Fault positioning method and system based on traveling wave signals

Abstract

The invention discloses a fault positioning method and system based on a traveling wave signal. The method comprises the following steps: collecting a fault traveling wave signal on a power line; cooperatively injecting the adjustable amplitude noise and the frequency domain windowing noise into the fault traveling wave signal to perform iterative mode decomposition to obtain an IMF component set; screening out a target IMF component from the IMF component set based on the wave head significance index, performing wavelet packet decomposition on the target IMF component to obtain a node coefficient set, determining a target base based on a cost function of the node coefficient set, and performing asymmetric quantization processing on a node coefficient of the target base to obtain a noise reduction signal; and performing energy enhancement processing on the noise reduction signal to obtain an enhanced signal, screening out a peak with the highest energy value as a fault traveling wave head through dynamic single-peak detection based on the enhanced signal, and performing fault positioning based on the fault traveling wave head. According to the invention, the precision of traveling wave head detection can be improved.

Description

Technical Field

[0001] This invention relates to the field of traveling wave detection, and in particular to a fault location method and system based on traveling wave signals. Background Technology

[0002] In power systems, the stable operation of transmission lines is crucial, and fault location is a vital link in ensuring this stability. When a fault occurs on a line, transient traveling wave signals are generated that propagate along the line. These traveling wave signals contain key information such as the location and type of the fault. By detecting and analyzing these traveling wave signals, the location of the fault can be quickly and accurately determined, enabling timely repairs, reducing power outage time and economic losses, and improving power supply reliability and system stability. Therefore, traveling wave signal detection is of paramount importance for fault location in power lines.

[0003] Currently, common techniques for fault location using traveling wave signals include wavelet transform and neural network methods. However, these methods all have their drawbacks. Wavelet transform uses multi-resolution analysis to extract abrupt changes in traveling wave signals to achieve fault detection and location, but it suffers from mode aliasing, is sensitive to the selection of wavelet basis functions, and has high computational complexity. Neural network methods use deep learning models to automatically extract features from traveling wave signals, but they require a large amount of labeled data for training, consume a lot of computational resources, have poor model interpretability, and may also suffer from overfitting. Therefore, they are difficult to meet the requirements of high accuracy, strong robustness, and real-time fault location in practical applications. Summary of the Invention

[0004] This invention provides a fault location method and system based on traveling wave signals, which can improve the accuracy of traveling wave front detection and thus achieve high-precision fault location of power lines.

[0005] One embodiment of the present invention provides a fault location method based on traveling wave signals, comprising:

[0006] Collect fault traveling wave signals on power lines;

[0007] Adjustable amplitude noise and frequency domain windowed noise are co-injected into the fault traveling wave signal for iterative mode decomposition to obtain the IMF component set. The adjustable amplitude noise adaptively decays with the residual energy of the decomposition process, and the frequency domain windowed noise is generated in the wavefront characteristic frequency band by the matched signal.

[0008] Target IMF components are selected from the IMF component set based on the wavefront significance index. Wavelet packet decomposition is performed on the target IMF components to obtain a set of node coefficients. The target basis is determined based on the cost function of the node coefficient set. The node coefficients of the target basis are then subjected to asymmetric quantization to obtain a denoised signal.

[0009] The noise-reduced signal is subjected to energy enhancement processing to obtain an enhanced signal. Based on the enhanced signal, the peak with the highest energy value is selected as the fault traveling wave head through dynamic single-peak detection. Fault location is then performed based on the fault traveling wave head.

[0010] This invention employs iterative mode decomposition by co-injecting adjustable amplitude noise and frequency-domain windowed noise into the fault traveling wave signal. The adjustable amplitude noise avoids excessive interference from noise on the decomposition results, while the frequency-domain windowed noise allows the decomposition process to focus more on the key characteristic frequency bands of the fault traveling wave signal. Therefore, it obtains feature information that better reflects different frequencies and time scales in the original fault traveling wave signal, providing higher-quality basic data for subsequent feature selection and signal processing. By selecting the target IMF component, it further focuses on the component that best reflects the characteristics of the fault traveling wave front, removing irrelevant or noisy components. Wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF component, allowing it to... By decomposing the signal into different frequency bands and time positions, more detailed signal feature information is obtained. Asymmetric quantization of the target base's node coefficients further optimizes the signal representation, highlighting useful information, suppressing noise, and preserving key feature details such as the wavefront. Therefore, the resulting denoised signal retains the key features of the fault traveling wave while reducing noise interference, making subsequent wavefront detection easier and more accurate. Energy enhancement processing further enhances the characteristics of the fault traveling wave front in the denoised signal, making it more prominent in terms of energy. Simultaneously, dynamic single-peak detection accurately selects the peak with the highest energy value from the enhanced signal as the fault traveling wave front, avoiding interference from noise and spurious peaks, and improving the accuracy of wavefront detection. Compared with existing technologies, this application can improve the accuracy of traveling wave front detection, thereby achieving high-precision fault location in power lines.

[0011] Furthermore, the adjustable amplitude noise adaptively decays with the residual energy of the decomposition process, specifically as follows:

[0012] The total energy of the signal is obtained by squaring the amplitude of each sampling point in the fault traveling wave signal and then summing the results.

[0013] The residual energy is obtained by squaring the amplitudes of each sampling point in the residual signal during the decomposition process of the fault traveling wave signal and summing them. The adjustable amplitude noise is then determined based on the total signal energy and the residual energy.

[0014] By ensuring that the adjustable amplitude noise adaptively decays with the residual energy during the decomposition process, it helps to gradually extract different feature components in the signal during the decomposition process and avoids excessive interference of noise on the decomposition results.

[0015] Furthermore, the frequency-domain windowed noise is obtained through wavefront characteristic frequency band conversion, specifically as follows:

[0016] Perform a Fourier transform on the fault traveling wave signal to obtain the transform result, and calculate the power spectral density of the transform result;

[0017] The wavefront characteristic frequency band is determined based on the power spectral density, and the spectrum is truncated within the wavefront characteristic frequency band to generate matched noise. The matched noise is then converted into the frequency domain windowed noise through inverse Fourier transform.

