Wave head calibration method and system of fault traveling wave, storage medium and electronic equipment

Abstract

The invention discloses a wave head calibration method and system for fault traveling waves, a storage medium and electronic equipment, and the method comprises the steps: obtaining fault traveling wave data of a detection point, and obtaining time-frequency distribution based on the fault traveling wave data; acquiring kurtosis values of different frequencies based on the time-frequency distribution, and selecting the frequency corresponding to the maximum kurtosis value as a target frequency; and performing energy demodulation on a time domain signal corresponding to the target frequency in the time frequency distribution to obtain an energy spectrum, and selecting a moment corresponding to a point with the maximum energy amplitude in the energy spectrum as a wave head arrival moment. According to the method, the time-varying characteristics of different frequencies of the traveling wave can be accurately captured by using the GLCT, and the frequency component which is most easy to calibrate is selected by using the kurtosis value, so that the interference of background noise is effectively inhibited, and the overall efficiency and precision of the algorithm are improved; the TEO can be used for rapidly capturing transient abrupt change in signals, the detection sensitivity of energy abrupt change points is enhanced, the wave head time domain characteristics are effectively enhanced, and the method is easy and convenient to use and high in adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of wave head calibration of distribution network fault traveling waves, and more specifically, to a wave head calibration method, system, storage medium and electronic equipment for fault traveling waves. Background Art

[0002] Considering the current state of research on accurate fault traveling wave head identification methods both domestically and internationally, the most widely used approach is time-frequency analysis, which converts the time-domain signal into a time-frequency signal. When the traveling wave head reaches the detection point, its transient mutation characteristics form a steep rising edge in the time-domain signal and trigger violent fluctuations in the frequency-domain energy. Joint time-frequency analysis can simultaneously capture the signal's mutation characteristics in both the time and frequency dimensions, thereby improving the accuracy of wave head location. Current mainstream time-frequency analysis methods include wavelet transform (WT), S-transform (ST), modal decomposition (such as VMD), and Wigner-Ville distribution (WVD).

[0003] In the detection of traveling wave heads in distribution networks, the mainstream time-frequency domain analysis methods have the following key problems: although the wavelet transform has a significant effect on the detection of non-singular signals, its basis function selection needs to strictly match the wide-band characteristics of the traveling wave signal (covering high-frequency transients to low-frequency steady-state components), and the analysis scale needs to be manually preset for different frequency domain ranges, resulting in insufficient adaptability and limited generalization performance; the S transform can optimize the time-frequency resolution by dynamically adjusting the Gaussian window parameters, but its time-frequency analysis is based on the assumption that the signal changes smoothly within a short time window, while the actual traveling wave head has significant non-stationary characteristics (such as high-frequency transients and rapid frequency jumps), which leads to the poor ability to capture high-frequency components. The method is limited and does not integrate a noise suppression mechanism, making it susceptible to on-site electromagnetic interference. Although empirical mode decomposition (EMD) can adaptively decompose traveling wave signals into multiple intrinsic mode functions (IMFs), modal aliasing will blur the sudden boundary characteristics of the traveling wave head, causing misjudgment of the wave head arrival time, which directly affects the fault location accuracy. Although variational mode decomposition (VMD) avoids modal aliasing by presetting constraints, its performance is highly dependent on the empirical setting of the number of decomposition layers and penalty factors. Faced with complex and changeable traveling wave signals (such as the superposition of attenuated oscillating waves and refracted waves), the lack of a parameter adaptive optimization mechanism can easily lead to the omission of characteristic component extraction. These methods have limited ability to capture the transient characteristics of non-stationary traveling wave signals, resulting in the accumulation of wave head positioning errors (up to hundreds of meters). These shortcomings seriously restrict the practical accuracy and reliability of traveling wave fault detection technology in complex distribution network environments.

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

[0005] The present invention provides a method, system, storage medium and electronic equipment for calibrating the wave head of a fault traveling wave, so as to solve the problem of how to accurately calibrate the wave head of a fault traveling wave.

