby improving the accuracy of the traveling wave feature extraction.

[0121] In some embodiments, before calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window, the method also includes: if the relationship between the energy value of each first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, then the first frequency band window is used as the first target frequency band window, and the corresponding second frequency band window is used as the second target frequency band window; accordingly, calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: calculating the total energy value of the energy value of each first target frequency band window and the energy value of the corresponding second target frequency band window.

[0122] In the specific implementation process, it is considered that the purpose of this application is to jointly determine the center frequency of the bandpass filter based on the first spectrum data and the second spectrum data, so that the traveling wave signals collected from both ends of the cable to be detected can be filtered, thereby reducing the impact of single-ended noise.

[0123] If the energy values ​​of the frequency band windows corresponding to the first spectrum data and the second spectrum data are significantly different, it means that the traveling wave signals at both ends are unevenly distributed in the same frequency band window, and there is no commonality between the two, and the calculated total energy value has no reference significance.

[0124] For example, the energy value of the first frequency band window of the first spectrum data is 1, and the energy value of the first frequency band window of the second spectrum data is 10. Since 1 and 10 are quite different, it means that the signal difference between the first sub-spectrum data and the second sub-spectrum data corresponding to the first frequency band window is quite large, and the waveforms are inconsistent. Therefore, the value of the center frequency determined in this way is not high.

[0125] Therefore, before calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window, it is necessary to consider whether the first traveling wave signal and the second traveling wave signal have similarity, that is, it is necessary to consider whether the spectral distribution of each first frequency band window and the corresponding second frequency band window have similarity.

[0126] For example, whether the frequency spectrum distribution of a first frequency band window with a frequency range of [0 MHz, 5 MHz] and a corresponding second frequency band window with a frequency range of [0 MHz, 5 MHz] are similar.

[0127] Therefore, in order to avoid some invalid calculations and further improve the accuracy of the center frequency. In the process of determining the target frequency band window based on the energy value of each first frequency band window and the energy value of each second frequency band window, it is first determined whether the relationship between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship. If the preset relationship is met, the total energy value of the energy value of each target first frequency band window and the energy value of the corresponding second target frequency band window is calculated to obtain multiple total energy values.

[0128] The specific process of calculating the total energy value of the energy value of each first target frequency band window and the energy value of the corresponding second target frequency band window can be found in the above embodiment and will not be described in detail here.

[0129] When judging whether the relationship between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, it is determined whether the ratio of the energy value of the first frequency band window to the energy value of the corresponding second frequency band window meets the preset ratio; or, it is determined whether the absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset threshold.

[0130] Among them, the preset ratio is a preset value, where its value range is usually q∈[0.5,1].

[0131] The preset threshold is a pre-set value, which can be specifically set according to actual conditions.

[0132] The embodiment of the present application excludes the frequency band windows without commonality in the first spectrum data and the second spectrum data by first determining whether the relationship between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, so as to reduce the calculation cost. At the same time, since the frequency band windows without commonality in the first spectrum data and the second spectrum data are eliminated, the accuracy of the target frequency band window finally determined is also improved, and the accuracy of the center frequency is further improved, thereby improving the accuracy of the traveling wave feature extraction.

[0133] Optionally, if the ratio of the energy value of the first frequency band window to the energy value of the corresponding second frequency band window meets the preset ratio, or if the absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset threshold, the energy value of the first frequency band window and the energy value of the corresponding second frequency band window are multiplied to obtain an energy product. The energy product is used as the total energy value of the first frequency band window and the corresponding second frequency band window. Finally, the first frequency band window or the second frequency band window corresponding to the maximum energy product among the multiple energy products is used as the target frequency band window.

[0134] Optionally, if the ratio of the energy value of the first frequency band window to the energy value of the corresponding second frequency band window meets the preset ratio, or if the absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset threshold, the energy value of the first frequency band window and the energy value of the corresponding second frequency band window are summed to obtain the energy sum; the energy sum is used as the total energy value of the first frequency band window and the corresponding second frequency band window. Finally, the first frequency band window or the second frequency band window corresponding to the maximum energy sum among the multiple energy sums is used as the target frequency band window.

[0135] In some embodiments, the method further includes: the server determines the spectrum resolution according to the sampling rates corresponding to the first and second traveling wave signals and the data points of the first traveling wave signal; and determines the preset window size and step size according to the spectrum resolution.