[0018] By converting the wavefront characteristic frequency band to obtain the frequency domain windowed noise, the decomposition process can be more focused on the key characteristic frequency bands of the fault traveling wave signal, thereby improving the accuracy and representativeness of the decomposed IMF components.

[0019] Furthermore, the step of selecting target IMF components from the IMF component set based on the wavefront significance index specifically involves:

[0020] Time-domain analysis is performed on the IMF component set to determine the maximum amplitude and standard deviation in the time domain, and the power spectral density and target signal frequency of the IMF component set in the characteristic frequency band of the wavefront are calculated.

[0021] The wavefront significance index is calculated based on the maximum amplitude in the time domain, the standard deviation, the power spectral density, and the target signal frequency.

[0022] Based on the wavefront significance index, the target IMF component is selected from the IMF component set.

[0023] By filtering the target IMF components, we can further focus on the components that best reflect the characteristics of the fault traveling wave front, and remove irrelevant components or those with strong noise interference.

[0024] Furthermore, the step of performing wavelet packet decomposition on the target IMF component to obtain the node coefficient set specifically involves:

[0025] The target IMF component is used as the input signal for layer-by-layer decomposition, and a wavelet packet tree is constructed layer by layer during the decomposition process until a preset number of decomposition layers is reached. Then, the coefficients corresponding to all nodes are extracted from the wavelet packet tree to obtain the node coefficient set.

[0026] In this way, wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF components, decomposing them into different frequency bands and time positions to obtain more detailed signal characteristic information.

[0027] Furthermore, the step of using the target IMF component as an input signal for layer-by-layer decomposition, and constructing a wavelet packet tree layer by layer during the decomposition process, specifically involves:

[0028] In the first-level decomposition, the input signal is subjected to low-pass filtering and high-pass filtering respectively to obtain the low-frequency coefficients and high-frequency coefficients of the first level. In each subsequent level decomposition, the low-frequency coefficients and high-frequency coefficients output by the previous level are subjected to low-pass filtering, high-pass filtering and downsampling operations respectively to obtain the low-frequency coefficients and high-frequency coefficients of the current level. A wavelet packet tree is constructed based on the low-frequency coefficients and high-frequency coefficients of all levels.

[0029] In this way, wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF components, decomposing them into different frequency bands and time positions to obtain more detailed signal characteristic information.

[0030] Furthermore, the cost function includes information entropy, L1 norm, and kurtosis, and the determination of the target basis based on the cost function of the node coefficient set is specifically as follows:

[0031] For each node in the set of node coefficients, calculate the information entropy, the L1 norm, and the kurtosis.

[0032] The cost function is determined based on the information entropy, the L1 norm, and the kurtosis, and the node with the smallest cost function is selected as the target basis.

[0033] By determining the target basis through a cost function and performing asymmetric quantization on the node coefficients of the target basis, the signal representation can be further optimized, highlighting useful information, suppressing noise, and protecting key feature details such as wavefronts.

[0034] Further, the step of performing energy enhancement processing on the noise-reduced signal to obtain the enhanced signal specifically involves:

[0035] The noise-reduced signal is subjected to fractional-order differentiation to obtain the first processing result;

[0036] The time-domain differential energy and time-frequency distribution of the first processing result are calculated, and the frequency-domain gradient energy is calculated at the wavefront frequency feature based on the time-frequency distribution. The time-domain differential energy and the frequency-domain gradient energy are then weighted and fused according to a preset weight to obtain the second processing result, wherein the wavefront frequency feature is the characteristic frequency of the target IMF component.

[0037] The analysis window is dynamically adjusted based on the energy curve of the second processing result to obtain the enhanced signal.

[0038] By using energy enhancement processing, the characteristics of the faulty traveling wavefront in the noise reduction signal can be further enhanced, making it more prominent in terms of energy.

[0039] Furthermore, the step of selecting the peak with the highest energy value as the fault traveling wave front based on the enhanced signal through dynamic single-peak detection, and then locating the fault based on the fault traveling wave front, specifically involves:

[0040] Calculate the dynamic energy threshold of the enhanced signal, compare the dynamic energy threshold with a preset energy curve, and filter out wavefront candidate points in the energy curve that are higher than the dynamic energy threshold;

[0041] The peak with the highest energy value among the candidate wavefronts is taken as the fault traveling wavefront, and the time information of the fault traveling wavefront is extracted. The time information is then input into the traveling wave double-end ranging formula to calculate the fault location.

[0042] By employing dynamic single-peak detection, the peak with the highest energy value can be accurately selected from the enhanced signal as the fault traveling wave front, avoiding interference from noise and spurious peaks and improving the accuracy of wave front detection. Based on the accurate fault traveling wave front, fault location can be precisely calculated using the traveling wave double-end ranging formula, achieving high-precision fault location in power lines.

[0043] Another embodiment of the present invention provides a fault location system based on traveling wave signals, including: an acquisition module, a decomposition module, a processing module, and a location module;

[0044] The acquisition module is used to acquire fault traveling wave signals on power lines;

[0045] The decomposition module is used to inject adjustable amplitude noise and frequency domain windowed noise into the fault traveling wave signal to perform iterative mode decomposition and obtain IMF component set. The adjustable amplitude noise adaptively decays with the residual energy of the decomposition process, and the frequency domain windowed noise is generated in the wavefront characteristic frequency band by the matching signal.

[0046] The processing module is used to select target IMF components from the IMF component set based on the wavefront significance index, perform wavelet packet decomposition on the target IMF components to obtain a set of node coefficients, determine the target basis based on the cost function of the node coefficient set, and perform asymmetric quantization on the node coefficients of the target basis to obtain a denoised signal.

[0047] The positioning module is used to perform energy enhancement processing on the noise reduction signal to obtain an enhanced signal, and based on the enhanced signal, select the peak with the highest energy value as the fault traveling wave head through dynamic single-peak detection, and perform fault positioning based on the fault traveling wave head.

