Fault traveling wave signal feature extraction method, system, device and medium

Abstract

The invention provides a fault traveling wave signal feature extraction method, system and device and a medium, and the method comprises the steps: obtaining a line mode component of a fault traveling wave signal in a distribution line; performing variational mode decomposition on the linear mode component to obtain a plurality of mode components; performing Hilbert transform and frequency domain feature extraction on each modal component in the plurality of modal components in sequence to obtain an amplitude feature of the fault traveling wave signal; and performing Wigner-Ville distribution calculation and windowing smoothing processing on each mode component in the plurality of mode components in sequence to obtain a time-frequency characteristic and an energy characteristic of the fault traveling wave signal. According to the invention, accurate extraction of fault traveling wave waveform features can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular to a fault traveling wave signal feature extraction method, system, device and medium. BACKGROUND

[0002] Most distribution lines use overhead line-cable hybrid distribution lines, and the key of the traveling wave fault location technology of distribution network lines is usually achieved by extracting the waveform transient characteristics of the traveling wave. Most of the existing traveling wave detection methods only identify and extract the fault traveling wave head, while the fault traveling wave is a full time-frequency signal, and only extracting the head information will miss a large amount of time-frequency domain fault characteristics, thereby easily leading to low reliability of the existing traveling wave protection and fault location method. SUMMARY

[0003] In order to solve the problems of the prior art, the present application provides a fault traveling wave signal feature extraction method, system, device and medium, which aims to accurately extract the fault traveling wave waveform characteristics.

[0004] The purpose of the present application is achieved by using the following technical scheme: On the one hand, the present application provides a fault traveling wave signal feature extraction method, which comprises: obtaining a line mode component of a fault traveling wave signal in a distribution line; performing variational mode decomposition on the line mode component to obtain a plurality of modal components; performing Hilbert transform and frequency domain feature extraction on each modal component in the plurality of modal components in turn to obtain amplitude features of the fault traveling wave signal; performing Wigner-Ville distribution calculation and window smoothing processing on each modal component in the plurality of modal components in turn to obtain time-frequency features and energy features of the fault traveling wave signal.

[0005] Optionally, the variational mode decomposition on the line mode component to obtain a plurality of modal components comprises: performing phase-mode transformation on the line mode component to obtain a line mode signal; performing variational mode decomposition on the line mode signal to obtain the plurality of modal components.

[0006] Optionally, the Hilbert transform and frequency domain feature extraction on each modal component in the plurality of modal components in turn to obtain the amplitude features of the fault traveling wave signal comprises: performing Hilbert transform on each modal component to obtain the Hilbert transformed signal of each modal component, and correspondingly combining each modal component and the Hilbert transformed signal of each modal component to construct an analytical signal of each modal component. analyzing the instantaneous frequency of each modal component and the analytic signal of each modal component to obtain a boundary spectrum frequency domain feature of each modal component; combining and superimposing the boundary spectrum frequency domain features of each modal component to obtain an amplitude feature of the fault traveling wave signal.

[0007] Optionally, the analyzing the instantaneous frequency of each modal component and the analytic signal of each modal component to obtain a boundary spectrum frequency domain feature of each modal component comprises: using Hilbert-Huang transform to analyze and derive the analytic signal of each modal component and the instantaneous frequency of each modal component to obtain a Hilbert spectrum of each modal component; combining and superimposing the Hilbert spectrum of each modal component in the time domain to obtain the boundary spectrum frequency domain feature of each modal component.

[0008] Optionally, the sequentially performing Wigner-Ville distribution calculation and window smoothing processing on each modal component in the plurality of modal components to obtain a time-frequency feature and an energy feature of the fault traveling wave signal comprises: sequentially performing Wigner-Ville distribution calculation and window smoothing processing on each modal component to obtain a smoothed pseudo Wigner-Ville distribution signal of each modal component; performing boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the frequency domain to obtain an instantaneous energy of each modal component, and performing boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the time domain to obtain an energy spectrum density of each modal component; combining and superimposing the instantaneous energy of each modal component to obtain the time-frequency feature of the fault traveling wave signal, and combining and superimposing the energy spectrum density of each modal component to obtain the energy feature of the fault traveling wave signal.

[0009] Optionally, the sequentially performing Wigner-Ville distribution calculation and window smoothing processing on each modal component to obtain a smoothed pseudo Wigner-Ville distribution signal of each modal component comprises: using the Wigner-Ville distribution to analyze each modal component to obtain a time-frequency plane distribution signal of each modal component; using a smoothing window function to perform phased sliding window on the time-frequency plane distribution signal of each modal component to obtain the smoothed pseudo Wigner-Ville distribution signal of each modal component.

[0010] Optionally, the boundary analysis is performed on the smoothed pseudo-Villiger-Wigner distribution signal in the frequency domain to obtain the instantaneous energy of each modal component, and the boundary analysis is performed on the smoothed pseudo-Villiger-Wigner distribution signal in the time domain to obtain the energy spectral density of each modal component, comprising: performing the boundary analysis on the smoothed pseudo-Villiger-Wigner distribution signal in the frequency domain to obtain the time boundary function of each modal component; performing the boundary analysis on the smoothed pseudo-Villiger-Wigner distribution signal in the time domain to obtain the frequency domain boundary function of each modal component; determining the time boundary function of each modal component as the instantaneous energy of each modal component, and determining the frequency domain boundary function of each modal component as the energy spectral density of each modal component.

