Methods, systems, storage media, and electronic devices for calibrating the wavefront of a fault traveling wave.

Abstract

This invention discloses a method, system, storage medium, and electronic device for wavefront calibration of a fault traveling wave. The method includes: acquiring fault traveling wave data at a detection point and obtaining a time-frequency distribution based on the fault traveling wave data; obtaining kurtosis values ​​at different frequencies based on the time-frequency distribution and selecting the frequency corresponding to the maximum kurtosis value as the target frequency; performing energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, and selecting the time corresponding to the point with the maximum energy amplitude in the energy spectrum as the wavefront arrival time. This invention utilizes GLCT to accurately capture the time-varying characteristics of different frequencies of the traveling wave and uses kurtosis values ​​to select the most easily calibrated frequency components, effectively suppressing background noise interference and improving the overall efficiency and accuracy of the algorithm. It also utilizes TEO to quickly capture transient changes in the signal, enhancing the detection sensitivity of energy change points and effectively enhancing the time-domain characteristics of the wavefront. The method is simple to use and highly adaptable.

Description

Technical Field

[0001] This invention relates to the field of fault traveling wave front calibration technology in power distribution networks, and more specifically, to a fault traveling wave front calibration method, system, storage medium, and electronic device. Background Technology

[0002] Based on the current research status of accurate traveling wave front identification methods both domestically and internationally, the most commonly used approach is time-frequency analysis, which transforms the time-domain signal into a time-frequency domain signal. When the traveling wave front reaches the detection point, its transient change characteristics create a steep rising edge in the time-domain signal, triggering drastic fluctuations in frequency-domain energy. By using joint time-frequency analysis, the abrupt changes in the signal's time and frequency dimensions can be captured simultaneously, thereby improving the accuracy of wavefront localization. Current mainstream time-frequency analysis methods include wavelet transform (WT), S-transform (ST), mode decomposition (such as VMD), and Wigner-Ville distribution (WVD).

[0003] In the detection of traveling wave fronts in distribution networks, mainstream time-frequency domain analysis methods suffer from the following key problems: While wavelet transform is effective for detecting non-singular signals, its basis function selection must strictly match the wideband characteristics of the traveling wave signal (covering high-frequency transients to low-frequency steady-state components). Different frequency ranges require manual preset analysis scales, resulting in insufficient adaptive capability and limited generalization performance. S-transform can optimize time-frequency resolution by dynamically adjusting Gaussian window parameters, but its time-frequency analysis is based on the assumption that the signal changes steadily within a short time window. However, actual traveling wave fronts have significant non-stationary characteristics (such as high-frequency transients and rapid frequency jumps), which affects the ability to capture high-frequency components. Limited by limitations and the lack of integrated noise suppression mechanisms, traveling wave fault detection technology is susceptible to electromagnetic interference in the field. While Empirical Mode Decomposition (EMD) can adaptively decompose traveling wave signals into multiple intrinsic mode functions (IMFs), mode aliasing can obscure the abrupt boundary characteristics of the traveling wave front, leading to misjudgments of wave front arrival time and directly affecting fault location accuracy. Variational Mode Decomposition (VMD), although avoiding mode aliasing through preset constraints, is highly dependent on the empirical setting of the number of decomposition layers and penalty factors. In complex and variable traveling wave signals (such as scenarios involving the superposition of attenuated oscillating waves and refracted waves), the lack of adaptive parameter optimization mechanisms can easily lead to missed feature component extraction. These methods have limited ability to capture the transient characteristics of non-stationary traveling wave signals, resulting in accumulated wave front location errors (up to hundreds of meters). These shortcomings severely restrict the practical accuracy and reliability of traveling wave fault detection technology in complex power distribution network environments.

[0004] Therefore, a new method and system for calibrating the wavefront of a fault traveling wave is needed. Summary of the Invention

[0005] This invention proposes a method, system, storage medium, and electronic device for calibrating the wavefront of a fault traveling wave, in order to solve the problem of how to accurately calibrate the wavefront of a fault traveling wave.

