Traveling wave head extraction method and system based on short-time Fourier transform

Abstract

The invention discloses a traveling wave head extraction method and system based on short-time Fourier transform. The method comprises the following steps: acquiring a first voltage traveling wave signal; performing first preprocessing on the first voltage traveling wave signal by using a first window function to obtain a second voltage traveling wave signal; based on the second voltage traveling wave signal, a first spectrogram is obtained through first Fourier transform calculation; and on the basis of the first spectrogram, through first time-frequency analysis, capturing and extracting a transient traveling wave head after the fault. According to the invention, the fault information in the traveling wave can be effectively extracted.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and particularly to a method and system for extracting traveling wave heads based on short-time Fourier transform. Background Art

[0002] With the rapid economic development, as a key link directly connected to users, the requirements for the stability and reliability of the distribution network are gradually increasing. However, faults frequently occur in the distribution network, which are likely to cause power outages to users, resulting in economic losses, and even inducing serious accidents such as personal electric shock and forest fires. Efficient and reliable detection and rapid and accurate location of faults are helpful for troubleshooting, eliminating potential hazards and restoring power supply in a timely manner.

[0003] When a fault occurs on an overhead line, a suddenly changing voltage wave is instantaneously generated and propagates from the fault point to both ends of the line at a speed almost close to the speed of light, and even propagates to other lines on multi-terminal transmission lines. When the converter station contacts the fault wave, the corresponding capacitor starts to discharge, accompanied by a rapid drop in voltage and a rapid increase in fault current. This transient process transitions to a stable state after dozens of milliseconds. The process of generating fault traveling waves can be analyzed using the superposition theorem on the premise of omitting other interference factors. During a fault, it is equivalent to when the power source potential is zero, and a voltage suddenly appears at the fault point, which is the reverse voltage of the normal state voltage of the same magnitude. At this time, the fault current and voltage traveling waves we need will appear on the transmission line, both of which are transient components. They contain a large amount of useful fault information.

[0004] After a fault occurs in the distribution network, if the transient traveling wave head after the fault cannot be accurately captured and extracted, it will have a serious impact on the detection and location of the fault and even induce serious accidents such as electric shock and fire. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract and the title, and such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method and system for extracting traveling wave heads based on short-time Fourier transform to solve the problem that the existing methods cannot accurately capture and extract the transient traveling wave heads after a fault.

[0008] To solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a method for extracting the traveling wave head based on the short-time Fourier transform, including:

[0010] Obtain the first voltage traveling wave signal;

[0011] Perform a first preprocessing on the first voltage traveling wave signal using a first window function to obtain a second voltage traveling wave signal;

[0012] Based on the second voltage traveling wave signal, calculate through the first Fourier transform to obtain a first spectrogram;

[0013] Based on the first spectrogram, capture and extract the transient traveling wave head after the fault through the first time-frequency analysis.

[0014] As a preferred solution of the method for extracting the traveling wave head based on the short-time Fourier transform according to the present invention, wherein:

[0015] The obtaining of the voltage traveling wave signal includes determining whether the first voltage traveling wave signal is detected;

[0016] If the first voltage traveling wave signal is obtained, determine the corresponding window length and step size;

[0017] If the first voltage traveling wave signal is not obtained, enter the next time interval and continue the detection.

[0018] As a preferred solution of the method for extracting the traveling wave head based on the short-time Fourier transform according to the present invention, wherein:

[0019] The obtaining of the result of the short-time Fourier transform includes:

[0020] Perform the first Fourier transform on the second voltage traveling wave signal in each window using the fast Fourier transform algorithm to obtain a first spectrogram.

[0021] As a preferred solution of the method for extracting the traveling wave head based on the short-time Fourier transform according to the present invention, wherein:

[0022] The first time-frequency analysis includes

[0023] Determine the identification frequency;

[0024] The first window function moves the window according to the set step size;

[0025] Calculate the first Fourier transform again for the signal segment in the new window position;

[0026] Repeat the steps of applying the window function, calculating the first Fourier transform, and moving the window until the entire signal is processed to obtain a second spectrogram.