[0048] This invention employs iterative mode decomposition by co-injecting adjustable amplitude noise and frequency-domain windowed noise into the fault traveling wave signal. The adjustable amplitude noise avoids excessive interference from noise on the decomposition results, while the frequency-domain windowed noise allows the decomposition process to focus more on the key characteristic frequency bands of the fault traveling wave signal. Therefore, it obtains feature information that better reflects different frequencies and time scales in the original fault traveling wave signal, providing higher-quality basic data for subsequent feature selection and signal processing. By selecting the target IMF component, it further focuses on the component that best reflects the characteristics of the fault traveling wave front, removing irrelevant or noisy components. Wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF component, allowing it to... By decomposing the signal into different frequency bands and time positions, more detailed signal feature information is obtained. Asymmetric quantization of the target base's node coefficients further optimizes the signal representation, highlighting useful information, suppressing noise, and preserving key feature details such as the wavefront. Therefore, the resulting denoised signal retains the key features of the fault traveling wave while reducing noise interference, making subsequent wavefront detection easier and more accurate. Energy enhancement processing further enhances the characteristics of the fault traveling wave front in the denoised signal, making it more prominent in terms of energy. Simultaneously, dynamic single-peak detection accurately selects the peak with the highest energy value from the enhanced signal as the fault traveling wave front, avoiding interference from noise and spurious peaks, and improving the accuracy of wavefront detection. Compared with existing technologies, this application can improve the accuracy of traveling wave front detection, thereby achieving high-precision fault location in power lines. Attached Figure Description

[0049] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating an embodiment of the fault location method based on traveling wave signals provided in this application;

[0051] Figure 2 This is a schematic diagram of an embodiment of the fault location system based on traveling wave signals provided in this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] 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 this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0054] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0057] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0058] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0059] In power systems, the stable operation of transmission lines is crucial, and fault location is a key aspect of ensuring this stability. When a fault occurs on a line, a transient traveling wave signal is generated, containing crucial information such as the fault's location and type. By detecting and analyzing the traveling wave signal, the fault location can be quickly and accurately determined, enabling timely repairs and reducing power outage time and economic losses. Therefore, traveling wave signal detection is extremely important for power line fault location. Currently, wavelet transform and neural network methods are commonly used for traveling wave signal fault location, but these methods have limitations and cannot meet the requirements of high accuracy, strong robustness, and real-time performance in practical applications.

[0060] See Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the fault location method based on traveling wave signals provided in this application. To improve the accuracy of traveling wave front detection, an embodiment of the present invention provides a fault location method based on traveling wave signals, including steps S101 to S104.

[0061] Step S101: Collect fault traveling wave signals on the power line;

[0062] In some embodiments, fault traveling wave signals on power lines are acquired by: using traveling wave sensors installed on power lines to monitor and capture voltage or current traveling wave signals, i.e., initial fault traveling wave signals, generated when a fault occurs in the power line in real time. These sensors typically have high sensitivity, fast response characteristics, and are able to capture weak traveling wave signals at the moment a fault occurs.

[0063] In some embodiments, after obtaining the initial fault traveling wave signal, the initial fault traveling wave signal is initially filtered by a hardware filtering circuit to remove some high-frequency noise and interference signals. Then, the continuous analog signal is converted into a discrete digital signal by an analog-to-digital converter (AD sampling chip) to obtain discrete traveling wave signal data (i.e., a determined fault traveling wave signal), which provides basic data input for subsequent signal processing.

[0064] Step S102: Adjustable amplitude noise and frequency domain windowed noise are co-injected into the fault traveling wave signal to perform iterative mode decomposition and obtain the IMF component set. The adjustable amplitude noise adaptively decays with the residual energy of the decomposition process, and the frequency domain windowed noise is generated in the wavefront characteristic frequency band by the matched signal.

[0065] In some embodiments, the adjustable amplitude noise adaptively attenuates with the residual energy of the decomposition process. Specifically, this involves: squaring the amplitudes of each sampling point in the fault traveling wave signal and summing them to obtain the total signal energy; squaring the amplitudes of each sampling point in the residual signal during the decomposition process of the fault traveling wave signal and summing them to obtain the residual energy; and determining the adjustable amplitude noise based on the total signal energy and the residual energy. Specifically, firstly, the amplitude x of each sampling point is obtained from the fault traveling wave signal x(t). i These amplitudes reflect the signal strength at different time points, and the amplitude at each sampling point is squared. By amplifying the differences in signal amplitude, the contribution of high-amplitude components to the energy is made more significant. Then, the squares of the amplitudes of all sampling points are summed to obtain the total signal energy. The relevant formula is In the formula, N is the total number of sampling points of the fault traveling wave signal. Secondly, during the decomposition of the fault traveling wave signal, the amplitude r of each sampling point in the k-th order residual signal is obtained. k,i The amplitude at each sampling point is squared, and then the squared amplitudes are summed to obtain the residual energy. The relevant formula is: In the formula, r k,i This represents the amplitude of the k-th order residual signal at the i-th sampling point, where N is the total number of sampling points, and k represents the current order during the decomposition process. Finally, an initial noise amplitude β0 (default 0.2) is set to control the noise injection intensity, based on the total signal energy. and residual energy Calculate the adjustable amplitude noise β k The relevant calculation formula is: In the formula, β0 is the initial noise amplitude (default 0.2), which controls the noise injection intensity; Let L2 norm squared be the residual signal of order k, representing the remaining energy in the current decomposition stage; ε is the total energy of the original signal, used to normalize the residual energy; ε is a minimal constant to prevent the denominator from being zero.

[0066] It should be noted that the initial residual signal is r0(t) = x(t), and the total signal energy characterizes the overall strength of the entire signal.

[0067] It should be noted that in the early stage of decomposition, the residual energy is high, and more noise is injected to fully exploit the high-frequency features of the signal; as the decomposition progresses, the residual energy decreases, and at this time, noise injection is reduced to avoid excessive noise interference in the later decomposition, thereby suppressing high-frequency over-decomposition and reducing mode mixing.

[0068] By ensuring that the adjustable amplitude noise adaptively decays with the residual energy during the decomposition process, it helps to gradually extract different feature components in the signal during the decomposition process and avoids excessive interference of noise on the decomposition results.