[0136] In a specific implementation process, spectrum resolution refers to the minimum frequency interval that can distinguish two close frequency components, that is, the ability to distinguish two adjacent frequency components in the spectrum.

[0137] The spectral resolution is related to the ratio between the number of sampled data points and the sampling rate. The spectral resolution is usually given by the following formula:

[0138]

[0139] Where Δf represents the spectral resolution, f s Indicates the sampling rate (the number of points sampled per second), and N represents the sampling rate based on f sThe number of data points of the first traveling wave signal or the second traveling wave signal collected within the duration of the traveling wave signal. Therefore, the spectrum resolution is proportional to the sampling rate and inversely proportional to the number of sampled data points.

[0140] Therefore, the spectrum resolution is obtained by dividing the sampling rate by the number of data points of the first or second traveling wave signal. For example, if the sampling rate is 100 MHz, the number of data points of the first or second traveling wave signal is 100,000, and therefore, 100 MHz / 100,000 = 1 kHz is used as the spectrum resolution.

[0141] Optionally, after obtaining the first spectrum resolution by dividing the sampling rate by the number of data points of the first traveling wave signal or the second traveling wave signal, a preset multiple of the first spectrum resolution is taken as the final spectrum resolution. For example, 5 times the first spectrum resolution is taken as the final spectrum resolution.

[0142] The specific spectrum resolution can be set according to actual conditions.

[0143] Therefore, the spectrum resolution can be obtained by the sampling rate and length of the traveling wave signal, and the preset window size and step size can be determined to distinguish close frequency components and avoid frequency confusion. And optimizing the preset window size and step size can reduce spectrum leakage.

[0144] In some embodiments, the filtered first traveling wave signal includes a first first wave signal; the filtered second traveling wave signal includes a second first wave signal; after the server obtains the filtered first traveling wave signal and the filtered second traveling wave signal, the method further includes: the server calculates the time difference between the arrival time of the first first wave signal and the arrival time of the second first wave signal; the server calculates the fault point position of the cable to be detected based on the time difference and the transmission speed of the first traveling wave signal or the second traveling wave signal in the cable to be detected.

[0145] In the specific implementation process, if the collected first traveling wave signal and the second traveling wave signal are traveling wave signals generated when a fault occurs in the cable to be detected, the fault position of the cable to be detected can be located by the filtered first traveling wave signal and the filtered second traveling wave signal.

[0146] Due to signal attenuation, the time difference between the first wave signal of the filtered first traveling wave signal and the first wave signal of the filtered second traveling wave signal reaching their respective endpoints is usually selected for positioning analysis, and the position of the fault point in the cable to be detected is calculated based on the time difference and the propagation speed of the traveling wave signal in the cable to be detected. The calculation formula for the positioning position p is as follows:

[0147]

[0148] Among them, l is the length of the cable to be detected, p represents the location of the fault point, that is, the distance between the fault point and the main end of the cable to be detected, t1 represents the time when the first wave signal of the first traveling wave signal reaches the main end from the fault point p, t2 represents the time when the first wave signal of the second traveling wave signal reaches the slave end from the fault point p, and v represents the propagation speed of the traveling wave signal in the cable to be detected. For example, the traveling wave speed of the cable made of XLPE material is generally around 172m / us.

[0149] The embodiment of the present application determines the position of the fault point of the cable to be detected by using the filtered first traveling wave signal and the second traveling wave signal, thereby improving the accuracy of fault point positioning.

[0150] Figure 2 A schematic diagram of the structure of a signal filtering device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device includes: an acquisition module 201, a transformation module 202, a determination module 203, a generation module 204 and a filtering module 205; wherein,

[0151] An acquisition module 201 is used to acquire a first traveling wave signal and a second traveling wave signal respectively collected at both ends of the cable to be detected; a transformation module 202 is used to perform frequency domain transformation on the first traveling wave signal and the second traveling wave signal respectively to obtain first spectrum data corresponding to the first traveling wave signal and second spectrum data corresponding to the second traveling wave signal; a determination module 203 is used to determine the center frequency of the bandpass filter based on the first spectrum data and the second spectrum data; a generation module 204 is used to generate a filter coefficient according to the center frequency and a preset window function; a filtering module 205 is used to filter the first traveling wave signal and the second traveling wave signal respectively based on the filter coefficient to obtain a filtered first traveling wave signal and a filtered second traveling wave signal.