[0006] To address the aforementioned problems, according to one aspect of the present invention, a method for wavefront calibration of a fault traveling wave is provided, the method comprising:

[0007] Acquire fault traveling wave data at the detection point, and obtain the time-frequency distribution based on the fault traveling wave data;

[0008] Based on the time-frequency distribution, kurtosis values ​​at different frequencies are obtained, and the frequency corresponding to the maximum kurtosis value is selected as the target frequency.

[0009] The time-domain signal corresponding to the target frequency in the time-frequency distribution is demodulated to obtain the energy spectrum, and the time corresponding to the point with the maximum energy amplitude in the energy spectrum is selected as the wavefront arrival time.

[0010] Preferably, obtaining the time-frequency distribution based on the fault traveling wave data includes:

[0011] Perform phase mode transformation on the fault traveling wave data to obtain the β-mode component;

[0012] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution.

[0013] Preferably, the β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain the time-frequency distribution, including:

[0014] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain GLCT transform data GLCT(t,f,α) of time-frequency points (t,f) corresponding to different rotation angles α.

[0015] The maximum value of GLCT(t,f,α) at ​​different time-frequency points (t,f) is obtained by iterating through the data and obtaining the corresponding optimal rotation angle α:

[0016] The time-frequency distribution obtained based on the optimal rotation angle α′ is:

[0017]

[0018] Where t is time; f is frequency; h(τ) is the amplitude of the β-mode component time-domain signal at time τ; w(τ-t) is the time window; For demodulation operator; j is imaginary; F s T is the sampling frequency; s Sampling time.

[0019] Preferably, the frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

[0020] Preferably, the energy demodulation of the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum includes:

[0021] The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

[0022] According to another aspect of the present invention, a wavefront calibration system for a fault traveling wave is provided, the system comprising:

[0023] The spectrum data acquisition unit is used to acquire fault traveling wave data at the detection point and acquire time-frequency distribution based on the fault traveling wave data;

[0024] The target frequency determination unit is used to obtain kurtosis values ​​of different frequencies based on the time-frequency distribution, and select the frequency corresponding to the maximum kurtosis value as the target frequency.

[0025] The calibration unit is used to perform energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, and select the time corresponding to the point with the maximum energy amplitude in the energy spectrum as the wavefront arrival time.

[0026] Preferably, the spectrum data acquisition unit, which acquires the time-frequency distribution based on the fault traveling wave data, includes:

[0027] Perform phase mode transformation on the fault traveling wave data to obtain the β-mode component;

[0028] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution.

[0029] Preferably, the spectral data acquisition unit performs generalized linear frequency modulated wavelet transform (GLCT) processing on the β-mode component to obtain the time-frequency distribution, including:

[0030] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain GLCT transform data GLCT(t,f,α) of time-frequency points (t,f) corresponding to different rotation angles α.

[0031] The maximum value of GLCT(t,f,α) at ​​different time-frequency points (t,f) is obtained by iterating through the data and obtaining the corresponding optimal rotation angle α′:

[0032] The time-frequency distribution obtained based on the optimal rotation angle α′ is:

[0033]

[0034] Where t is time; f is frequency; h(τ) is the amplitude of the β-mode component time-domain signal at time τ; w(τ-t) is the time window; For demodulation operator; j is imaginary; F s T is the sampling frequency; s Sampling time.

[0035] Preferably, the frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

[0036] Preferably, the calibration unit performs energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, including:

[0037] The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

[0038] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the wavefront calibration methods for a fault traveling wave.

[0039] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0040] The aforementioned computer-readable storage medium; and

[0041] One or more processors for executing a program in the computer-readable storage medium.