[0069] In some embodiments, the frequency-domain windowed noise is obtained by conversion of the wavefront characteristic frequency band. Specifically, this involves: performing a Fourier transform on the fault traveling wave signal to obtain the transform result, and calculating the power spectral density of the transform result; determining the wavefront characteristic frequency band based on the power spectral density, extracting the spectrum within the wavefront characteristic frequency band and generating matched noise, and converting the matched noise into the frequency-domain windowed noise through an inverse Fourier transform. Specifically, firstly, the time-domain data of the fault traveling wave signal x(t) is substituted into the Fourier transform formula to obtain the transform result, which reflects the amplitude and phase information of the signal at different frequencies; then, the power spectral density P of the transform result is calculated. x (f), where the power spectral density P x (f) describes the power distribution of the signal in the frequency domain; then, the power spectral density curve is analyzed to find the frequency band with higher power, i.e., the wavefront characteristic frequency band [f] c -B / 2,f c +B / 2], and within the determined wavefront characteristic frequency band [f c -B / 2,f c +B / 2], extract the spectrum of this frequency band from the transformation result, then generate matched noise based on the extracted spectrum, and finally substitute the generated matched noise into the inverse Fourier transform formula to transform from the frequency domain back to the time domain, obtaining the frequency domain windowed noise ζ. (k) (t), the relevant calculation formula is: In the formula, N(0,1) is standard Gaussian white noise; P x (f) represents the original signal power spectral density; rect(·) denotes the power spectral density of the signal with respect to f. c A rectangular window centered at a bandwidth of B; F -1 This is the inverse Fourier transform.

[0070] It should be noted that, typically, the wavefront of a fault traveling wave signal contains abundant high-frequency components. Therefore, the power corresponding to the wavefront characteristic frequency band on the power spectral density curve is relatively high. The approximate range of the wavefront characteristic frequency band can be determined by setting a power threshold or based on experience. For example, a frequency band with a high power spectral density (such as the first 10% of the high-frequency portion) can be selected as the wavefront characteristic frequency band, thereby matching its spectrum with the signal power spectrum and focusing energy on the wavefront frequency band.

[0071] It should be noted that the frequency domain windowed noise ζ (k) The generation of (t) allows the noise power spectrum to match the signal spectrum, with energy focused in the wavefront band f. c ±B ensures the directionality of IMF decomposition.

[0072] By converting the wavefront characteristic frequency band to obtain the frequency domain windowed noise, the decomposition process can be more focused on the key characteristic frequency bands of the fault traveling wave signal, thereby improving the accuracy and representativeness of the decomposed IMF components.

[0073] In some embodiments, adjustable amplitude noise and frequency-domain windowed noise are co-injected into the fault traveling wave signal for iterative mode decomposition to obtain an IMF component set. Specifically, adjustable amplitude noise with adjusted amplitude and generated frequency-domain windowed noise are co-injected into the fault traveling wave signal. Then, the CEEMDAN algorithm is used to perform iterative mode decomposition on the noise-injected fault traveling wave signal to obtain a series of intrinsic mode functions (IMFs). In each iteration, the injected noise makes the signal decomposition more stable and effective, avoiding mode aliasing. The decomposed IMF component set characterizes the features of the original signal at different frequencies and time scales, and retains key fault feature information in the signal.

[0074] It should be noted that co-injection refers to superimposing two types of noise into the fault traveling wave signal in an appropriate manner, so that they can work together during the iterative mode decomposition process.

[0075] Step S103: Based on the wavefront significance index, target IMF components are selected from the IMF component set, wavelet packet decomposition is performed on the target IMF components to obtain a set of node coefficients, and the target basis is determined based on the cost function of the node coefficient set. Then, the node coefficients of the target basis are subjected to asymmetric quantization to obtain a denoised signal.

[0076] In some embodiments, the step of selecting target IMF components from the IMF component set based on the wavefront significance index specifically involves: performing time-domain analysis on the IMF component set to determine the maximum amplitude and standard deviation in the time domain, and calculating the power spectral density and target signal frequency of the IMF component set in the wavefront characteristic frequency band; calculating the wavefront significance index based on the maximum amplitude in the time domain, the standard deviation, the power spectral density, and the target signal frequency; and selecting the target IMF component from the IMF component set based on the wavefront significance index. Specifically, firstly, for each IMF component in the IMF component set, the amplitude of all sampling points of that IMF component is traversed, and the maximum amplitude in the time domain, max(|IMF_max ... k |), reflecting the impact intensity of the wavefront in this component (the larger the value, the more significant the wavefront), and the standard deviation std(IMF) of this IMF component is calculated using a statistical formula. k First, to reflect the fluctuation of the signal (the smaller the standard deviation, the more stable the signal and the less noise interference); second, to perform Fourier transform on the IMF components to obtain their frequency domain representation, and to calculate the power spectral density at each frequency point and the maximum frequency with a power spectral density greater than zero, which is the target signal frequency; then, based on In the formula, max(|IMF k |) represents the maximum amplitude of the IMF in the time domain, reflecting the intensity of the wavefront impact; std(IMF k () represents the IMF standard deviation, which measures signal volatility; f is the IMF power spectral density; max The highest frequency of the signal; [f c -B / 2,f c [+B / 2] represents the wavefront characteristic frequency band. The maximum amplitude in the time domain, the standard deviation, the power spectral density, and the target signal frequency are substituted into the calculation. By combining the time and frequency domain characteristics, the wavefront significance index (WSI) for each IMF component is obtained. k This is used to evaluate the significance of wavefront features in IMF components; finally, a threshold for the wavefront significance index is set, and based on the calculated wavefront significance index of each IMF component, IMF components with indices greater than the threshold are selected (i.e., IMFK components with steep time-domain features and concentrated frequency-domain energy are selected). * =argmax(WSI K As target IMF components, these components contain obvious wavefront features, which can be used for subsequent fault detection and location analysis.

[0077] It should be noted that the maximum amplitude in the time domain, max(|IMF), is used to... k |) and standard deviation std(IMF) kThe relative relationship between the two components can be used to make a preliminary judgment on whether the IMF components contain steep wavefront features (the larger the ratio, the steeper the wavefront).

[0078] By filtering the target IMF components, we can further focus on the components that best reflect the characteristics of the fault traveling wave front, and remove irrelevant components or those with strong noise interference.