[0152] Based on the above embodiment, the determination module 203 is specifically used to: divide the first spectrum data into a number of first frequency band windows according to a preset window size and step size, and divide the second spectrum data into second frequency band windows with the same number as the first frequency band windows; the first frequency band window includes first sub-spectrum data intercepted from the first spectrum data; the second frequency band window includes second sub-spectrum data intercepted from the second spectrum data; calculate the energy value of each first frequency band window and the energy value of each second frequency band window respectively; determine the target frequency band window based on the energy value of each first frequency band window and the energy value of each second frequency band window; and use the center frequency of the target frequency band window as the center frequency of the bandpass filter.

[0153] Based on the above embodiment, the determination module 203 is specifically used to: use the first sub-spectrum data to calculate the energy spectrum of the corresponding first frequency band window, and use the second sub-spectrum data to calculate the energy spectrum of the corresponding second frequency band window; normalize the energy spectrum of each first frequency band window and the energy spectrum of each second frequency band window respectively to obtain the energy value of each first frequency band window and the energy value of each second frequency band window.

[0154] On the basis of the above embodiment, each first frequency band window corresponds to each second frequency band window one by one; the determination module 203 is specifically used to: calculate the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window to obtain multiple total energy values; and use the first frequency band window or the second frequency band window corresponding to the maximum total energy value among the multiple total energy values ​​as the target frequency band window.

[0155] On the basis of the above embodiment, the determination module 203 is specifically used for: if the relationship between the energy value of each first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, the first frequency band window is used as the first target frequency band window, and the corresponding second frequency band window is used as the second target frequency band window; accordingly, the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window is calculated, including: calculating the total energy value of the energy value of each first target frequency band window and the energy value of the corresponding second target frequency band window.

[0156] Based on the above embodiment, the determination module 203 is specifically used to: the ratio of the energy value of the first frequency band window to the energy value of the corresponding second frequency band window meets the preset ratio; or the absolute difference between the energy value of the first frequency band window and the energy value of the corresponding second frequency band window meets the preset threshold.

[0157] Based on the above embodiment, the determination module 203 is specifically used to: calculate the energy product of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window; or calculate the energy sum of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window.

[0158] Based on the above embodiment, the device also includes a window size and step size determination module, which is used to determine the spectrum resolution according to the sampling rate corresponding to the first traveling wave signal and the second traveling wave signal and the data points of the first traveling wave signal; and determine the preset window size and step size according to the spectrum resolution.

[0159] On the basis of the above embodiment, the generating module 204 is specifically used for: determining the unit impulse response of the bandpass filter according to the center frequency; and generating the filter coefficient based on the preset window function and the unit impulse response.

[0160] Based on the above embodiment, the filtered first traveling wave signal includes a first first wave signal; the filtered second traveling wave signal includes a second first wave signal; the device also includes a fault location module, which is used to calculate the time difference between the arrival time of the first first wave signal and the arrival time of the second first wave signal; and calculate the fault point position of the cable to be detected based on the time difference and the transmission speed of the first traveling wave signal or the second traveling wave signal in the cable to be detected.

[0161] It should be understood that the device corresponds to the above-mentioned signal filtering method embodiment and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be referred to in the above description. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the device.

[0162] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, the electronic device includes a processor 301 (processor), a memory 302 (memory) and a bus 303; wherein the processor 301 and the memory 302 communicate with each other through the bus 303. The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above-mentioned method embodiments.

[0163] The processor 301 can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 301 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0164] The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0165] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0166] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the methods provided by the above-mentioned method embodiments.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0168] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0170] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A signal filtering method, characterized in that: The method comprises: Acquire a first traveling wave signal and a second traveling wave signal respectively collected at two ends of the cable to be detected; Performing frequency domain transformation on the first traveling wave signal and the second traveling wave signal respectively to obtain first spectrum data corresponding to the first traveling wave signal and second spectrum data corresponding to the second traveling wave signal; determining a center frequency of a bandpass filter based on the first spectrum data and the second spectrum data; Generate a filter coefficient according to the center frequency and a preset window function; The first traveling wave signal and the second traveling wave signal are filtered based on the filtering coefficient to obtain a filtered first traveling wave signal and a filtered second traveling wave signal.