[0011] In another aspect, the embodiment of the present application also provides a feature extraction system of a fault traveling wave signal, comprising: an acquisition module configured to acquire a line-mode component of a fault traveling wave signal in a power distribution line; a decomposition module configured to perform variational modal decomposition on the line-mode component to obtain a plurality of modal components; a first processing module configured to sequentially perform Hilbert transform and frequency domain feature extraction on each modal component in the plurality of modal components to obtain an amplitude feature of the fault traveling wave signal; a second processing module configured to sequentially perform Wigner-Villiger distribution calculation and window smoothing processing on each modal component in the plurality of modal components to obtain a time-frequency feature and an energy feature of the fault traveling wave signal.

[0012] Optionally, the decomposition module is specifically configured to perform phase-mode transformation on the line-mode component to obtain a line-mode signal, and perform variational modal decomposition on the line-mode signal to obtain the plurality of modal components.

[0013] Optionally, the first processing module comprises: a first processing unit configured to perform Hilbert transform on each modal component to obtain a Hilbert transformed signal of each modal component, and correspondingly combine each modal component and the Hilbert transformed signal of each modal component to construct an analytic signal of each modal component; a second processing unit configured to analyze the instantaneous frequency of each modal component and the analytic signal of each modal component to obtain a boundary spectrum frequency domain feature of each modal component; and a third processing unit configured to combine and superimpose the boundary spectrum frequency domain feature of each modal component to obtain the amplitude feature of the fault traveling wave signal.

[0014] Optionally, the second processing unit is specifically configured to analyze and derive the analytic signal of each modal component and the instantaneous frequency of each modal component by using a Hilbert-Huang transform to obtain a Hilbert spectrum of each modal component; and combine and superimpose the Hilbert spectrum of each modal component in the time domain to obtain a boundary spectrum frequency domain feature of each modal component.

[0015] Optionally, the second processing module comprises: a fourth processing unit configured to sequentially perform Wigner-Ville distribution calculation and window smoothing processing on each modal component to obtain a smoothed pseudo Wigner-Ville distribution signal of each modal component; a fifth processing unit configured to perform boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the frequency domain to obtain an instantaneous energy of each modal component, and perform boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the time domain to obtain an energy spectral density of each modal component; and a sixth processing unit configured to combine and superimpose the instantaneous energy of each modal component to obtain a time-frequency feature of the fault traveling wave signal, and combine and superimpose the energy spectral density of each modal component to obtain an energy feature of the fault traveling wave signal.

[0016] Optionally, the fourth processing unit is specifically configured to analyze each modal component by using the Wigner-Ville distribution to obtain a time-frequency plane distribution signal of each modal component; and perform phased sliding window on the time-frequency plane distribution signal of each modal component by using a smoothing window function to obtain a smoothed pseudo Wigner-Ville distribution signal of each modal component.

[0017] Optionally, the fifth processing unit is specifically configured to perform boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the frequency domain to obtain a time boundary function of each modal component; perform boundary analysis on the smoothed pseudo Wigner-Ville distribution signal in the time domain to obtain a frequency domain boundary function of each modal component; determine the time boundary function of each modal component as an instantaneous energy of each modal component; and determine the frequency domain boundary function of each modal component as an energy spectral density of each modal component.

[0018] In still another aspect, an electronic device is also provided, comprising: at least one processor and a memory; the memory and the processor are connected through a bus; the memory is configured to store one or more programs; when the one or more programs are executed by the at least one processor, the above-mentioned feature extraction method of the fault traveling wave signal is implemented.

[0019] In still another aspect, the embodiment of the present application further provides a readable storage medium, which has an execution program stored thereon, and the execution program, when executed, implements the feature extraction method of the fault traveling wave signal.

[0020] Compared with the prior art, the present application has the following advantages: The embodiment of the present application provides a feature extraction method, system, device and medium for a fault traveling wave signal, which performs Hilbert transform and frequency domain feature extraction on a plurality of modal components obtained by performing variational modal decomposition on the fault traveling wave signal to obtain amplitude features, performs Wigner-Ville distribution calculation and window smoothing processing to obtain time-frequency features and energy features. The present application can adaptively realize frequency domain profiling and effective separation of each component of the fault traveling wave signal by using variational modal decomposition, can process the full time-frequency signal of the fault traveling wave signal, and can extract complete fault features of the full time-frequency signal by subsequently performing Hilbert transform and frequency domain feature extraction, Wigner-Ville distribution calculation and window smoothing processing on each modal component in the plurality of modal components, rather than only extracting wave head information as in the related art. Therefore, the reliability of the traveling wave protection and fault location method can be improved.

[0021] The present application can also realize effective signal extraction and noise reduction by performing Hilbert transform and frequency domain feature extraction on each modal component, so as to extract more accurate amplitude features of the fault traveling wave signal; at the same time, the present application can eliminate cross-term interference to a certain extent while maintaining high time-frequency aggregation by performing Wigner-Ville distribution calculation and window smoothing processing on each modal component, so as to finely extract the time-frequency features and energy features of the fault traveling wave signal. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings. Figure 1 A flowchart of a feature extraction method for a fault traveling wave signal provided by the embodiment of the present application; Figure 2 An implementation step flowchart corresponding to the feature extraction method for a fault traveling wave signal provided by the embodiment of the present application; Figure 3 An amplitude diagram in feature extraction of a fault traveling wave sample signal a by the feature extraction method for a fault traveling wave signal provided by the embodiment of the present application; Figure 4The amplitude schematic diagram in the feature extraction of the fault traveling wave sample signal b by the feature extraction method of the fault traveling wave signal provided by the embodiment of the application is as shown in the figure; Figure 5 The frequency schematic diagram in the feature extraction of the fault traveling wave sample signal a by the feature extraction method of the fault traveling wave signal provided by the embodiment of the application is as shown in the figure; Figure 6 The frequency schematic diagram in the feature extraction of the fault traveling wave sample signal b by the feature extraction method of the fault traveling wave signal provided by the embodiment of the application is as shown in the figure; Figure 7 The component schematic diagram of the feature extraction system of the fault traveling wave signal provided by the embodiment of the application is as shown in the figure; Figure 8 The component schematic diagram of the electronic device provided by the embodiment of the application is as shown in the figure. DETAILED DESCRIPTION

[0023] The other advantages and effects of the present application can be easily understood by those skilled in the art from the content disclosed in the specification. The present application can also be implemented or applied by means of other different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for the purpose of illustrating the present application, but not for limiting the protection scope of the present application.