[0042] This invention provides a method, system, storage medium, and electronic device for wavefront calibration of a fault traveling wave, comprising: acquiring fault traveling wave data at a detection point, and acquiring a time-frequency distribution based on the fault traveling wave data; acquiring kurtosis values ​​at different frequencies based on the time-frequency distribution, and selecting the frequency corresponding to the maximum kurtosis value as the target frequency; performing energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, and selecting the time corresponding to the point with the maximum energy amplitude in the energy spectrum as the wavefront arrival time. This invention utilizes the high-resolution time-frequency representation capability of GLCT to accurately capture the time-varying characteristics of traveling waves at different frequencies. By using kurtosis values ​​to select the most easily calibrated frequency components, it effectively suppresses background noise interference, significantly reduces the computational burden of time-frequency analysis in traditional methods, and improves the overall efficiency and accuracy of the algorithm. Furthermore, the teager energy operator (TEO) can quickly capture transient changes in the signal, enhancing the detection sensitivity of energy change points and effectively strengthening the wavefront time-domain characteristics. It is easy to use and highly adaptable. Combined with GLCT, it constructs a time-frequency-energy dual-dimensional detection framework, breaking through the limitations of traditional detection methods and effectively achieving accurate calibration of the traveling wavefront arrival time. This solves the problems of low practical accuracy and reliability in existing traveling wave fault detection technologies. Attached Figure Description

[0043] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0044] Figure 1 A flowchart of a fault traveling wave calibration method 100 according to an embodiment of the present invention;

[0045] Figure 2 The flowchart and result diagram of the fault traveling wave GLCT-TEO transformation when SNR=20dB according to an embodiment of the present invention are shown.

[0046] Figure 3 The flowchart and result diagram of the fault traveling wave GLCT-TEO transformation when SNR=5dB according to an embodiment of the present invention are shown.

[0047] Figure 4 The following are diagrams showing the fault traveling wavelet transform results under different noise levels according to an embodiment of the present invention.

[0048] Figure 5 This is a fault traveling wave HHT result diagram with SNR=20dB according to an embodiment of the present invention;

[0049] Figure 6 The above is a diagram of the fault traveling wave HHT results when SNR = 5dB according to an embodiment of the present invention.

[0050] Figure 7This is a schematic diagram of the structure of a complex 10kV distribution network according to an embodiment of the present invention;

[0051] Figure 8 This is a waveform diagram of a noiseless fault traveling wave signal according to an embodiment of the present invention;

[0052] Figure 9 The flowchart and result diagram of the noiseless fault traveling wave GLCT-TEO transformation according to an embodiment of the present invention are shown.

[0053] Figure 10 This is a schematic diagram of the structure of a fault traveling wave wave calibration system 1000 according to an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0055] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0056] Figure 1 This is a flowchart of a fault traveling wave wave front calibration method 100 according to an embodiment of the present invention. Figure 1As shown, the fault traveling wave wavefront calibration method provided by this invention utilizes the high-resolution time-frequency expression capability of GLCT to accurately capture the time-varying characteristics of different frequencies of the traveling wave, and uses kurtosis values ​​to select the most easily calibrated frequency components, effectively suppressing background noise interference, effectively reducing the computational burden of time-frequency analysis in traditional methods, and improving the overall efficiency and accuracy of the algorithm. The teager energy operator (TEO) can quickly capture transient changes in the signal, enhancing the detection sensitivity of energy change points, effectively enhancing the time-domain characteristics of the wavefront, and is simple to use and highly adaptable. Combined with GLCT, it constructs a time-frequency-energy dual-dimensional detection framework, breaking through the limitations of traditional detection methods, effectively realizing the accurate calibration of the arrival time of the traveling wave wavefront, and solving the problem of low practical accuracy and reliability in existing traveling wave fault detection technologies. The fault traveling wave wavefront calibration method 100 provided by this invention starts from step 101. In step 101, fault traveling wave data at the detection point is acquired, and the time-frequency distribution is obtained based on the fault traveling wave data.

[0057] Preferably, obtaining the time-frequency distribution based on the fault traveling wave data includes:

[0058] Perform phase mode transformation on the fault traveling wave data to obtain the β-mode component;

[0059] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution.