[0006] In order to solve the above problem, according to one aspect of the present invention, a method for calibrating the wave head of a fault traveling wave is provided, the method comprising:

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

[0008] Obtaining kurtosis values of different frequencies based on the time-frequency distribution, and selecting the frequency corresponding to the maximum kurtosis value as the target frequency;

[0009] Energy demodulation is performed on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, and the time corresponding to the maximum energy amplitude point in the energy spectrum is selected as the wave head arrival time.

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

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

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

[0013] Preferably, performing a generalized linear frequency modulation wavelet transform (GLCT) process on the β-mode component to obtain a time-frequency distribution includes:

[0014] Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α;

[0015] Traverse and find the maximum value of GLCT transformation data GLCT(t,f,α) at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α:

[0016] The time-frequency distribution 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; is the demodulation operator; j is an imaginary number; F s is the sampling frequency; T s is the sampling time.

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

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

[0021] The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

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

[0023] a spectrum data acquisition unit, configured to acquire fault traveling wave data of a detection point and acquire a time-frequency distribution based on the fault traveling wave data;

[0024] a target frequency determination unit, configured 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 an energy spectrum, and select the time corresponding to the maximum energy amplitude point in the energy spectrum as the wave head arrival time.

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

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

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

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

[0030] Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α;

[0031] Traverse and find the maximum value of GLCT(t,f,α) of GLCT transformation data at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α′:

[0032] The time-frequency distribution 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; is the demodulation operator; j is an imaginary number; F s is the sampling frequency; T s is the sampling time.

[0035] Preferably, the frequency range of the 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 an energy spectrum, including:

[0037] The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

[0038] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the program implements any step of a method for calibrating the wave head of a fault traveling wave.

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

[0040] The computer-readable storage medium described above; and

[0041] One or more processors are configured to execute the program in the computer-readable storage medium.

[0042] The present invention provides a wave head calibration method, system, storage medium and electronic device for a fault traveling wave, comprising: obtaining fault traveling wave data of a detection point, and obtaining a time-frequency distribution based on the fault traveling wave data; obtaining kurtosis values of 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 a 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 wave head arrival time. The present invention utilizes the high-resolution time-frequency expression capability of GLCT to accurately capture the time-varying characteristics of traveling waves at different frequencies, and uses the kurtosis value to select the frequency component that is easiest to calibrate, effectively suppressing the interference of background noise, effectively reducing the computational burden of time-frequency analysis in traditional methods, and improving the overall efficiency and accuracy of the algorithm; utilizing the teager energy operator TEO, it can quickly capture transient mutations in the signal, enhance the sensitivity of energy mutation point detection, and effectively enhance the time domain characteristics of the wave head. It is easy to use and has strong adaptability. Combined with GLCT to construct a time-frequency-energy dual-dimensional detection framework, it breaks through the limitations of traditional detection methods, effectively realizes the accurate calibration of the arrival time of the traveling wave head, and solves the problems of low practical accuracy and reliability in existing traveling wave fault detection technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0044] Figure 1 Flowchart of a method 100 for calibrating the wave head of a fault traveling wave according to an embodiment of the present invention;

[0045] Figure 2 2. The fault traveling wave GLCT-TEO conversion process and result diagram when SNR=20db according to an embodiment of the present invention;

[0046] Figure 3 2. ...

[0047] Figure 4 Graphs showing wavelet transform results of fault traveling waves at different noise levels according to an embodiment of the present invention;

[0048] Figure 5 2 is a diagram showing the fault traveling wave HHT result when SNR=20 dB according to an embodiment of the present invention;

[0049] Figure 6 2 is a diagram showing the fault traveling wave HHT result when SNR=5dB according to an embodiment of the present invention;

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

[0051] Figure 8 2 is a waveform diagram of a noise-free fault traveling wave signal according to an embodiment of the present invention;

[0052] Figure 9 1 is a diagram showing a process and result of a traveling wave GLCT-TEO transformation in a noise-free fault state according to an embodiment of the present invention;