[0079] In some embodiments, the step of performing wavelet packet decomposition on the target IMF component to obtain the node coefficient set specifically involves: using the target IMF component as an input signal to perform layer-by-layer decomposition, and constructing a wavelet packet tree layer by layer during the decomposition process until a preset number of decomposition layers is reached; then extracting the coefficients corresponding to all nodes from the wavelet packet tree to obtain the node coefficient set.

[0080] In some embodiments, the step of using the target IMF component as an input signal for layer-by-layer decomposition and constructing a wavelet packet tree layer by layer during the decomposition process specifically involves: in the first layer decomposition, the input signal is subjected to low-pass filtering and high-pass filtering respectively to obtain the low-frequency and high-frequency coefficients of the first layer; in each subsequent layer decomposition, the low-frequency and high-frequency coefficients output from the previous layer are subjected to low-pass filtering, high-pass filtering, and downsampling operations respectively to obtain the low-frequency and high-frequency coefficients of the current layer; and a wavelet packet tree is constructed based on the low-frequency and high-frequency coefficients of all layers. Specifically, first, a suitable wavelet basis function (such as Daubechies) is selected, and a suitable number of decomposition layers (such as 3 layers) is preset; then, the decomposition process is initialized, and the target IMF component is... As the input signal, for layer-by-layer decomposition, in the first layer decomposition, for By performing low-pass filtering and high-pass filtering respectively, the low-frequency coefficients a1 and high-frequency coefficients d1 of the first layer are obtained, as shown in the following formula: In the formula, h = [0.33267, 0.80689, 0.45988, -0.13501, -0.13501, -0.08544, 0.03523] represents the coefficients of the low-pass filter, and g = [0.03523, 0.08544, 0.13501, 0.45988, 0.80689, 0.33267] represents the coefficients of the high-pass filter. A wavelet packet tree is constructed starting from the low-frequency coefficients (a1) and high-frequency coefficients (d1) obtained from the first layer decomposition. The low-frequency coefficients (a1) correspond to the left branch of the wavelet packet tree, and the high-frequency coefficients (d1) correspond to the right branch. Subsequently, for each subsequent layer decomposition (from the 2nd to the 3rd layer), not only the low-frequency coefficients a1 of the previous layer... j-1 Low-pass and high-pass filtering are performed, followed by downsampling, to obtain the low-frequency coefficients a of the next layer. j and high frequency coefficient d j At the same time, the high-frequency coefficients d of the previous layer also need to be considered. j-1Performing the same processing, we obtain the low-frequency coefficient a' of the other branch. j and high frequency coefficient d' j The formula is as follows: k is the index of the filter coefficients. This process is equivalent to further subdividing each frequency band of the signal to construct a complete wavelet packet tree based on the low-frequency and high-frequency coefficients of each layer. Each node of the wavelet packet tree corresponds to specific frequency band information, which can more finely describe the characteristics of the signal.

[0081] In some embodiments, until a preset number of decomposition layers is reached, the coefficients corresponding to all nodes are extracted from the wavelet packet tree to obtain the node coefficient set. Specifically, after each decomposition layer is completed, it is checked whether the current decomposition layer has reached the preset number of decomposition layers (e.g., 3 layers). If not, the decomposition of the low-frequency and high-frequency coefficients of the current layer continues to the next layer. If the preset number of decomposition layers is reached, the decomposition process is stopped, and the coefficients corresponding to all nodes are extracted from the constructed wavelet packet tree. These coefficients fully contain the characteristic information of the signal in different frequency bands, including the energy information of the low-frequency part and the detailed information of the high-frequency part. These coefficients are then organized into a set to obtain the node coefficient set.

[0082] It should be noted that low-pass filters extract the low-frequency components of a signal, preserving the main energy and trends of the signal; high-pass filters extract the high-frequency components of a signal, containing the details and abrupt changes of the signal.

[0083] It should be noted that each element in the set of node coefficients corresponds to a node in the wavelet packet tree, containing the feature information of that node, which provides a foundation for subsequent feature extraction and analysis.

[0084] In this way, wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF components, decomposing them into different frequency bands and time positions to obtain more detailed signal characteristic information.

[0085] In some embodiments, the cost function includes information entropy, L1 norm, and kurtosis. Determining the target basis based on the cost function of the node coefficient set specifically involves: calculating the information entropy, L1 norm, and kurtosis for each node in the node coefficient set; determining the cost function based on the information entropy, L1 norm, and kurtosis; and selecting the node with the smallest cost function as the target basis. Specifically, firstly, for each node in the node coefficient set, the wavelet coefficient d... n ,pass Calculate information entropy H(d) n In the formula, d n,i Let be the i-th wavelet coefficient of node n, and N be the total number of wavelet coefficients; then, for the wavelet coefficients d of node n...n Taking the absolute values ​​and summing them, we obtain the L1 norm ||d n ||1, The calculation formula is: To measure the richness of the transient components of a signal, it is also necessary to calculate the kurtosis K(d). n The relevant calculation formula is: In the formula, E[] represents the expectation operation, and μ is the mean of the wavelet coefficients of node n. Then, the information entropy H(d) of each node is... n L1 norm ||d n ||1 and kurtosis K(d) n Substitute the values ​​into the cost function formula to calculate the cost function value C(n) for each node based on the preset weights. For example, C(n) = 0.5·H(d n )+0.3·||d n ||1+0.2·K(d n These weights can be freely set as needed, and this application does not impose any restrictions. Finally, the cost function values ​​C(n) of all nodes are compared, and the node with the smallest cost function value is selected as the target basis, i.e., A. * =arg min(C(n)), where the target basis is the set of nodes that best represents the characteristics of the signal, providing the optimal basis for subsequent asymmetric quantization processing.

[0086] It should be noted that information entropy reflects the complexity of a signal; the higher the entropy value, the more complex the signal. The L1 norm reflects the energy concentration of a signal; the larger the value, the more concentrated the energy. The higher the kurtosis value, the richer the transient components. The cost function comprehensively considers the signal's complexity, energy concentration, and the richness of transient components.

[0087] By minimizing the cost function and comprehensively considering signal complexity, energy concentration, and transient component richness, the set of nodes that best represents the signal characteristics is selected, providing the optimal basis for subsequent asymmetric quantization processing. This ensures that while preserving high-frequency details of the wavefront, signal complexity is reduced, and processing efficiency and specificity are improved.