[0060] Preferably, the β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain the time-frequency distribution, including:

[0061] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain GLCT transform data GLCT(t,f,α) of time-frequency points (t,f) corresponding to different rotation angles α.

[0062] The maximum value of GLCT(t,f,α) at ​​different time-frequency points (t,f) is obtained by iterating through the data and obtaining the corresponding optimal rotation angle α:

[0063] The time-frequency distribution obtained based on the optimal rotation angle α is as follows:

[0064]

[0065] Where t is time; f is frequency; h(τ) is the amplitude of the β-mode component time-domain signal at time τ; w(τ-t) is the time window; For demodulation operator; j is imaginary; F s T is the sampling frequency; s Sampling time.

[0066] In this invention, the voltage traveling wave signal at the detection point is acquired, and the voltage traveling wave signal is subjected to phase mode transformation to obtain the phase mode component h(t), and its spectrum is obtained by generalized linear frequency modulated wavelet transform (GLCT).

[0067] Specifically, fault traveling wave data within a 200μs time window after the fault is captured, and phase mode transformation is performed on the captured traveling wave data to obtain the β-mode component. The phase mode component is then input into the GLCT, and a three-dimensional time-frequency distribution is output. The specific principle is as follows:

[0068] The expression for the linear frequency modulated wavelet transform can be written as:

[0069]

[0070] Where c is the modulation slope, corresponding to a rotation angle of arctan(-c) in the time-frequency plane. LCT is essentially a signal modulating a Chirplet atom with a fixed modulation slope. The inner product. However, when the signal has nonlinear frequency modulation characteristics, LCT with a fixed c value has difficulty accurately matching local modulation components, leading to energy dispersion. To address this, GLCT further extends the fixed modulation slope in LCT to a dynamic rotation parameter α. By discretizing the value of α, the optimal Chirplet atom can be adaptively selected at each time-frequency point. Specifically, the rotation angle α is defined as follows:

[0071]

[0072] In the formula, Ts is the sampling time and Fs is the sampling frequency. Substituting them into the LCT expression, we obtain the mathematical form of GLCT:

[0073]

[0074] Discretizing the value of α allows the chirplet atom to rotate by a fixed angle at the time-frequency distribution point. If α has N values, the time-frequency surface at the corresponding distribution point can be divided into N+1 equal parts, as shown below:

[0075]

[0076] When N=1, α=0, and GLCT degenerates into STFT. As N increases, the rotation angles of Chirplet atoms in the time-frequency plane increase, improving energy focusing, but also increasing computational complexity. In practical applications, a balance between accuracy and efficiency must be achieved by weighing the value of N. The advantage of GLCT lies in its ability to dynamically adjust α, allowing the Chirplet atoms at each time-frequency point to infinitely approximate the local modulation components of the signal, thereby maximizing amplitude and sharpening energy concentration in the time-frequency distribution. The parameters N, window type, and window function also need to be selected. Parameter N is the number of rotation angles α. The larger N is, the higher the probability that the calculated instantaneous frequency matches the actual frequency, making it easier to obtain higher time-frequency resolution, but the corresponding computational load will also increase. When N>11, the time-frequency resolution will not improve significantly, but the computational cost will continue to increase; therefore, N=11 is chosen.

[0077] The amplitudes of time-frequency points (t, f) corresponding to different rotation angles α differ. A larger amplitude indicates higher time-frequency clustering and better represents the true time-frequency distribution of the signal. Therefore, in this invention, the optimal rotation angle α′ can be obtained by iterating through and calculating the maximum value at different time-frequency points (t, f).

[0078]

[0079] Therefore, the time-frequency distribution of the signal obtained by GLCT solution can be:

[0080]

[0081] In step 102, kurtosis values ​​at different frequencies are obtained based on the time-frequency distribution, and the frequency corresponding to the maximum kurtosis value is selected as the target frequency.