[0024] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict.

[0025] In the following description, the terms "first\second\third" are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the present application belong. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the embodiments of the present application.

[0027] Embodiment 1: The embodiments of the present application provide a feature extraction method of a fault traveling wave signal, as shown in the figure, comprising: Figure 1 ​Step 101, obtaining a line mode component of a fault traveling wave signal in a power distribution line.

[0028] In some embodiments of the present application, the power distribution line refers to a power line that is responsible for the task of distributing electric energy in the power grid. The fault traveling wave signal in the power distribution line can be a voltage or current transient traveling wave signal generated when the power distribution line fails or the equipment in the power distribution line fails.

[0029] It should be noted that in the field of power transmission, the traveling wave signal includes a line mode component and a zero mode component, wherein the line mode component and the zero mode component represent the symmetric component and the asymmetric component in the current or voltage, respectively. Specifically, the line mode component represents the part with the same amplitude and phase relationship in the three-phase current or voltage, while the zero mode component represents the average value or direct current component in the three-phase, showing asymmetry.

[0030] In some embodiments of the present application, the modal component of the fault traveling wave signal in the power distribution line can also be divided into a line mode component and a zero mode component; wherein the wave speed of the line mode component is larger, the wave impedance is smaller, the transmission is stable, and the influence of external factors is smaller, while the wave speed of the zero mode component is smaller, the wave impedance is larger, and only when the ground fault occurs. In addition, the influence of frequency change on the wave speed of the line mode component is much smaller than its influence on the zero mode component, so in the present application, the line mode component can be selected for fault location to extract fault feature information, so as to realize the subsequent detection and feature extraction of the fault traveling wave signal.

[0031] In some embodiments of the present application, the fault traveling wave signal in the power distribution line is a full time-frequency signal. Correspondingly, the line mode component of the fault traveling wave signal in the power distribution line is a full time-frequency signal.

[0032] Step 102, performing variational modal decomposition on the line mode component to obtain a plurality of modal components.

[0033] In some embodiments of the present application, the process of performing variational modal decomposition on the line mode component can include the process of phase-mode transformation and variational modal decomposition. The line mode component of the extracted fault traveling wave signal can be first subjected to phase-mode transformation to obtain a line mode signal ; then, the line mode signal is decomposed into K modal components by using a variational modal decomposition (VMD) algorithm.

[0034] That is, the above step 102 can be realized by the following step 1021 and step 1022 (not shown in the figure): Figure 1 ​Step 1021, performing a phase-mode transformation on the line-mode component to obtain a line-mode signal.

[0035] In some embodiments of the present application, the phase-mode transformation is a mathematical transformation method commonly used in power systems, which can realize the transformation of the line-mode component into a line-mode signal through coordinate transformation.

[0036] Step 1022, performing variational modal decomposition on the line-mode signal to obtain a plurality of modal components.

[0037] In some embodiments of the present application, the line-mode signal obtained is decomposed into a plurality of modal components through variational modal decomposition.

[0038] Here, the line-mode signal can be decomposed into a plurality of modal components by using formula (1) . That is: Formula (1); Wherein, .

[0039] In this way, by performing the phase-mode transformation and the variational modal decomposition on the line-mode component, not only can the frequency domain profiling and the effective separation of each component of the fault traveling wave signal be realized adaptively, but also parameter support can be provided for subsequent feature extraction.

[0040] Step 103, sequentially performing Hilbert transformation and frequency domain feature extraction on each modal component in the plurality of modal components to obtain an amplitude feature of the fault traveling wave signal.

[0041] In some embodiments of the present application, Hilbert transformation can be performed on each modal component sequentially, and then the frequency domain feature of the modal component after the Hilbert transformation is extracted to obtain the amplitude feature corresponding to the fault traveling wave signal.

[0042] It should be noted that the Hilbert transformation is a linear operator that generates a function with the same domain as the function.

[0043] In some embodiments of the present application, the amplitude feature of the fault traveling wave signal can refer to the maximum amplitude or intensity feature of the fault traveling wave signal in the time domain.

[0044] In some embodiments of the present application, the above step 103 can be realized by the following steps 1031 to 1033 (not shown in the figure): Figure 1 ​Step 1031, performing Hilbert transform on the modal components to obtain Hilbert transformed signals of the modal components, and combining the modal components and the Hilbert transformed signals of the modal components correspondingly to construct analytic signals of the modal components.

[0045] In some embodiments of the present application, the modal components can be Hilbert transformed by using formula (2) to obtain Hilbert transformed signals of the modal components .

[0046] Formula (2); wherein, is a Cauchy principal value, is a time window translation factor, is a time variable.

[0047] In some embodiments of the present application, the modal components and the corresponding Hilbert transformed signals of the modal components can be constructed according to formula (3) to obtain analytic signals of the modal components. Here, the analytic signals of the modal components can be composed of two parts of real and imaginary parts.

[0048] Formula (3); wherein, is an amplitude representation function, is a phase representation function, that is, a representation of imaginary part.

[0049] In some embodiments of the present application, the in formula (3) above can be represented by formula (4), and the in formula (3) above can be represented by formula (5): Formula (4); Formula (5). Formula (5).

[0050] Step 1032, performing analysis on the instantaneous frequencies of the modal components and the analytic signals of the modal components to obtain boundary spectral frequency domain features of the modal components.