[0053] Figure 10 Schematic diagram of the structure of a fault traveling wave head calibration system 1000 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0055] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0056] Figure 1 FIG. 1 is a flow chart of a method 100 for calibrating the wave head of a fault traveling wave according to an embodiment of the present invention. Figure 1As shown, the fault traveling wave head calibration method provided by the embodiment of the present invention utilizes the high-resolution time-frequency expression capability of GLCT to accurately capture the time-varying characteristics of the traveling wave at different frequencies, and uses the kurtosis value to select the frequency component that is easiest to calibrate, effectively suppressing the interference of background noise, effectively reducing the computational burden of time-frequency analysis in traditional methods, and improving the overall efficiency and accuracy of the algorithm; utilizing the teager energy operator TEO to quickly capture transient mutations in the signal, enhance the sensitivity of energy mutation point detection, and effectively enhance the time domain characteristics of the wave head. It is easy to use and highly adaptable. Combining with GLCT to construct a time-frequency-energy dual-dimensional detection framework, it breaks through the limitations of traditional detection methods, effectively realizes the accurate calibration of the arrival time of the traveling wave head, and solves the problem of low practical accuracy and reliability in existing traveling wave fault detection technology. The fault traveling wave head calibration method 100 provided by the embodiment of the present invention starts at step 101. In step 101, the fault traveling wave data of the detection point is obtained, 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] Performing phase mode transformation on the fault traveling wave data to obtain a β mode component;

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

[0060] Preferably, performing a generalized linear frequency modulation wavelet transform (GLCT) process on the β-mode component to obtain a time-frequency distribution includes:

[0061] Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α;

[0062] Traverse and find the maximum value of GLCT transformation data GLCT(t,f,α) at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α:

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

[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; is the demodulation operator; j is an imaginary number; F s is the sampling frequency; T s is the sampling time.

[0066] In the present invention, the voltage traveling wave signal of the detection point is collected, 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 through generalized linear frequency modulation wavelet transform GLCT.

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

[0068] The linear frequency modulation wavelet transform expression can be written as:

[0069]

[0070] Where c is the modulation slope, and the corresponding rotation angle in the time-frequency plane is arctan(-c). LCT is essentially a Chirplet atom with a fixed modulation slope. However, when the signal has nonlinear frequency modulation characteristics, LCT with a fixed c value is difficult to accurately match the local modulation component, resulting in energy dispersion. To this end, GLCT further expands the fixed modulation slope in LCT into 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] Where Ts is the sampling time and Fs is the sampling frequency. Substituting them into the LCT expression, we get the mathematical form of GLCT:

[0073]

[0074] By discretizing the value of α, the chirplet atoms can be rotated by a corresponding fixed angle at the time-frequency distribution point. If α takes 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 and α = 0, the GLCT degenerates into the STFT. As N increases, the rotation angles of the chirplet atoms in the time-frequency plane increase, improving energy focusing but also increasing computational complexity. In practical applications, a trade-off between accuracy and efficiency must be struck by adjusting the value of N. The advantage of the GLCT lies in its ability to dynamically adjust α, allowing the chirplet atoms at each time-frequency point to approximate the local modulation component of the signal, thereby maximizing the amplitude and sharpening the energy concentration in the time-frequency distribution. The parameter N, window type, and window function must also be selected. Parameter N represents the number of rotation angles α. A larger N increases the likelihood that the calculated instantaneous frequency will coincide with the actual frequency, making it easier to achieve higher time-frequency resolution, but also increases the computational complexity. When N > 11, the time-frequency resolution does not improve significantly, but the computational cost increases, so N = 11 is the preferred choice.