[0088] By determining the target basis through a cost function and performing asymmetric quantization on the node coefficients of the target basis, the signal representation can be further optimized, highlighting useful information, suppressing noise, and protecting key feature details such as wavefronts.

[0089] In some embodiments, the node coefficients of the target basis are subjected to asymmetric quantization to obtain a denoised signal. Specifically, firstly, the noise standard deviation of the denoised signal is calculated by performing statistical analysis on the signal, using the formula: σ j =median(|d j |) / 0.6745, where |d j| represents the absolute value sequence of coefficients for all layer nodes after wavelet packet decomposition. Then, based on the noise standard deviation σ... j The noise standard deviation σ of each layer in wavelet packet decomposition is estimated by using the energy distribution or other statistical properties of the signal. j And calculate the normalized information entropy of the wavelet coefficients at each level. In the formula, H j H is the information entropy of the wavelet packet node coefficients at the j-th layer. max This is the maximum information entropy of the wavelet packet node coefficients across all layers. Then, it is estimated based on the noise standard deviation of each layer. and normalized information entropy The threshold for asymmetric quantization is determined using the following formula: Finally, the node coefficients of the target basis are subjected to asymmetric quantization. The node coefficients after asymmetric quantization are used to reconstruct the signal, thereby obtaining the denoised signal.

[0090] In some embodiments, the specific implementation method for asymmetric quantization of the nodal coefficients of the target basis is as follows: for positive coefficients, a relaxed threshold is applied to retain more detailed information; for negative coefficients, a strict threshold is applied to strictly protect the wavefront descent. The relevant formula is: In the formula, d j,m This represents the m-th wavelet coefficient of the j-th layer; This represents the positive threshold; the higher the entropy, the lower the threshold.

[0091] It should be noted that noise standard deviation, as an indicator of noise intensity, and normalized information entropy can reflect the relative importance of information at each layer. The signal after wavelet domain joint thresholding not only suppresses noise but also retains key feature information such as wavefronts, thereby enhancing the signal's noise resistance and feature saliency.

[0092] Step S104: Perform energy enhancement processing on the noise reduction signal to obtain an enhanced signal, and select the peak with the highest energy value as the fault traveling wave head based on the enhanced signal through dynamic single-peak detection, and perform fault location based on the fault traveling wave head.

[0093] In some embodiments, the step of performing energy enhancement processing on the denoised signal to obtain an enhanced signal specifically involves: performing fractional-order differential processing on the denoised signal to obtain a first processing result; calculating the time-domain differential energy and time-frequency distribution of the first processing result, and calculating the frequency-domain gradient energy at the wavefront frequency feature based on the time-frequency distribution; and weighting and fusing the time-domain differential energy and the frequency-domain gradient energy according to a preset weight to obtain a second processing result, wherein the wavefront frequency feature is the characteristic frequency of the target IMF component; and dynamically adjusting the analysis window according to the energy curve of the second processing result to obtain the enhanced signal. Specifically, firstly, a 1.5th-order Caputo fractional-order differential is used to process the denoised signal, the mathematical expression of which is: In the formula, D 1.5 For fractional differential operators, x(t) is the fault traveling wave signal; τ is the integration variable used to time-shift the signal in the Wigner-Ville distribution calculation; x(n) is the denoised signal; k is the index variable for summation, used to represent different sampling points in the discrete-time signal; M is the order of the discrete fractional derivative; thus, the first processing result can be obtained. Next, the first processing result is subjected to time-domain differentiation, typically by calculating its first derivative, and then integrating or summing the square of the derivative in the time domain to obtain the time-domain differential energy (D). 1.5 x(t)) 2 Then, the time-frequency distribution is calculated using the Wigner-Ville distribution (WVD), and the relevant formula is as follows: In the formula, x * (t) represents the complex conjugate of the denoised signal x(t), f is the frequency variable, and the Wigner-Ville distribution can characterize the energy distribution of the signal in the time-frequency domain, reflecting the change of signal energy with time and frequency. Then, at the wavefront frequency f... c To calculate the time-frequency gradient energy, specifically, to calculate the gradient of the Wigner-Ville distribution with respect to frequency. This operation can reflect the instantaneous rate of change of the signal near the characteristic frequency of the wavefront, highlighting the high-frequency variation characteristics of the wavefront position, and converting the time-domain differential energy (D... 1.5 x(t)) 2 and frequency domain gradient energy The weighted summation yields the second processing result, as shown in the formula: In the formula, W x (t,f) represents the Wigner-Ville time-frequency distribution; f cHere, λ represents the wavefront characteristic frequency; the weight λ = 0.4 represents the weight of the frequency domain gradient energy. Finally, the energy curve of the second processing result is analyzed to determine the maximum value of the energy curve and the energy change of the signal at different time points. Based on the dynamic changes of the energy curve, the length of the analysis window is adjusted to obtain the enhanced signal. The relevant formula for the window length is: In the formula, L(t) is the dynamic window length; E max E(t) represents the maximum value of the energy curve; E(t) represents the energy value at the current moment.

[0094] It should be noted that when performing fractional derivative processing, the Grünwald-Letnikov approximation is usually used to achieve discretization calculation, thereby highlighting the transient characteristics of the wavefront, suppressing baseline drift, improving the wavefront response speed, making the high-frequency change characteristics at the wavefront more obvious, and obtaining the first processing result.

[0095] It should be noted that the time-domain differential energy reflects the rate of change of the signal in the time domain. At the wavefront position, due to the abrupt change in the signal, its time-domain differential energy will increase significantly.

[0096] It should be noted that when the energy is high at the wavefront, the window length is shortened to 5 to improve the time resolution and accurately locate the wavefront; when the energy is low in the noise range, the window length is extended to 30 to suppress noise interference. By dynamically adjusting the analysis window, the energy peak at the wavefront position is highlighted, noise is suppressed, and an enhanced signal is obtained.

[0097] By weighting and fusing the time-domain differential energy and the frequency-domain gradient energy, the signal-to-noise ratio of the main peak of the signal can be improved, making the energy peak at the wavefront position more prominent and facilitating subsequent wavefront detection.