[0051] In some embodiments of the present application, the instantaneous frequencies of the modal components can be represented by using , and , which represents the modal components ​​​​​​​​The amplitude varies with time and frequency across the entire frequency range.

[0052] Correspondingly, step 1032 above can be achieved through the following process: The first step is to use the Hilbert-Huang transform to analyze and derive the analytic signals and instantaneous frequencies of each modal component, thereby obtaining the Hilbert spectrum of each modal component.

[0053] In some embodiments of the present invention, the Hilbert-Huang transform is a time-frequency analysis method for processing nonlinear and non-stationary signals. It mainly involves first performing Empirical Mode Decomposition (EMD) on the original signal to obtain several essential mode functions, and then using the Hilbert transform to further analyze and process the signal.

[0054] Here, the Hilbert-Huang transform is used, and formula (6) can be used to convert each modal component. Analyzed signal In and its instantaneous frequency Analysis and derivation are performed to obtain the modal components. Hilbert score .

[0055] Formula (6); in, The parentheses indicate that the real part is taken, and formula (6) statistically represents the cumulative amplitude at each frequency point.

[0056] The second step is to combine and superimpose the Hilbert spectra of each modal component in the time domain to obtain the boundary spectral frequency domain features of each modal component.

[0057] In some embodiments of the present invention, formula (7) can be used to achieve the processing of each modal component. Hilbert score The modal components are obtained by combining and superimposing (i.e., accumulating) the components in the time domain. Boundary spectral frequency domain features .

[0058] Formula (7).

[0059] In this way, the Hilbert-Huang transform is first used to analyze and derive the analytic signals and instantaneous frequencies of each modal component to obtain the Hilbert spectrum of each modal component. Then, in the time domain, the Hilbert spectra of each modal component are combined and superimposed to quickly obtain the boundary spectral frequency domain characteristics of each modal component with high accuracy.

[0060] Step 1033, combining and superimposing the boundary spectrum frequency domain features of the modal components to obtain the amplitude feature of the fault traveling wave signal.

[0061] In some embodiments of the present application, the boundary spectrum frequency domain features of the modal components are directly combined and superimposed to obtain the amplitude feature of the fault traveling wave signal. In some embodiments of the present application, the boundary spectrum frequency domain features of the modal components are directly combined and superimposed to obtain the amplitude feature of the fault traveling wave signal.

[0062] In this way, the analytic signals of the modal components are sequentially constructed by using the Hilbert transform, the boundary spectrum frequency domain features of the modal components are obtained by means of the analytic signals, and the amplitude feature of the fault traveling wave signal is obtained based on the boundary spectrum frequency domain features of the modal components. In this way, effective signal extraction and noise reduction can be realized, and the relatively accurate amplitude feature can be extracted.

[0063] Step 104, sequentially performing Wigner-Ville distribution calculation and window smoothing processing on each of the modal components in the plurality of modal components to obtain the time-frequency feature and the energy feature of the fault traveling wave signal.

[0064] Here, the Wigner-Ville distribution (WVD) is a very important one in nonlinear time-frequency representation, and has good time and frequency resolution.

[0065] In some embodiments of the present application, the WVD analysis, i.e., WVD calculation, can be performed on each of the modal components first, and the smoothing processing is performed on the same to obtain the smoothed pseudo Wigner-Ville distribution of each of the modal components. In some embodiments of the present application, the WVD analysis, i.e., WVD calculation, can be performed on each of the modal components first, and the smoothing processing is performed on the same to obtain the smoothed pseudo Wigner-Ville distribution of each of the modal components. In some embodiments of the present application, the WVD analysis, i.e., WVD calculation, can be performed on each of the modal components first, and the smoothing processing is performed on the same to obtain the smoothed pseudo Wigner-Ville distribution of each of the modal components. In some embodiments of the present application, the WVD analysis, i.e., WVD calculation, can be performed on each of the modal components first, and the smoothing processing is performed on the same to obtain the smoothed pseudo Wigner-Ville distribution of each of the modal components.

[0066] ​​​​​In some embodiments of the present application, the time-frequency feature of the fault traveling wave signal can be used to refer to the energy distribution of the fault traveling wave signal at different times and frequencies, and the instantaneous frequency and its amplitude of the fault traveling wave signal at different time points can be further observed through the time-frequency distribution, so that the time-frequency filtering and time-varying signal research of the fault traveling wave signal can be carried out. Meanwhile, the energy feature of the fault traveling wave signal can include the energy signal and power signal of the fault traveling wave signal and other information.

[0067] In some embodiments of the present application, WVD is also a time-frequency analysis method widely used for non-stationary signal feature extraction, which can reflect the time-frequency representation of signal energy density and is a variable of frequency and time t.

[0068] In some embodiments of the present application, the above step 104 can be realized by the following steps 1041 to 1043 (not shown in the figure): Figure 1 Step 1041, the Wigner-Ville distribution calculation and window smoothing processing are sequentially carried out on the modal components to obtain the smoothed pseudo Wigner-Ville distribution signals of the modal components.

[0069] In some embodiments of the present application, the WVD analysis is carried out on each modal component , and then the smoothing processing is carried out to obtain the smoothed pseudo Wigner-Ville distribution of each modal component in the time-frequency domain.

[0070] In other words, the above step 1041 can be realized by the following process: First, the Wigner-Ville distribution is used to analyze the modal components to obtain the time-frequency plane distribution signals of the modal components.

[0071] In some embodiments of the present application, after obtaining k modal components, the WVD distribution calculation can be carried out on each modal component in the k modal components; wherein the WVD is the Fourier transform of the signal center covariance function, which does not lose the amplitude and phase information, and has good resolution for instantaneous frequency and group delay. The Wigner-Ville distribution calculation can be carried out on each modal component by using formula (8) to obtain the time-frequency plane distribution signals of each modal component .