[0077] The amplitudes of the time-frequency points (t, f) corresponding to different rotation angles α are different. The larger the amplitude, the higher the time-frequency aggregation, and the better it can represent the true time-frequency distribution of the signal. Therefore, in the present invention, the maximum value at different time-frequency points (t, f) can be obtained by traversing and obtaining the corresponding optimal rotation angle α′:

[0078]

[0079] Then the time-frequency distribution of the signal solved by GLCT can be obtained as:

[0080]

[0081] In step 102, kurtosis values of 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 the different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

[0083] In the present invention, after the spectrum data is acquired, the kurtosis value of each frequency in 100kHz-Fs / 2MHz is obtained, and the frequency with the largest kurtosis value is selected as the target frequency for wave head calibration.

[0084] Since the wave velocity variation of the line mode component in the high frequency band is small, the frequency band selection range is controlled within 100kHz-Fs / 2Hz in the present 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 wave head characteristics.

[0085] For a certain frequency signal x(t), the kurtosis value calculation process can be solved by formula (7). The larger the value, the more obvious the peak of the traveling wave.

[0086]

[0087] Where 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, energy demodulation is performed on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, and the time corresponding to the maximum energy amplitude point in the energy spectrum is selected as the wave head arrival time.

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

[0090] The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

[0091] In the present invention, the Teager Energy Operator (TEO) is used to perform energy demodulation on the time-domain signal at a selected frequency in the GLCT time-frequency matrix to obtain an energy spectrum, and the time corresponding to the maximum amplitude point in the energy spectrum is used as the wave head arrival time. The present invention uses the Teager Energy Operator (TEO) to perform energy demodulation on the time-domain signal at a selected frequency in the GLCT time-frequency matrix to further enhance the wave head recognition. The Teager Energy Operator is a nonlinear differential operator that can estimate the total energy required to generate the 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] Where, The first and second order derivatives of s(t) are obtained respectively; ψ[s(t)] is the Teager energy operator.

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

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

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

[0098] In the present invention, in the obtained TEO energy spectrum, the time corresponding to the maximum energy amplitude point is determined as the wave front arrival time, so as to achieve accurate positioning of the fault traveling wave front.

[0099] Under the same noise conditions, the proposed method was compared with the wavelet transform and Hilbert-Huang transform (HHT) commonly used in existing traveling wave head calibration. The accuracy of the proposed method was analyzed by superimposing white noise of varying intensities on the traveling wave signal and then processing the mixed signal using wavelet transform and HHT, respectively.

[0100] Figure 2 and Figure 3 The GLCT-TEO processing process and results are shown when the signal-to-noise ratio (SNR) is 20dB and 5dB, respectively. The theoretical values of the line mode wave velocity are calculated to be 299194289m / s and 299165771m / s. The measured arrival times obtained by TEO energy spectrum peak detection are 0.0833561s and 0.0833562s, respectively. The absolute errors compared with the theoretical values of 0.08335604s and 0.08335605s are maintained at the order of 0.06μs and 0.15μs, respectively.

[0101] Figure 4 This is the result of detecting the traveling wave head using wavelet transform. When SNR = 20dB, scale 1 contains many high-frequency components, with the highest noise content, making the wave head indistinguishable. At scale 2, the wave head is more distinct, and the calibrated wave head arrival time is 0.0833564s. Theoretically, scale 2 corresponds to a frequency range of 1.25-2.5MHz, and a wave velocity of 299003512m / s, corresponding to a theoretical arrival time of 0.083356s, with an error of 0.4us, approximately seven times that of the method proposed in this paper. When SNR = 5dB, scales 1 and 2 are both significantly affected by noise, so scale 3 is used for wave head calibration, with a calibrated wave head time of 0.0833566s. Theoretically, scale 3 corresponds to a frequency range of 0.625–1.25 MHz, and the wave velocity is 298,853,364 m / s, resulting in a theoretical arrival time of 0.08335606 s, with an error of 0.54 μs, approximately four times that of the proposed method. This indicates that in practical applications, the wavelet transform must consider the impact of the number of decomposition levels, and detection performance is poor under the influence of noise.