[0082] Preferably, the frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

[0083] In this invention, after obtaining the spectrum data, the kurtosis value of each frequency in the range of 100kHz-Fs / 2MHz is calculated, and the frequency with the largest kurtosis value is selected as the target frequency for wavefront calibration.

[0084] Since the wave velocity of the line mode component changes little in the high-frequency band, the frequency band selection range is controlled within 100kHz-Fs / 2Hz in this invention, where Fs is the sampling frequency. The kurtosis index is calculated for different frequencies within the frequency band, and the frequency with the largest kurtosis value is selected as the frequency with the most obvious wavefront characteristics.

[0085] For a signal x(t) of a certain frequency, the kurtosis value can be calculated using equation (7). The larger the value, the more pronounced the peak of the traveling wave.

[0086]

[0087] In the formula, μ is the mean of x, σ is the standard deviation of x, and E(t) represents the expected value of t.

[0088] In step 103, the time-domain signal corresponding to the target frequency in the time-frequency distribution is demodulated to obtain the energy spectrum, and the time corresponding to the point with the maximum energy amplitude in the energy spectrum is selected as the wavefront arrival time.

[0089] Preferably, the energy demodulation of the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum includes:

[0090] The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

[0091] In this invention, the Teager energy operator (TEO) is used to demodulate the energy of the time-domain signal at a selected frequency in the GLCT time-frequency matrix, obtaining the energy spectrum. The time corresponding to the point of maximum amplitude in the energy spectrum is taken as the wavefront arrival time. This invention uses the Teager energy operator (TEO) to demodulate the energy of the time-domain signal at a selected frequency in the GLCT time-frequency matrix, further enhancing wavefront identification. The Teager energy operator is a nonlinear differential operator that can estimate the total energy required to generate a dynamic signal by analyzing the instantaneous energy value of the signal and its nonlinear combination, thereby highlighting the display of abnormal pulses in the signal.

[0092] For continuous signals, TEO is defined as:

[0093]

[0094] In the formula, These are the first and second derivatives of s(t), respectively; ψ[s(t)] is the Teager energy operator.

[0095] For a discrete signal s(n), equation (8) can be approximated as equation (9):

[0096] ψ[s(n)]=(s(n)) 2 -s(n+1)*s(n-1)(9)

[0097] In the formula, x(n) represents the discrete time signal sampling point, and ψ represents the energy value of the sampling point.

[0098] In this invention, the moment corresponding to the point of maximum energy amplitude in the obtained TEO energy spectrum is determined as the arrival time of the wavefront, thereby achieving precise positioning of the fault traveling wavefront.

[0099] Under the same noise conditions, the method of this invention is compared and simulated with wavelet transform and Hilbert-Huang transform (HHT) commonly used in existing traveling wave front calibration. White noise of different intensities is superimposed on the traveling wave signal, and then wavelet transform and HHT are used to process the mixed signal respectively to analyze the accuracy of the method of this invention.

[0100] Figure 2 and Figure 3 The GLCT-TEO processing procedures and results are shown for signal-to-noise ratio (SNR) of 20dB and 5dB, respectively. The theoretical values ​​calculated based on the linear mode wave velocity are 299,194,289 m / s and 299,165,771 m / s, respectively. The measured arrival times obtained by TEO energy spectrum peak detection are 0.0833561 s and 0.0833562 s, respectively. The absolute errors of these values ​​compared with the theoretical values ​​of 0.08335604 s and 0.08335605 s are maintained at the order of 0.06 μs and 0.15 μs, respectively.