[0072] Formula (8); Wherein, is a time delay variable, is a time variable, is a frequency variable, represents​​ The complex conjugate, In order to be in The modal components corresponding to time 1. In order to be in The modal components corresponding to the time step.

[0073] The second step involves applying a smooth window function to the time-frequency plane distribution signal of each modal component in stages to obtain the smoothed pseudo-Wigner-Willi distribution signal of each modal component.

[0074] In some embodiments of the present invention, in order to suppress each modal component The time-frequency plane distribution signal, when analyzing the influence of the cross terms of multiple components, can further refine the above formula (8) in the above formula. and Add time delay windows respectively and temporal smoothing window That is, the modal components are obtained using formula (9). The function smooths the pseudo-Wigner-Ville distribution, i.e., smooths the pseudo-Wigner-Ville distribution signal. (Smooth Pseudo Wigner-Ville Distribution, SPWD).

[0075] Formula (9); here, and It is a symmetrical signal and has .

[0076] In this way, after obtaining the time-frequency plane distribution signals of each modal component using the Wigner-Willi distribution, a smooth window function can be used to perform staged sliding windowing on the time-frequency plane distribution signals of each modal component, thereby obtaining the smooth pseudo-Wigner-Willi distribution signals of each modal component, which can provide parameter support for subsequent acquisition of the instantaneous energy and energy spectral density of each modal component.

[0077] Step 1042: Perform boundary analysis on the smoothed pseudo-Wigner-Willi distribution signal in the frequency domain to obtain the instantaneous energy of each modal component, and perform boundary analysis on the smoothed pseudo-Wigner-Willi distribution signal in the time domain to obtain the energy spectral density of each modal component.

[0078] In some embodiments of the present invention, each modal component The instantaneous energy can refer to each modal component. The energy possessed within a certain time period or specific point in time; correspondingly, each modal component. The energy spectral density refers to the energy density of each modal component. Signal energy in a unit frequency band.

[0079] It should be noted that the boundary analysis generally refers to testing the boundary values of the input or output, etc.

[0080] In some embodiments of the present application, the modal components The smoothed pseudo Wigner-Ville distribution signal in the time-frequency domain is analyzed in the frequency domain to obtain the time boundary function of each modal component The smoothed pseudo Wigner-Ville distribution signal in the time-frequency domain is analyzed in the time domain to obtain the frequency domain boundary function of each modal component The smoothed pseudo Wigner-Ville distribution signal in the time-frequency domain is analyzed in the time domain to obtain the frequency domain boundary function of each modal component The time boundary function of each modal component The instantaneous energy of each modal component The frequency domain boundary function of each modal component The energy spectral density of each modal component

[0081] In other words, the above step 1042 can be implemented by the following process: First, the smoothed pseudo Wigner-Ville distribution signal is analyzed in the frequency domain to obtain the time boundary function of each modal component.

[0082] Second, the smoothed pseudo Wigner-Ville distribution signal is analyzed in the time domain to obtain the frequency domain boundary function of each modal component.

[0083] In some embodiments of the present application, the smoothed pseudo Wigner-Ville distribution signal in the time-frequency domain of each modal component The smoothed pseudo Wigner-Ville distribution signal in the time-frequency domain of each modal component The time boundary function of each modal component The frequency domain boundary function of each modal component The frequency domain boundary function of each modal component The frequency domain boundary function of each modal component The frequency domain boundary function of each modal component The frequency domain boundary function of each modal component .

[0084] Equation (10); Equation (11).

[0085] Third, the time boundary function of each modal component is determined as the instantaneous energy of each modal component, and the frequency domain boundary function of each modal component is determined as the energy spectral density of each modal component.

[0086] In some embodiments of the present application, the time boundary function may further represent the instantaneous energy of the signal, the frequency domain boundary function may further represent the energy spectral density of the signal.

[0087] In this way, the instantaneous energy and the energy spectral density of each modal component are respectively analyzed in the time domain and the frequency domain of the smoothed pseudo-Vigener-Wille distribution signal, and are represented by the time boundary function and the frequency domain boundary function using each modal component. On the basis of being able to finely extract the change characteristics of the fault traveling wave signal in the time domain and the frequency domain, the time-frequency characteristics and the energy characteristics of the fault traveling wave waveform can be accurately extracted.

[0088] Step 1043, combining and superimposing the instantaneous energy of each modal component to obtain the time-frequency characteristics of the fault traveling wave signal, and combining and superimposing the energy spectral density of each modal component to obtain the energy characteristics of the fault traveling wave signal.

[0089] In some embodiments of the present application, the instantaneous energy of each modal component is directly combined and superimposed, so as to obtain the time-frequency characteristics of the fault traveling wave signal, and the energy spectral density of each modal component is combined and superimposed, so as to obtain the energy characteristics of the fault traveling wave signal.

[0090] In this way, the present application sequentially performs Wigner-Ville distribution calculation, window smoothing processing, and boundary analysis in the time domain and the frequency domain respectively, to obtain the corresponding instantaneous (transient) energy and energy spectrum. To a certain extent, the cross-term interference can be eliminated, while the high time-frequency aggregation is maintained, so as to truly and accurately reflect the time-frequency characteristics and the energy characteristics of the traveling wave full waveform.

[0091] With reference to the above description, as Figure 2 shown, an implementation step flow diagram of a feature extraction method of a fault traveling wave signal provided by an embodiment of the present application is provided; wherein the specific implementation step flow is as follows: 201. Collecting a fault traveling wave signal.