[0098] This integration calculation takes into account the rate of change of the signal at different time points, thus highlighting the high-frequency variations in the signal. This processing helps to more accurately identify and locate the wavefront position in subsequent steps.

[0099] By using energy enhancement processing, the characteristics of the faulty traveling wavefront in the noise reduction signal can be further enhanced, making it more prominent in terms of energy.

[0100] In some embodiments, the step of selecting the peak with the highest energy value as the fault traveling wave front based on the enhanced signal through dynamic single-peak detection, and locating the fault based on the fault traveling wave front, specifically involves: calculating the dynamic energy threshold of the enhanced signal, comparing the dynamic energy threshold with a preset energy curve, and selecting candidate wave fronts in the energy curve that are higher than the dynamic energy threshold; selecting the peak with the highest energy value among the candidate wave fronts as the fault traveling wave front, extracting the time information of the fault traveling wave front, and inputting the time information into the traveling wave double-end ranging formula to calculate the fault location. Specifically, firstly, the energy curve of the enhanced signal is analyzed, its maximum value and median are calculated, and then, based on E... th =η·max(E(t))+(1-η)·median(E(t)) (where η=0.6, and η is a weighting factor to balance the influence of the maximum and median values). Substituting the maximum and median values ​​into the calculation, the dynamic energy threshold is obtained. Then, the dynamic energy threshold of the enhanced signal is compared with the preset energy curve, and points in the energy curve that are higher than the dynamic energy threshold are selected as wavefront candidate points, i.e., E(t). p )≥E th These points may be the locations of the fault traveling wavefront, requiring further verification and confirmation. Then, among the selected candidate wavefront points, the peak with the highest energy value is identified; the location corresponding to this peak is the location of the fault traveling wavefront. The time information of the fault traveling wavefront—its specific time position within the signal—is extracted; this time information is crucial for subsequent fault location. Finally, the extracted time information of the fault traveling wavefront is input into the traveling wave two-end ranging formula to calculate the specific location of the fault in the power line, achieving precise fault location.

[0101] It should be noted that the dynamic threshold combines the global extremum with the background noise level, avoiding the failure of the fixed threshold (traditional method) at low signal-to-noise ratios.

[0102] It should be noted that the preset energy curve is an energy curve determined based on experience or historical data, and is used as a benchmark for judging wavefront candidate points.

[0103] It should be noted that the traveling wave two-end ranging formula is a formula for calculating the fault location based on the time difference between the arrival times of the traveling wave at both ends.

[0104] By employing dynamic single-peak detection, the peak with the highest energy value can be accurately selected from the enhanced signal as the fault traveling wave front, avoiding interference from noise and spurious peaks and improving the accuracy of wave front detection. Based on the accurate fault traveling wave front, fault location can be precisely calculated using the traveling wave double-end ranging formula, achieving high-precision fault location in power lines.

[0105] This invention employs iterative mode decomposition by co-injecting adjustable amplitude noise and frequency-domain windowed noise into the fault traveling wave signal. The adjustable amplitude noise avoids excessive interference from noise on the decomposition results, while the frequency-domain windowed noise allows the decomposition process to focus more on the key characteristic frequency bands of the fault traveling wave signal. Therefore, it obtains feature information that better reflects different frequencies and time scales in the original fault traveling wave signal, providing higher-quality basic data for subsequent feature selection and signal processing. By selecting the target IMF component, it further focuses on the component that best reflects the characteristics of the fault traveling wave front, removing irrelevant or noisy components. Wavelet packet decomposition enables more detailed time-frequency analysis of the target IMF component, allowing it to... By decomposing the signal into different frequency bands and time positions, more detailed signal feature information is obtained. Asymmetric quantization of the target base's node coefficients further optimizes the signal representation, highlighting useful information, suppressing noise, and preserving key feature details such as the wavefront. Therefore, the resulting denoised signal retains the key features of the fault traveling wave while reducing noise interference, making subsequent wavefront detection easier and more accurate. Energy enhancement processing further enhances the characteristics of the fault traveling wave front in the denoised signal, making it more prominent in terms of energy. Simultaneously, dynamic single-peak detection accurately selects the peak with the highest energy value from the enhanced signal as the fault traveling wave front, avoiding interference from noise and spurious peaks, and improving the accuracy of wavefront detection. Compared with existing technologies, this application can improve the accuracy of traveling wave front detection, thereby achieving high-precision fault location in power lines.

[0106] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0107] An embodiment of the present invention provides a fault location system based on traveling wave signals, comprising: an acquisition module 100, a decomposition module 200, a processing module 300, and a location module 400;

[0108] The acquisition module 100 is used to acquire fault traveling wave signals on power lines;

[0109] The decomposition module 200 is used to inject adjustable amplitude noise and frequency domain windowed noise into the fault traveling wave signal to perform iterative mode decomposition and obtain IMF component set. The adjustable amplitude noise adaptively decays with the residual energy of the decomposition process, and the frequency domain windowed noise is generated in the wavefront characteristic frequency band by the matching signal.

[0110] The processing module 300 is used to select target IMF components from the IMF component set based on the wavefront saliency index, perform wavelet packet decomposition on the target IMF components to obtain a set of node coefficients, determine the target basis based on the cost function of the node coefficient set, and perform asymmetric quantization on the node coefficients of the target basis to obtain a denoised signal.

[0111] The positioning module 400 is used to perform energy enhancement processing on the noise reduction signal to obtain an enhanced signal, and based on the enhanced signal, select the peak with the highest energy value as the fault traveling wave head through dynamic single-peak detection, and perform fault positioning based on the fault traveling wave head.

[0112] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the fault location method based on traveling wave signals provided by any of the above-described method embodiments of the present invention.

[0113] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0114] Based on the above embodiments of the fault location method based on traveling wave signals, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the fault location method based on traveling wave signals of any embodiment of the present invention.

[0115] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0116] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0117] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0118] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the fault location method based on traveling wave signals as described in any of the above-described method embodiments of the present invention.