[0101] Figure 4 The results are obtained by using wavelet transform to detect the traveling wave front. When SNR = 20 dB, scale 1 has numerous high-frequency components and the highest noise content, making it impossible to distinguish the wave front. For scale 2, the wave front is more obvious, and the calibrated wave front arrival time is 0.0833564 s. Theoretically, scale 2 corresponds to a frequency band of 1.25–2.5 MHz, and the wave acquisition velocity of 299003512 m / s corresponds to a theoretical arrival time of 0.083356 s, with an error of 0.4 μs, which is about 7 times that of the method proposed in this invention. When SNR = 5 dB, both scales 1 and 2 are interfered with by a large amount of noise. Scale 3 is used for wave front calibration, and the calibrated wave front time is 0.0833566 s. Theoretically, scale 3 corresponds to a frequency band of 0.625–1.25 MHz, and with a wave velocity of 298,853,364 m / s, the theoretical arrival time is 0.08335606 s, resulting in an error of 0.54 μs, approximately four times that of the method proposed in this invention. It can be seen that in practical applications, wavelet transform needs to consider the impact of the number of decomposition levels, and its detection performance is poor under noise conditions.

[0102] Figure 5 and Figure 6 The results are obtained by detecting the traveling wave front using the Hilbert-Huang Transform (HHT) under different noise conditions. Figure 5 (a) It can be seen that when SNR = 20dB, the frequency range of each IMF component obtained by EMD gradually decreases, with IMF1 having the highest frequency and the richest fault traveling wave information. However, at the same time, the influence of noise components is also the most severe, but the wavefront is still relatively easy to distinguish. However, after Hilbert transform, Figure 5(b) shows multiple mutations that are suspected to be the initial traveling wave, making it difficult to extract the effective arrival time of the initial traveling wave based on the HHT results.

[0103] observe Figure 6 (a) It can be seen that when SNR = 5dB, IMF2 also contains a large amount of noise, but compared with IMF1 and IMF3, the wavefront is more obvious. Therefore, a Hill wavefront transform is performed on it, corresponding to... Figure 6 (b) shows that there are many abrupt changes before the wavefront, making it difficult to calibrate the wavefront.

[0104] Therefore, it can be seen that the GLCT-TEO-based method proposed in this invention can accurately extract the fault feature information of the traveling wave signal when detecting the traveling wave signal under strong noise interference, and can effectively detect the traveling wave signal submerged under strong noise interference, which has engineering significance.

[0105] In an embodiment of the present invention, a system is built in the PSCAD simulation software as follows: Figure 7 The complex power distribution network shown. Figure 7 The network consists of 28 branch lines, composed of overhead lines and cables, with their specific lengths shown in the diagram. A fault traveling wavefront calibration device is installed at the network terminal, with the sampling rate set to 10MHz.

[0106] A single-phase ground fault with high resistance is set 200m away from point c on branch cd. The initial phase angle of the fault is 60°, the transition resistance is 1000Ω, the fault start time is 0.08333833s, and the duration is 0.001s. Taking measuring point A as an example, after the measured voltage signal is transformed by phase-mode conversion, its β-mode component is obtained. To separate the fault traveling wave signal, the β-mode component of the corresponding time period before the fault can be subtracted from the mode component of the time period after the fault. The waveform of the fault traveling wave is shown below, with a time window of 200us and a time window of 60us before the fault occurrence time. Figure 8 As shown, the time of the fault is marked by a red dashed line.

[0107] Combination Figure 8Based on the propagation characteristics of voltage traveling waves, fault traveling waves exhibit typical multi-reflection features during transmission: the fault traveling wave generated at the fault point propagates along the line at near the speed of light, and its arrival time at the detection point is positively correlated with the fault distance. Due to line dispersion effects and the existence of impedance discontinuities, the initial traveling wave front experiences amplitude attenuation and waveform distortion during propagation, resulting in a time lag between the peak value of the wavefront captured by the detection point and the abrupt change in direction. The greater the propagation distance, the greater the time lag, meaning a larger difference in arrival time between high-frequency and low-frequency components. In the figure, the initial wavefront rises from 0.0833559s, reaching its peak at 0.0833564s. When the traveling wave encounters impedance abrupt changes such as busbars or branch nodes, reflection and transmission phenomena occur, forming a periodically arriving sequence of subsequent wavefronts.