[0092] 202. Performing phase-mode transformation on the signal to extract a line-mode signal. Here, the fault traveling wave signal collected in 201 is subjected to phase-mode transformation to obtain a line-mode signal.

[0093] 203. Discretely calculating (sampling calculation) the line-mode signal, that is, performing variational modal decomposition on the line-mode signal obtained in 202 to obtain a plurality of modal components (204). 、 、 、… ​

[0094] 205. First, the Hilbert transform can be performed on each of the obtained plurality of modal components; then, the modal components after the Hilbert transform are subjected to boundary spectrum frequency domain feature extraction to obtain the boundary spectrum frequency domain features of each modal component; finally, the boundary spectrum frequency domain features of each modal component can be combined and superimposed, and the amplitude feature of the measured signal can be obtained, i.e., the amplitude feature of the fault traveling wave signal collected in 201.

[0095] 206. First, the Wigner-Ville distribution calculation can be performed on each of the obtained plurality of modal components to obtain the time-frequency plane distribution signals of the modal components; then, the time-frequency plane distribution signals of the modal components are subjected to windowing and smoothing, i.e., phased sliding windowing, to obtain the smoothed pseudo Wigner-Ville distribution signals of the modal components; finally, the smoothed pseudo Wigner-Ville distribution signals of the modal components can be subjected to boundary analysis in the time domain and the frequency domain, respectively, to obtain the instantaneous energy and energy spectral density of each modal component, and the instantaneous energy and energy spectral density of each modal component can be combined and superimposed to obtain the time-frequency feature and energy feature of the fault traveling wave signal collected in 201.

[0096] In addition, those skilled in the art should know that in the field of fault traveling wave signal processing, due to the time-varying frequency characteristics of the fault traveling wave signal, in order to capture this time-varying characteristic, time-frequency analysis needs to be performed on the fault traveling wave signal, and the time-frequency analysis method can include wavelet transform, Hilbert transform, Hilbert-Huang transform, etc.; wherein: 1) Wavelet transform: it plays an important role in fault traveling wave local signal processing and can be used to analyze fault traveling wave local time-frequency domain features, and thus is widely used in fault traveling wave positioning. Among them, wavelet theory plays an important role in signal denoising, which uses wavelet to decompose and reconstruct the signal according to the concept of multi-resolution analysis, to achieve the effect of local detail processing, but cannot depict the local characteristics of the signal in the time domain, thus the effect on sudden signal and non-stationary signal is not good. Further, without time-frequency analysis, the wavelet has a limited duration and sudden frequency and amplitude.

[0097] 2) S transform (Stockwell Transform): a time-frequency transform technique, mainly used in signal processing field, has great advantages in analyzing non-stationary signals. It can provide good time and frequency resolution, and can apply different resolutions at different frequencies, which makes it more effective in processing localized signal features. The phase spectrum of S transform in time-frequency representation is directly related to the original signal, which enables it to extract more feature quantities in signal analysis and is not sensitive to noise.

[0098] 3) Hilbert-Huang Transform (HHT): suitable for processing nonlinear and non-stationary signals, can effectively decompose the signal into multiple intrinsic mode functions (IMF), each IMF is effective and can better reflect the local characteristics of the signal; thus, more accurate instantaneous frequency information can be provided.

[0099] However, the above time-frequency analysis methods have the following defects, wherein: 1) The wavelet transform has high algorithm complexity and lacks adaptability. The wavelet transform is sensitive to high-frequency components in the signal, which will lead to unpredictable problems in signal processing. The wavelet transform requires the sampling rate to be a multiple of 2, which limits its application in signal processing, and invalid components are decomposed and signal aliasing occurs.

[0100] 2) The window function of S transform changes with frequency, but once the basic wavelet is determined, it cannot be changed, and the adaptability in the signal processing process is limited. Although the mathematical model of S transform is relatively simple, its computational complexity is very high, especially when processing large-scale data.

[0101] 3) The process of HHT is relatively complex, and in empirical mode decomposition, multiple iterations and screening are required, which will lead to long calculation time and may cause error accumulation, affecting the final analysis result. Moreover, mode aliasing problem occurs in the process of empirical mode decomposition, which will affect the accuracy.

[0102] Therefore, the embodiment of the present application provides a traveling wave waveform extraction method based on adaptive VMD-SPWD, which can finely extract the change characteristics of the fault traveling wave full waveform in the time domain and the frequency domain within a fixed time window, thereby realizing accurate extraction of the fault traveling wave waveform. VMD is a non-recursive, variational mode decomposition estimation method, which determines the frequency center and bandwidth of each component by iteratively searching for the optimal solution of the variational model, and can adaptively realize frequency domain partitioning of the signal and effective separation of each component. And SPWD is a smoothing process of WVD, which usually uses a smoothing window function-Hamming window function to convolve WVD, thereby obtaining a smoothed pseudo Wigner-Ville distribution. This processing method can eliminate cross-term interference to a certain extent while maintaining high time-frequency concentration, has good noise suppression effect, high time-frequency resolution and concentration, highlights fault information, and truly and accurately reflects the time-frequency characteristics of the traveling wave full waveform.

[0103] In this way, reference can be made to Figures 3 to 6As shown, respectively, are fault traveling wave sample signal a and fault traveling wave sample signal b, using the fault traveling wave signal feature extraction method provided by the embodiment of the present application to extract amplitude features and frequency domain features. At the same time, from Figures 3 to 6 In the corresponding simulation results, it can be seen that in the fault traveling wave sample signal a and the fault traveling wave sample signal b, there are clear boundary components at certain positions, and there are no cross interference terms. The simulation results show that the fault traveling wave signal feature extraction method provided by the present method can improve the time-frequency concentration of the fault traveling wave signal to a certain extent. Not only is the time-frequency concentration the best, but the noise suppression effect is also obvious, and the fault traveling wave features can be effectively extracted.