[0027] As a preferred solution of the traveling wave head extraction method based on short-time Fourier transform according to the present invention, wherein:

[0028] The determining of the recognition frequency includes

[0029] According to the window length, select a suitable recognition frequency in the second spectrogram based on the sampling frequency;

[0030] The selection of the recognition frequency is the middle value of the updated frequency components corresponding to the selected window length;

[0031] As a preferred solution of the traveling wave head extraction method based on short-time Fourier transform according to the present invention, wherein:

[0032] The first time-frequency analysis further includes

[0033] Analyze the second spectrogram, observe the amplitude change under different frequency components, and find the first feature related to the traveling wave head;

[0034] Utilize the first feature to capture and extract the transient traveling wave head after the fault.

[0035] As a preferred solution of the traveling wave head extraction method based on short-time Fourier transform according to the present invention, wherein:

[0036] Perform the first Fourier transform on the second voltage traveling wave signal in each window using the fast Fourier transform algorithm, and the obtained first spectrogram is expressed as:

[0037]

[0038] In the formula, N is the number of FFT points, n is the time-domain index of the input sample, h[n] is the input sample, g[n] is the window function, m is the position of g[n], h is the hop number between consecutive windows, and k is the frequency index.

[0039] In a second aspect, the present invention provides a traveling wave head extraction system based on short-time Fourier transform, including:

[0040] A signal acquisition module for acquiring the first voltage traveling wave signal;

[0041] A preprocessing module for performing the first preprocessing on the first voltage traveling wave signal using the first window function to obtain the second voltage traveling wave signal;

[0042] A Fourier transform calculation module for obtaining the first spectrogram through the first Fourier transform calculation based on the second voltage traveling wave signal;

[0043] A time-frequency analysis module for extracting the fault information in the traveling wave through the first time-frequency analysis based on the first spectrogram.

[0044] In a third aspect, the present invention provides a computing device, comprising:

[0045] a memory for storing programs;

[0046] a processor for executing the computer-executable instructions, which when executed by the processor implement the steps of the traveling wave head extraction method based on the short-time Fourier transform.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of the traveling wave head extraction method based on the short-time Fourier transform are implemented.

[0048] Advantages of the present invention: The present invention proposes a new traveling wave head extraction method. Based on the characteristic that the traveling wave caused by a fault contains rich fault information, a traveling wave head extraction method based on the short-time Fourier transform (STFT) is proposed. This method uses the short-time Fourier transform (STFT) to extract the traveling wave head, directly samples the time series signal using a window function, selects the Hann window function as the window function, and determines the window length at the same time. The window function is applied to the input VTW signal and the discrete Fourier transform (DFT) is performed on the sampled signal. In addition, by reducing the window length and the step size, the time resolution is improved. At the same time, in the short-time Fourier transform spectrum, since the amplitude distortion in the high-frequency range is relatively serious, the selected frequency for identification should be the middle value of the updated frequency components corresponding to the selected window length. Compared with the traditional traveling wave method, this method eliminates the low-frequency oscillation of the voltage 0-mode component, can effectively distinguish the traveling wave peak value, and reduces the calculation amount. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0050] Figure 1 is a schematic diagram of the basic process of a traveling wave head extraction method based on the short-time Fourier transform provided by an embodiment of the present invention;

[0051] Figure 2 is a flowchart of a traveling wave head extraction method based on the short-time Fourier transform (STFT) of a traveling wave head extraction method based on the short-time Fourier transform provided by an embodiment of the present invention;

[0052] Figure 3TW position map of PG fault for a traveling wave head extraction method based on short-time Fourier transform provided by an embodiment of the present invention;

[0053] Figure 4 STFT response map of PG fault for a traveling wave head extraction method based on short-time Fourier transform provided by an embodiment of the present invention;

[0054] Figure 5 TW position map of PP fault for a traveling wave head extraction method based on short-time Fourier transform provided by an embodiment of the present invention;

[0055] Figure 6 STFT response map of PP fault for a traveling wave head extraction method based on short-time Fourier transform provided by an embodiment of the present invention. Detailed implementation manners