[0108] The process and results of performing GLCT-TEO transformation on the noise-free fault traveling wave are as follows: Figure 9 As shown. Figure 9 (a) is the time-frequency diagram generated by GLCT transform of the fault traveling wave signal. It can be seen that GLCT exhibits excellent time-frequency convergence characteristics at most wavefronts, accurately characterizing the time-frequency distribution features of the initial wavefront of the fault traveling wave. Based on the time-frequency analysis results, the intensity of abrupt changes at different frequencies is further quantified using the kurtosis index, as shown in the following figure. Figure 9 As shown in (b), after screening for kurtosis extrema, 4.455MHz was determined to be the time-frequency signal with the most significant wavefront characteristics, and its corresponding energy time-domain distribution is shown in Figure [Figure number missing]. Figure 9 As shown in (c). To further enhance wavefront identification, the Teager energy operator (TEO) is used to demodulate the time-frequency signal of the selected target frequency. The resulting TEO energy spectrum is shown in [image / data]. Figure 9 As shown in (d), the results show that the wavefront time-domain characteristics are significantly enhanced after GLCT-TEO joint processing, with the energy peak corresponding to a time of 0.0833561s. Based on the theoretical propagation speed of the 4.455MHz line mode (299208168m / s), the theoretical arrival time should be 0.08335604s, with the measured value deviating from the theoretical value by only 0.06μs. This error may originate from the measurement error (intercept error) between the measuring points, and the effectiveness of the method is verified within the allowable error range in engineering.

[0109] Figure 10 This is a schematic diagram of the wavefront calibration system 1000 for a fault traveling wave according to an embodiment of the present invention. Figure 10 As shown, the fault traveling wave wavefront calibration system 1000 provided in this embodiment of the invention includes: a spectrum data acquisition unit 1001, a target frequency determination unit 1002, and a calibration unit 1003.

[0110] Preferably, the spectrum data acquisition unit 1001 is used to acquire fault traveling wave data at the detection point and acquire time-frequency distribution based on the fault traveling wave data.

[0111] Preferably, the spectrum data acquisition unit 1001, which acquires the time-frequency distribution based on the fault traveling wave data, includes:

[0112] Perform phase mode transformation on the fault traveling wave data to obtain the β-mode component;

[0113] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution.

[0114] Preferably, the spectrum data acquisition unit 1001 performs generalized linear frequency modulated wavelet transform (GLCT) processing on the β-mode component to obtain the time-frequency distribution, including:

[0115] The β-mode component is subjected to generalized linear frequency modulated wavelet transform (GLCT) processing to obtain GLCT transform data GLCT(t,f,α) of time-frequency points (t,f) corresponding to different rotation angles α.

[0116] The maximum value of GLCT(t,f,α) at ​​different time-frequency points (t,f) is obtained by iterating through the data and obtaining the corresponding optimal rotation angle α′:

[0117] The time-frequency distribution obtained based on the optimal rotation angle α is as follows:

[0118]

[0119] Where t is time; f is frequency; h(τ) is the amplitude of the β-mode component time-domain signal at time τ; w(τ-t) is the time window; is the demodulation operator; j is the imaginary number; Fs is the sampling frequency; Ts is the sampling time.

[0120] Preferably, the target frequency determination unit 1002 is used to obtain kurtosis values ​​of different frequencies based on the time-frequency distribution, and select the frequency corresponding to the maximum kurtosis value as the target frequency.

[0121] Preferably, the frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

[0122] Preferably, the calibration unit 1003 is used to perform energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, and select the time corresponding to the point with the maximum energy amplitude in the energy spectrum as the wavefront arrival time.

[0123] Preferably, the calibration unit 1003 performs energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, including:

[0124] The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

[0125] The fault traveling wave wavefront calibration system 1000 of this invention corresponds to the fault traveling wave wavefront calibration method 100 of another embodiment of this invention, and will not be described again here.