[0104] The fault traveling wave signal feature extraction method provided by the embodiment of the present application can obtain multiple modal components by performing variational modal decomposition on the fault traveling wave signal, perform Hilbert transform and frequency domain feature extraction on each modal component, obtain amplitude features, perform Wigner-Ville distribution calculation and window smoothing processing, and obtain time-frequency features and energy features. The present application can adaptively realize frequency domain partitioning of the fault traveling wave signal and effective separation of each component by using variational modal decomposition, can process the full time-frequency signal of the fault traveling wave signal, and can extract complete fault features of the full time-frequency signal by subsequently performing Hilbert transform and frequency domain feature extraction, Wigner-Ville distribution calculation and window smoothing processing on each modal component in the multiple modal components, rather than only extracting wave head information as in the related art. Therefore, the reliability of the traveling wave protection and fault location method can be improved.

[0105] In addition, the present application can also realize effective signal extraction and noise reduction by performing Hilbert transform and frequency domain feature extraction on each modal component, so as to extract more accurate amplitude features of the fault traveling wave signal; at the same time, the present application can also eliminate cross term interference to a certain extent while maintaining high time-frequency concentration by performing Wigner-Ville distribution calculation and window smoothing processing on each modal component, so as to finely extract time-frequency features and energy features of the fault traveling wave signal.

[0106] Embodiment 2: Based on the same inventive concept, the embodiment of the present application also provides a fault traveling wave signal feature extraction system, as shown in Figure 7 As shown, the system 700 provided by the embodiment of the present application includes: The acquisition module 701 is configured to acquire a line-mode component of a fault traveling wave signal in a power distribution line. The decomposition module 702 is configured to perform variational modal decomposition on the line-mode component to obtain multiple modal components. The first processing module 703 is configured to sequentially perform Hilbert transform and frequency domain feature extraction on each modal component in the plurality of modal components to obtain the amplitude feature of the fault traveling wave signal. The second processing module 704 is configured to sequentially perform Wigner-Ville distribution calculation and window smoothing processing on each modal component in the plurality of modal components to obtain the time-frequency feature and the energy feature of the fault traveling wave signal.

[0107] Optionally, the decomposition module 702 is specifically configured to perform phase-mode transformation on the line-mode component to obtain a line-mode signal, and perform variational modal decomposition on the line-mode signal to obtain the plurality of modal components.

[0108] Optionally, the first processing module 703 includes: a first processing unit configured to perform Hilbert transform on the each modal component to obtain a Hilbert-transformed signal of the each modal component, and correspondingly combine the each modal component and the Hilbert-transformed signal of the each modal component to construct an analytic signal of the each modal component; a second processing unit configured to analyze the instantaneous frequency of the each modal component and the analytic signal of the each modal component to obtain a border spectrum frequency domain feature of the each modal component; and a third processing unit configured to combine and superimpose the border spectrum frequency domain features of the each modal component to obtain the amplitude feature of the fault traveling wave signal.

[0109] Optionally, the second processing unit is specifically configured to perform analysis and derivation on the analytic signal of the each modal component and the instantaneous frequency of the each modal component by using Hilbert-Huang transform to obtain a Hilbert spectrum of the each modal component, and combine and superimpose the Hilbert spectrum of the each modal component in the time domain to obtain the border spectrum frequency domain feature of the each modal component.

[0110] Optionally, the second processing module 704 includes: a fourth processing unit configured to sequentially perform Wigner-Ville distribution calculation and window smoothing processing on the each modal component to obtain a smoothed pseudo-Wigner-Ville distribution signal of the each modal component; a fifth processing unit configured to perform border analysis on the smoothed pseudo-Wigner-Ville distribution signal in the frequency domain to obtain an instantaneous energy of the each modal component, and perform border analysis on the smoothed pseudo-Wigner-Ville distribution signal in the time domain to obtain an energy spectral density of the each modal component; and a sixth processing unit configured to combine and superimpose the instantaneous energies of the each modal component to obtain the time-frequency feature of the fault traveling wave signal, and combine and superimpose the energy spectral densities of the each modal component to obtain the energy feature of the fault traveling wave signal.

[0111] Optionally, the fourth processing unit is specifically configured to analyze the modal components by using the Wigner-Ville distribution to obtain time-frequency plane distribution signals of the modal components; and perform phased sliding window on the time-frequency plane distribution signals of the modal components by using a smoothing window function to obtain smoothed pseudo-Wigner-Ville distribution signals of the modal components.

[0112] Optionally, the fifth processing unit is specifically configured to perform boundary analysis on the smoothed pseudo-Wigner-Ville distribution signals in the frequency domain to obtain time boundary functions of the modal components; perform boundary analysis on the smoothed pseudo-Wigner-Ville distribution signals in the time domain to obtain frequency domain boundary functions of the modal components; determine the time boundary functions of the modal components as instantaneous energies of the modal components; and determine the frequency domain boundary functions of the modal components as energy spectrum densities of the modal components.

[0113] It should be noted that the description of the system is similar to the description of the above-mentioned application method embodiments, and has similar beneficial effects as the application method embodiments. For technical details not disclosed in the system embodiments of the present application, please refer to the description of the application method embodiments of the present application for understanding.

[0114] Embodiment 3: Based on the same inventive concept, as shown in Figure 8 The electronic device in this embodiment can include a processor 810, a memory 820, a transceiver component 830, etc. The processor 810, the memory 820, and the transceiver component 830 are connected through a bus 840; the memory 820 can be used to store an execution program, and an exemplary execution program can include instructions; the processor 810 is used to execute the instructions stored in the memory. The memory 820 can also be used to store data, which can be called and / or modified when the instructions are executed.