[0056] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0058] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0059] The present invention is described in detail in conjunction with the schematic diagrams. When detailing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the protection scope of the present invention here. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0060] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0061] Unless otherwise clearly specified and limited in the present invention, the terms "installation, connection, and coupling" shall be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0062] Embodiment 1

[0063] Referring to Figure 1 , which is an embodiment of the present invention, a method for extracting traveling wave heads based on short-time Fourier transform is provided, including:

[0064] S1: Obtain the first voltage traveling wave signal;

[0065] S2: Perform a first preprocessing on the first voltage traveling wave signal using the first window function to obtain the second voltage traveling wave signal;

[0066] S3: Based on the second voltage traveling wave signal, calculate through the first Fourier transform to obtain the first spectrogram;

[0067] S4: Based on the first spectrogram, capture and extract the transient traveling wave head after the fault through the first time-frequency analysis.

[0068] It should be noted that the above steps together constitute a method for extracting traveling wave heads based on short-time Fourier transform (STFT). This method performs segmented processing and frequency-domain analysis on the voltage traveling wave signal, generates a time-frequency representation (STFT spectrogram), and uses this spectrogram to identify and extract the position of the traveling wave head. This method is particularly suitable for fault detection scenarios that require high time and frequency resolution.

[0069] Embodiment 2

[0070] Referring to Figure 2 , which is an embodiment of the present invention, based on the previous embodiment, a method for extracting traveling wave heads based on short-time Fourier transform is provided, including:

[0071] In the embodiment of the present application, the first voltage traveling wave signal in step S1 includes a discrete time series signal;

[0072] In the embodiment of the present application, the first preprocessing of the first voltage traveling wave signal using the first window function in step S2 includes using a Hanning window function to localize the voltage traveling wave signal (VTW) to obtain a windowed signal segment, that is, the second voltage traveling wave signal.

[0073] In the embodiment of the present application, the first preprocessing in step S2 includes, in the short-time Fourier transform (STFT), by segmenting the signal and applying a window function to the signal in each time period for weighting processing, so as to retain time and frequency information. The main purpose of the localization processing is to perform spectral analysis in each time period, while reducing spectral leakage and improving frequency resolution.

[0074] It should be noted that this is the first step of the whole process. The purpose is to localize the original signal so that only a small segment of the signal is analyzed each time, thus achieving a balance between the time domain and the frequency domain. Directly performing a Fourier transform on the entire signal will lose time information, while using a window function can divide the signal into multiple small segments, and local spectral analysis is performed on each small segment.

[0075] In an alternative embodiment, the first window function can be a Hanning window function, a rectangular window function, or a Blackman window function;

[0076] In an alternative embodiment, the Hanning window function includes segmenting and weighting the input voltage traveling wave signal to reduce spectral leakage and optimize the STFT spectrogram, thereby accurately extracting the transient traveling wave head after a fault.

[0077] In an alternative embodiment, the rectangular window function includes segmenting the input voltage traveling wave signal, but due to its unweighted characteristic, the spectral leakage is large, thus affecting the frequency resolution of the short-time Fourier transform (STFT) and the accurate extraction of the transient traveling wave head.

[0078] In an alternative embodiment, the Blackman window function includes segmenting and weighting the input voltage traveling wave signal, and improving the frequency resolution by significantly reducing spectral leakage, but its wide main lobe may reduce the time resolution of the short-time Fourier transform (STFT), thereby affecting the accurate extraction of the transient traveling wave head.

[0079] It should be noted that the traveling wave in the voltage signal is characterized by the amplitude distortion of the updated frequency bin. In this study, the Hanning window function is selected as the first window function to reduce spectral leakage.

[0080] In the embodiment of the present application, in step S3, through the first Fourier transform calculation, the first spectrogram is obtained, which includes performing a discrete Fourier transform (DFT) on the windowed signal segment to obtain the spectral information within this time period. This step can be implemented by the fast Fourier transform (FFT) algorithm to improve the calculation efficiency.

[0081] In an alternative embodiment, the first Fourier transform can be the fast Fourier transform (FFT), can be the Hilbert-Huang transform, or can also be the S transform (S-Transform);

[0082] In an alternative embodiment, the fast Fourier transform (FFT) includes performing a discrete Fourier transform on the windowed input voltage traveling wave signal to generate a high-resolution spectrogram, thereby accurately extracting the transient traveling wave head after the fault.