[0126] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the wavefront calibration methods for a fault traveling wave.

[0127] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0128] The aforementioned computer-readable storage medium. And

[0129] One or more processors for executing a program in the computer-readable storage medium.

[0130] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0131] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

[0132] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for calibrating a wave head of a fault traveling wave, characterized in that, The method includes: Acquire fault traveling wave data at the detection point, and obtain the time-frequency distribution based on the fault traveling wave data; Based on the time-frequency distribution, kurtosis values ​​at different frequencies are obtained, and the frequency corresponding to the maximum kurtosis value is selected as the target frequency. The time-domain signal corresponding to the target frequency in the time-frequency distribution is demodulated to obtain the energy spectrum, and the time corresponding to the point with the maximum energy amplitude in the energy spectrum is selected as the wavefront arrival time. The acquisition of time-frequency distribution based on the fault traveling wave data includes: performing phase-mode transformation on the fault traveling wave data to obtain mode components; Regarding the The modulus components are processed by generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution. Among them, for the The modulus components are processed by Generalized Linear Frequency Modulated Wavelet Transform (GLCT) to obtain the time-frequency distribution, including: Regarding the Modulus components are processed using Generalized Linear Frequency Modulated Wavelet Transform (GLCT) to obtain different rotation angles. Corresponding time and frequency points GLCT transform data ; Traverse to find different time-frequency points GLCT transform data Find the maximum value and obtain the corresponding optimal rotation angle. for: ; Based on the optimal rotation angle The time-frequency distribution is obtained as follows: ， in, t For time; f For frequency; for Modulus component time-domain signal in time The amplitude at that time; For time windows; For demodulation operator; j is an imaginary number; The sampling frequency; Sampling time.

2. The method according to claim 1, characterized in that, The frequency range for different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

3. The method according to claim 1, characterized in that, The energy demodulation of the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum includes: The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

4. A wavefront calibration system for a fault traveling wave, characterized in that, The system includes: The spectrum data acquisition unit is used to acquire fault traveling wave data at the detection point and acquire time-frequency distribution based on the fault traveling wave data; The target frequency determination unit is used to obtain kurtosis values ​​of different frequencies based on the time-frequency distribution, and select the frequency corresponding to the maximum kurtosis value as the target frequency. The calibration unit is used to perform energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, and select the time corresponding to the point with the maximum energy amplitude in the energy spectrum as the wavefront arrival time. The spectrum data acquisition unit, which acquires the time-frequency distribution based on the fault traveling wave data, includes: Perform phase mode transformation on the fault traveling wave data to obtain Modulus component; Regarding the The modulus components are processed by generalized linear frequency modulated wavelet transform (GLCT) to obtain the time-frequency distribution. The spectrum data acquisition unit, for the... The modulus components are processed by Generalized Linear Frequency Modulated Wavelet Transform (GLCT) to obtain the time-frequency distribution, including: Regarding the Modulus components are processed using Generalized Linear Frequency Modulated Wavelet Transform (GLCT) to obtain different rotation angles. Corresponding time and frequency points GLCT transform data ; Traverse to find different time-frequency points GLCT transform data Find the maximum value and obtain the corresponding optimal rotation angle. for: ; Based on the optimal rotation angle The time-frequency distribution is obtained as follows: ， in, t For time; f For frequency; for Modulus component time-domain signal in time The amplitude at that time; For time windows; For demodulation operator; j is an imaginary number; The sampling frequency; Sampling time.

5. The system according to claim 4, characterized in that, The frequency range for different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

6. The system according to claim 4, characterized in that, The calibration unit performs energy demodulation on the time-domain signal corresponding to the target frequency in the time-frequency distribution to obtain the energy spectrum, including: The energy spectrum is obtained by using the Teager energy operator to demodulate the time-domain signal corresponding to the target frequency in the time-frequency distribution.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-3.

8. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 7; as well as One or more processors for executing a program in the computer-readable storage medium.

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