[0115] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are suitable for loading and executing one or more instructions in the storage medium to implement a corresponding method process or a corresponding function, so as to implement the fault traveling wave signal feature extraction method in the above embodiment.

[0116] Embodiment 4: Based on the same inventive concept, the application further provides a readable storage medium, specifically an electronic device readable storage medium (Memory). The electronic device readable storage medium is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the storage medium herein can include a built-in storage medium in the electronic device, and of course can also include an expansion storage medium supported by the electronic device. The storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more execution programs (including program codes). It should be noted that the storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium to implement the fault traveling wave signal feature extraction method in the above embodiment.

[0117] Those skilled in the art will understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0118] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0119] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0120] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0121] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but are not intended to limit the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. A method for feature extraction of fault traveling wave signals, characterized in that, The method includes: Obtain the line mode component of the fault traveling wave signal in the power distribution line; Variational mode decomposition is performed on the linear mode components to obtain multiple mode components; The amplitude characteristics of the fault traveling wave signal are obtained by sequentially performing Hilbert transform and frequency domain feature extraction on each of the multiple modal components. The time-frequency characteristics and energy characteristics of the fault traveling wave signal are obtained by sequentially performing Wigner-Willi distribution calculation and windowing smoothing on each of the multiple modal components.

2. The method according to claim 1, characterized in that, The variational mode decomposition of the linear mode components yields multiple mode components, including: The line-mode components are subjected to phase-mode transformation to obtain the line-mode signal; Variational mode decomposition is performed on the linear mode signal to obtain the multiple mode components.

3. The method according to claim 1 or 2, characterized in that, The step of sequentially performing Hilbert transform and frequency domain feature extraction on each of the multiple modal components to obtain the amplitude characteristics of the fault traveling wave signal includes: Perform a Hilbert transform on each modal component to obtain the Hilbert-transformed signal of each modal component, and combine each modal component and the Hilbert-transformed signal of each modal component accordingly to construct the analytic signal of each modal component; The instantaneous frequency and analytical signal of each modal component are analyzed to obtain the boundary spectrum frequency domain characteristics of each modal component. The amplitude characteristics of the fault traveling wave signal are obtained by combining and superimposing the boundary spectrum frequency domain features of each modal component.

4. The method according to claim 3, characterized in that, The analysis of the instantaneous frequency of each modal component and the analytic signal of each modal component to obtain the boundary spectral frequency domain characteristics of each modal component includes: The Hilbert-Huang transform is used to analyze and derive the analytic signals and instantaneous frequencies of each modal component, thereby obtaining the Hilbert spectrum of each modal component. The Hilbert spectra of each modal component are combined and superimposed in the time domain to obtain the boundary spectral frequency domain features of each modal component.

5. The method according to claim 1, characterized in that, The step of sequentially performing Wigner-Willi distribution calculations and windowing smoothing on each of the multiple modal components to obtain the time-frequency and energy characteristics of the fault traveling wave signal includes: The Wigner-Willi distribution of each modal component is calculated and windowed smoothed sequentially to obtain the smoothed pseudo-Wigner-Willi distribution signal of each modal component; Boundary analysis is performed on the smoothed pseudo-Wigner-Willi distribution signal in the frequency domain to obtain the instantaneous energy of each modal component, and boundary analysis is performed on the smoothed pseudo-Wigner-Willi distribution signal in the time domain to obtain the energy spectral density of each modal component; The instantaneous energies of each modal component are combined and superimposed to obtain the time-frequency characteristics of the fault traveling wave signal, and the energy spectral densities of each modal component are combined and superimposed to obtain the energy characteristics of the fault traveling wave signal.

6. The method according to claim 5, characterized in that, The step of sequentially performing Wigner-Willi distribution calculations and windowing smoothing on each modal component to obtain smoothed pseudo-Wigner-Willi distribution signals for each modal component includes: The Wigner-Willi distribution is used to analyze each modal component to obtain the time-frequency plane distribution signal of each modal component; A smooth window function is used to perform staged sliding windowing on the time-frequency plane distribution signals of each modal component to obtain the smoothed pseudo-Wigner-Willi distribution signals of each modal component.

7. The method according to claim 5 or 6, characterized in that, The step of performing boundary analysis on the smoothed pseudo-Wigner-Willie distribution signal in the frequency domain to obtain the instantaneous energy of each modal component, and performing boundary analysis on the smoothed pseudo-Wigner-Willie distribution signal in the time domain to obtain the energy spectral density of each modal component, includes: Boundary analysis is performed on the smoothed pseudo-Wigner-Willie distribution signal in the frequency domain to obtain the time boundary functions of each modal component; Boundary analysis is performed on the smoothed pseudo-Wigner-Willi distribution signal in the time domain to obtain the frequency domain boundary functions of each modal component; The time boundary function of each modal component is determined as the instantaneous energy of each modal component, and the frequency domain boundary function of each modal component is determined as the energy spectral density of each modal component.

8. A feature extraction system for fault traveling wave signals, characterized in that, The system includes: The acquisition module is used to acquire the line mode component of the fault traveling wave signal in the power distribution line; The decomposition module is used to perform variational mode decomposition on the linear mode components to obtain multiple mode components; The first processing module is used to sequentially perform Hilbert transform and frequency domain feature extraction on each of the multiple modal components to obtain the amplitude characteristics of the fault traveling wave signal. The second processing module is used to sequentially perform Wigner-Willi distribution calculation and windowing smoothing on each of the multiple modal components to obtain the time-frequency characteristics and energy characteristics of the fault traveling wave signal.

9. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the feature extraction method for fault traveling wave signals as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that, It contains an executable program, which, when executed, implements the feature extraction method for fault traveling wave signals as described in any one of claims 1 to 7.

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