[0102] Figure 5 and Figure 6 is the result of Hilbert-Huang transform (HHT) detection of traveling wave head 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, the frequency range of IMF1 component is the highest, and the fault traveling wave information is the richest, but at the same time the influence of the noise component is also the most serious, but relatively speaking, the wave head can still be clearly identified. However, after Hilbert transform, Figure 5In (b), there are multiple mutations suspected to be the initial traveling waves, 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, a lot of noise is also mixed in IMF2, but the wave head is more obvious than that of IMF1 and IMF3. Therefore, the Hill wave head transform is performed on it, corresponding to Figure 6 (b), it can be seen that there are many mutation points before the wave head, making it difficult to calibrate the wave head.

[0104] It can be seen that the GLCT-TEO method proposed in the present 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 in strong noise interference, which has engineering significance.

[0105] In an embodiment of the present invention, the following is constructed in the PSCAD simulation software: Figure 7 The complex distribution network shown. Figure 7 There are 28 branches in total, consisting of overhead lines and cables. The specific lengths are shown in the figure. A fault traveling wave head calibration device is installed at the network terminal, with a sampling rate set to 10 MHz.

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

[0107] Combine Figure 8As can be seen from the propagation characteristics of voltage traveling waves, fault traveling waves exhibit typical multi-reflection characteristics during transmission: The fault traveling wave generated at the fault point propagates along the line at near-light speed, and the time delay in reaching the detection point is positively correlated with the distance from the fault. Due to line dispersion and the presence of impedance discontinuities, the initial traveling wave head experiences amplitude attenuation and waveform distortion during propagation, resulting in a time lag between the peak of the head captured at the detection point and the starting point of the sudden change. The longer the traveling wave travels, the greater the time lag, i.e., the greater the difference in arrival time between the high-frequency and low-frequency components. The initial head in the figure begins to rise at 0.0833559s and reaches its peak at 0.0833564s. When the traveling wave encounters impedance discontinuities, such as busbars and branch nodes, it undergoes reflection and transmission, forming a periodic sequence of subsequent head arrivals.

[0108] The process and results of GLCT-TEO transformation of fault traveling waves without noise are as follows: Figure 9 shown. Figure 9 (a) is the time-frequency diagram of the fault traveling wave signal generated by GLCT transformation. It can be seen that GLCT shows excellent time-frequency aggregation characteristics at most wave heads, and can accurately characterize the time-frequency distribution characteristics of the initial wave head of the fault traveling wave. Based on the time-frequency analysis results, the kurtosis index is further used to quantify the mutation intensity of different frequencies. The results are shown in Figure 2. Figure 9 (b) As shown. After screening the extreme value of kurtosis, 4.455MHz is determined to be the time-frequency signal with the most significant wave head characteristics, and its corresponding energy time domain distribution is as follows Figure 9 To further enhance the wave head recognition, the Teager Energy Operator (TEO) is used to demodulate the time-frequency signal of the selected target frequency. The obtained TEO energy spectrum is shown as follows: Figure 9 (d) The results show that after GLCT-TEO combined processing, the time domain characteristics of the wave head are significantly enhanced, with the energy peak corresponding to 0.0833561s. Combined with the theoretical propagation velocity of 299208168m / s for a 4.455MHz line mode, the theoretical arrival time should be 0.08335604s. The measured value deviates from the theoretical value by only 0.06μs. This error may be due to the measurement error (intercept error) of the measurement point spacing, verifying the effectiveness of the method within the engineering tolerance range.

[0109] Figure 10 FIG. 1 is a schematic structural diagram of a fault traveling wave head calibration system 1000 according to an embodiment of the present invention. Figure 10 As shown, the wave head calibration system 1000 of the fault traveling wave provided by the embodiment of the present 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 configured to acquire fault traveling wave data of a detection point, and acquire a time-frequency distribution based on the fault traveling wave data.

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

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

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

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

[0115] Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α;

[0116] Traverse and find the maximum value of GLCT(t,f,α) of GLCT transformation data at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α′:

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

[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 an imaginary number; Fs is the sampling frequency; Ts is the sampling time.