[0083] In an alternative embodiment, the Hilbert-Huang transform includes performing empirical mode decomposition and Hilbert transform on the input voltage traveling wave signal to generate a time-frequency spectrogram adapted to nonlinear and non-stationary characteristics, thereby accurately extracting the transient traveling wave head after the fault.

[0084] In an alternative embodiment, the S transform includes performing time-frequency analysis on the input voltage traveling wave signal to generate a time-frequency spectrogram that accurately describes the variation of the signal frequency characteristics over time, so as to effectively detect and analyze the transient traveling wave characteristics after the fault.

[0085] It should be noted that although the Hilbert-Huang transform has advantages in dealing with non-stationary signals, their computational complexity is usually high, especially in the case of multi-scale analysis. Although the S transform has advantages in time-frequency localization, its frequency resolution is not as good as that of the FFT, especially in the high-frequency range. The present invention uses the fast Fourier transform (FFT) to be able to provide higher computational efficiency and a simpler implementation method while ensuring high frequency resolution, thereby efficiently generating a spectrogram and accurately extracting the transient traveling wave head after the fault.

[0086] It should be noted that using these windowed signal segments, their spectra are calculated through DFT. In this way, a spectrogram can be obtained at each time position m, that is, the first spectrogram.

[0087] In the embodiment of the present application, the first time-frequency analysis in step S4 includes moving the position of the window function forward by a certain distance according to the set step size "H" to form a new window position. Applying the window function again at the new window position and calculating the DFT. These steps are continuously repeated until the window function covers the entire signal.

[0088] It should be noted that in order to analyze the entire signal, we need to slide the window along the time axis, moving one step length H each time, and repeat the operation in the second step at the new window position. In this way, the time-frequency representation of the entire signal, that is, the second spectrogram (STFT spectrogram), can be obtained.

[0089] In the embodiment of the present application, the first time-frequency analysis in step S4 further includes determining the recognition frequency;

[0090] In the embodiment of the present application, determining the recognition frequency includes selecting the updated frequency component as the recognition frequency in the STFT spectrogram. In the spectrogram, a suitable frequency component is selected as the recognition frequency. These frequency components are usually related to the characteristics of the traveling wave, and selecting the median value can avoid the problem of amplitude distortion in the high-frequency range.

[0091] In the embodiment of the present application, the first time-frequency analysis in step S4 further includes analyzing the STFT spectrogram to find significant features, especially those updated frequency components related to the characteristics of the traveling wave. Using these recognition frequencies, further analysis is performed in the STFT spectrogram. By different analysis methods (such as peak detection, energy change detection, etc.), the position of the traveling wave head is found.

[0092] It should be noted that the basic principle of the short-time Fourier transform (STFT) includes:

[0093] The short-time Fourier transform calculates the Fourier transform of the function h(t) on the real symmetric window function g(t), which is translated in time by τ and modulated in frequency by f. The expression of the continuous-domain STFT is:

[0094]

[0095] where h(t) is the original signal and g(t) is the window function.

[0096] In actual use, the signal is sampled at a fixed sampling frequency, and the discrete Fourier transform (DFT) is calculated based on the fast Fourier transform (FFT) algorithm. The STFT in the discrete domain is modified as follows:

[0097]

[0098] In the formula, N is the number of FFT points, n is the time domain index of the input sample, h[n] is the input sample, g[n] is the window function, m is the position of g[n], h is the number of jumps between consecutive windows, and k is the frequency index. For the STFT in the discrete domain, the time resolution of the STFT is

[0099] It should be noted that the dependent parameters of the STFT include:

[0100] (1) Sampling frequency (f s ): A higher sampling frequency can improve the time resolution and frequency resolution of the short-time Fourier transform (STFT). In addition, to accurately obtain the fault signal with high time resolution, it is necessary to use a high sampling frequency to obtain sufficient time resolution and frequency resolution, as well as accurate traveling wave heads.

[0101] (2) Input samples (n): Determined by the length of the general window function. Therefore, high input samples will result in a large window size and high spectral resolution.