[0119] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0120] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method of fault location based on a travelling wave signal, characterized in that, The method comprises the following steps: Collecting a fault traveling wave signal on a power line; Injecting adjustable amplitude noise and frequency domain windowing noise into the fault traveling wave signal for iterative modal decomposition to obtain an IMF component set, wherein the adjustable amplitude noise is adaptively attenuated according to the residual energy of the decomposition process, and the frequency domain windowing noise is generated by matching the signal in the wave head characteristic frequency band; Screening a target IMF component from the IMF component set based on a wave head significance index, performing wavelet packet decomposition on the target IMF component to obtain a node coefficient set, determining a target basis based on a cost function of the node coefficient set, and performing asymmetric quantization processing on the node coefficients of the target basis to obtain a denoising signal; Performing energy enhancement processing on the denoising signal to obtain an enhanced signal, screening the peak with the highest energy value as a fault traveling wave head through dynamic single-peak detection based on the enhanced signal, and performing fault location based on the fault traveling wave head.

2. The method of fault location based on traveling wave signals according to claim 1, characterized in that, The adjustable amplitude noise is adaptively attenuated according to the residual energy of the decomposition process, specifically: Summing the square of the amplitude of each sampling point in the fault traveling wave signal to obtain the total energy of the signal; Summing the square of the amplitude of each sampling point in the residual signal in the decomposition process of the fault traveling wave signal to obtain the residual energy, and determining the adjustable amplitude noise based on the total energy of the signal and the residual energy.

3. The method of fault location based on traveling wave signals according to claim 1, characterized in that, The frequency domain windowing noise is converted in the wave head characteristic frequency band, specifically: Performing Fourier transform on the fault traveling wave signal to obtain a transform result, and calculating the power spectral density of the transform result; Determine the wave head characteristic frequency band based on the power spectral density, intercept the frequency spectrum in the wave head characteristic frequency band to generate matching noise, and convert the matching noise into the frequency domain windowing noise through inverse Fourier transform.

4. The method of fault location based on traveling wave signals as claimed in claim 1 wherein, The target IMF component is screened from the IMF component set based on the wave head significance index, specifically: Performing time domain analysis on the IMF component set to determine the maximum amplitude and standard deviation in the time domain, and calculating the power spectral density and target signal frequency of the IMF component set in the wave head characteristic frequency band; Based on the maximum amplitude in the time domain, the standard deviation, the power spectral density and the target signal frequency, the wave head significance index is calculated; According to the wave head significance index, the target IMF component is screened from the IMF component set.

5. The method of fault location based on traveling wave signals as claimed in claim 1 wherein, The target IMF component is decomposed by wavelet packet to obtain a node coefficient set, specifically: The target IMF component is taken as an input signal for layer-by-layer decomposition, and a wavelet packet tree is constructed layer by layer during the decomposition process until a preset decomposition layer number is reached, then the coefficients corresponding to all nodes in the wavelet packet tree are extracted to obtain the node coefficient set.

6. The method of fault location based on traveling wave signals according to claim 5, characterized in that, The target IMF component is taken as an input signal for layer-by-layer decomposition, and a wavelet packet tree is constructed layer by layer during the decomposition process, specifically: In the first layer decomposition, the input signal is respectively low-pass filtered and high-pass filtered to obtain low-frequency coefficients and high-frequency coefficients of the first layer, and in each subsequent layer decomposition, the low-frequency coefficients and high-frequency coefficients output by the previous layer are respectively low-pass filtered, high-pass filtered and down-sampled to obtain the low-frequency coefficients and high-frequency coefficients of the current layer, and a wavelet packet tree is constructed based on the low-frequency coefficients and high-frequency coefficients of all layers.

7. The method of fault location based on traveling wave signals according to any of claims 1-6, characterized in that, The cost function includes information entropy, L1 norm and kurtosis, and the cost function based on the node coefficient set determines the target base, specifically: The information entropy, L1 norm and kurtosis are calculated for each node in the node coefficient set respectively; The cost function is determined based on the information entropy, L1 norm and kurtosis, and the node with the minimum cost function is selected as the target base.

8. The method of fault location based on traveling wave signals according to any one of claims 1-6, characterized in that, The energy enhancement processing of the noise reduction signal to obtain an enhanced signal, specifically: The noise reduction signal is subjected to fractional order differentiation processing to obtain a first processing result; The time domain differential energy and time-frequency distribution of the first processing result are calculated, and the frequency domain gradient energy is calculated at the wave head frequency feature based on the time-frequency distribution, and the time domain differential energy and the frequency domain gradient energy are weighted and fused according to a preset weight to obtain a second processing result, wherein the wave head frequency feature is the characteristic frequency of the target IMF component; The analysis window is dynamically adjusted according to the energy curve of the second processing result to obtain an enhanced signal.

9. The method of claim 1-6, wherein, The dynamic single-peak detection based on the enhanced signal screens out the peak with the highest energy value as the fault traveling wave head, and the fault location is performed based on the fault traveling wave head, specifically: The dynamic energy threshold of the enhanced signal is calculated, and the dynamic energy threshold is compared with a preset energy curve to screen out wave head candidate points in the energy curve that are higher than the dynamic energy threshold; The peak with the highest energy value in the wave head candidate points is taken as the fault traveling wave head, and the time information of the fault traveling wave head is extracted and input into the traveling wave double-end distance measurement formula to calculate the fault location.

10. A fault location system based on travelling wave signals, characterized in that, It comprises: A collection module, a decomposition module, a processing module and a positioning module; The collection module is used to collect the fault traveling wave signal on the power line; The decomposition module is used to inject adjustable amplitude noise and frequency domain window noise into the fault traveling wave signal for iterative modal decomposition to obtain an IMF component set, wherein the adjustable amplitude noise is adaptively attenuated according to the residual energy of the decomposition process, and the frequency domain window noise is generated by matching the signal in the wave head characteristic frequency band; The processing module is used to screen out a target IMF component from the IMF component set based on a wave head saliency index, to perform wavelet packet decomposition on the target IMF component to obtain a node coefficient set, to determine a target base based on the cost function of the node coefficient set, and to perform asymmetric quantization processing on the node coefficients of the target base to obtain a noise reduction signal; The positioning module is configured to perform energy enhancement processing on the noise reduction signal to obtain an enhanced signal, perform dynamic single-peak detection based on the enhanced signal to select a peak with the highest energy value as a fault traveling wave front, and perform fault positioning based on the fault traveling wave front.

Images