[0120] Preferably, the target frequency determination unit 1002 is configured 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 the 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 an energy spectrum, and select the moment corresponding to the maximum energy amplitude point in the energy spectrum as the wave head arrival moment.

[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 an energy spectrum, including:

[0124] The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

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

[0126] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the program implements any step of a method for calibrating the wave head of a fault traveling wave.

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

[0128] The computer-readable storage medium described above. And

[0129] One or more processors are configured to execute the program in the computer-readable storage medium.

[0130] The present invention has been described with reference to a few embodiments. However, it is apparent to those skilled in the art that other embodiments than the one disclosed above are equally within the scope of the present invention.

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

[0132] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function 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, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for calibrating the wave head of a fault traveling wave, characterized in that: The method comprises: Acquire fault traveling wave data of a detection point, and acquire a time-frequency distribution based on the fault traveling wave data; Obtaining kurtosis values of different frequencies based on the time-frequency distribution, and selecting the frequency corresponding to the maximum kurtosis value as the target frequency; Energy demodulation is performed on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, and the time corresponding to the maximum energy amplitude point in the energy spectrum is selected as the wave head arrival time.

2. The method according to claim 1, characterized in that Acquiring a time-frequency distribution based on the fault traveling wave data includes: Performing phase mode transformation on the fault traveling wave data to obtain a β mode component; The β-mode component is subjected to generalized linear frequency modulation wavelet transform (GLCT) processing to obtain time-frequency distribution.

3. The method according to claim 2, characterized in that Performing a generalized linear frequency modulation wavelet transform (GLCT) process on the β-mode component to obtain a time-frequency distribution, including: Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α; Traverse and find the maximum value of GLCT(t,f,α) of GLCT transformation data at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α′: The time-frequency distribution based on the optimal rotation angle α′ is: 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 an imaginary number; Fs is the sampling frequency; Ts is the sampling time.

4. The method according to claim 1, wherein The frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

5. The method according to claim 1, wherein Performing energy demodulation on a time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum includes: The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

6. A fault traveling wave head calibration system, characterized in that: The system comprises: a spectrum data acquisition unit, configured to acquire fault traveling wave data of a detection point and acquire a time-frequency distribution based on the fault traveling wave data; a target frequency determination unit, configured 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 an energy spectrum, and select the time corresponding to the maximum energy amplitude point in the energy spectrum as the wave head arrival time.

7. The system according to claim 6, characterized in that The spectrum data acquisition unit acquires the time-frequency distribution based on the fault traveling wave data, including: Performing phase mode transformation on the fault traveling wave data to obtain a β mode component; The β-mode component is subjected to generalized linear frequency modulation wavelet transform (GLCT) processing to obtain time-frequency distribution.

8. The system according to claim 7, characterized in that The spectrum data acquisition unit performs a generalized linear frequency modulation wavelet transform (GLCT) on the β-mode component to obtain a time-frequency distribution, including: Performing generalized linear frequency modulation wavelet transform (GLCT) processing on the β-mode component to obtain GLCT transformation data GLCT (t, f, α) of the time-frequency point (t, f) corresponding to different rotation angles α; Traverse and find the maximum value of GLCT transformation data GLCT(t,f,α) at different time-frequency points (t,f) and obtain the corresponding optimal rotation angle α: The time-frequency distribution based on the optimal rotation angle α′ is: 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 an imaginary number; Fs is the sampling frequency; Ts is the sampling time.

9. The system according to claim 6, wherein: The frequency range of different frequencies is 100kHz-Fs / 2Hz; Fs is the sampling frequency.

10. The system according to claim 6, wherein: The calibration unit performs energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum, including: The Teager energy operator is used to perform energy demodulation on the time domain signal corresponding to the target frequency in the time-frequency distribution to obtain an energy spectrum.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

12. An electronic device, characterized in that: include: The computer-readable storage medium of claim 11; as well as One or more processors are configured to execute the program in the computer-readable storage medium.

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