2. According to claim 1, it is characterized in that The determining the center frequency of the bandpass filter based on the first spectrum data and the second spectrum data comprises: According to a preset window size and step size, the first spectrum data is divided into a plurality of first frequency band windows, and the second spectrum data is divided into a plurality of second frequency band windows which are the same in number as the first frequency band windows; the first frequency band window includes first sub-spectrum data intercepted from the first spectrum data; and the second frequency band window includes second sub-spectrum data intercepted from the second spectrum data; respectively calculating an energy value of each of the first frequency band windows and an energy value of each of the second frequency band windows; determining a target frequency band window based on an energy value of each of the first frequency band windows and an energy value of each of the second frequency band windows; The center frequency of the target frequency band window is used as the center frequency of the bandpass filter.

3. The method according to claim 2, characterized in that The respectively calculating the energy value of each first frequency band window and the energy value of each second frequency band window comprises: Calculating the energy spectrum of the corresponding first frequency band window using the first sub-spectrum data, and calculating the energy spectrum of the corresponding second frequency band window using the second sub-spectrum data; Normalization processing is performed on the energy spectrum of each first frequency band window and the energy spectrum of each second frequency band window respectively to obtain an energy value of each first frequency band window and an energy value of each second frequency band window.

4. The method according to claim 2, characterized in that: Each of the first frequency band windows corresponds to each of the second frequency band windows in a one-to-one manner; and determining the target frequency band window based on the energy value of each of the first frequency band windows and the energy value of each of the second frequency band windows comprises: Calculate the total energy value of each energy value of the first frequency band window and the corresponding energy value of the second frequency band window to obtain a plurality of total energy values; The first frequency band window or the second frequency band window corresponding to the maximum total energy value among the multiple total energy values ​​is used as the target frequency band window.

5. The method according to claim 4, characterized in that Before calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window, the method further includes: If the relationship between the energy value of each first frequency band window and the energy value of the corresponding second frequency band window meets the preset relationship, the first frequency band window is used as the first target frequency band window, and the corresponding second frequency band window is used as the second target frequency band window; Correspondingly, the step of calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window includes: The total energy value of each of the energy values ​​of the first target frequency band windows and the energy value of the corresponding second target frequency band window is calculated.

6. The method according to claim 5, characterized in that The relationship between the energy value of each of the first frequency band windows and the energy value of the corresponding second frequency band window conforms to a preset relationship, including: The ratio of the energy value of the first frequency band window to the corresponding energy value of the second frequency band window meets a preset ratio; or, An absolute difference between an energy value of the first frequency band window and a corresponding energy value of the second frequency band window meets a preset threshold.

7. The method according to claim 4, characterized in that The calculating the total energy value of the energy value of each first frequency band window and the energy value of the corresponding second frequency band window comprises: Calculating an energy product of an energy value of each first frequency band window and an energy value of a corresponding second frequency band window; or, An energy sum of the energy value of each first frequency band window and the corresponding energy value of the second frequency band window is calculated.

8. The method according to claim 2, characterized in that: The method further comprises: Determine the spectrum resolution according to the sampling rates corresponding to the first traveling wave signal and the second traveling wave signal and the data points of the first traveling wave signal; The preset window size and step size are determined according to the spectrum resolution.

9. The method according to claim 1, characterized in that: The generating of the filter coefficient according to the center frequency and the preset window function comprises: Determine a unit impulse response of the bandpass filter according to the center frequency; A filter coefficient is generated based on the preset window function and the unit impulse response.

10. The method according to any one of claims 1 to 9, characterized in that: The filtered first traveling wave signal includes a first head wave signal; the filtered second traveling wave signal includes a second head wave signal; after obtaining the filtered first traveling wave signal and the filtered second traveling wave signal, the method further includes: Calculating a time difference between an arrival time of the first first wave signal and an arrival time of the second first wave signal; The fault point position of the cable to be detected is calculated based on the time difference and the transmission speed of the first traveling wave signal or the second traveling wave signal in the cable to be detected.

11. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method according to any one of claims 1 to 10 is performed.

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