[0102] (3) Total number of points of the fast Fourier transform (FFT) (N): Increasing N will make the Fourier transform of the input signal closer to the continuous Fourier transform (CFT) and improve the frequency resolution of the STFT spectrum output. However, a higher N will increase the computational burden.

[0103] (4) Window function type (g[n]): Common window functions include rectangular window, triangular window, Hanning window, Hamming window, Blackman window, and Kaiser window, etc. In an ideal window function, its main lobe in the spectrum should be narrow to obtain high frequency resolution, and its sidelobe gain should decay rapidly.

[0104] (5) Step size (H): The time resolution can be improved by reducing the step size H, and high time resolution is necessary for obtaining accurate fault location estimation results.

[0105] It should be noted that when a fault occurs in the distribution network, it is equivalent to the power supply potential being zero, and a voltage suddenly appears at the fault point, which is the reverse voltage of the normal state voltage of the same magnitude. At this time, fault current and voltage traveling wave transient components will appear on the transmission line, which contain a large amount of useful fault information. Reasonably using the fault information in the traveling wave signal can achieve accurate fault location, and the accuracy of traveling wave head extraction directly affects the accuracy of the traveling wave location method. After a fault occurs in the distribution network, if the transient traveling wave head after the fault cannot be accurately captured and extracted, it will have a serious impact on the detection and location of the fault and even induce serious accidents such as electric shock and fire. Therefore, a wave head extraction method based on the short-time Fourier transform (STFT) is proposed to effectively extract the fault information in the traveling wave.

[0106] In the embodiment of the present application, when the transmission line is regarded as a uniform parameter, that is, it is assumed that the line is lossless, the influence of line resistance and leakage conductance on the line is omitted, and the voltage traveling wave and current of the unit length circuit can be represented by the partial differential equation with variables of position x and time t as follows:

[0107]

[0108] In the formula, C0 represents the capacitance to ground per unit length of the lossless line, and L0 represents the inductance to ground per unit length of the lossless line.

[0109] Differentiating and transforming the above equation with respect to \(x\) and \(f\) respectively, the traveling wave equation is obtained:

[0110]

[0111] The d'Alembert solution obtained from the above equation is:

[0112] \(u = u_1(t - x / v)+u_2(t + x / v)\)

[0113]

[0114] where \(u_1(t - x / v)\) is the forward traveling wave voltage propagating in the positive \(x\) direction, and \(u_2(t + x / v)\) is the backward traveling wave voltage propagating in the negative \(x\) direction. is the wave impedance of the lossless transmission line. is the propagation speed of the traveling wave.

[0115] In the embodiment of the present application, the transmission line can be approximated as a lossless line. Without considering the parameter frequency correlation, the formulas for the wave velocities \(v_1\) and \(v_0\) of the line mode and zero mode components of the transmission line are as follows:

[0116]

[0117] where \(L_1\) is the positive sequence inductance per unit length of the line, \(C_1\) is the positive sequence capacitance per unit length of the line, \(L_0\) is the zero sequence inductance per unit length of the line, and \(C_0\) is the zero sequence capacitance per unit length of the line.

[0118] In the embodiment of the present application, during the forward movement of the voltage traveling wave on the transmission line, the distributed capacitance of the line is continuously charged and a current traveling wave accompanying the forward movement is generated. The relationship between the voltage traveling wave and the current traveling wave can be described by the wave impedance \(Z\) (also known as the characteristic impedance).

[0119] The wave impedance of the transmission line is as follows:

[0120]

[0121] The forward voltage wave \(U\) + and the current wave \(I\) + should satisfy the following relationship:

[0122] \(U\) + / \(I\) + =\(Z\)

[0123] For the relationship between the backward voltage wave \(U\) - and the current wave \(I\) - , there is:

[0124]

[0125] From the above equations, it can be concluded that the forward voltage wave has the same polarity as the current wave, and the reverse voltage wave has the opposite polarity to the current wave.

[0126] In the embodiment of the present application, the short-time Fourier transform is used to process the traveling wave, and the specific method includes the following steps:

[0127] Step 1: g[n] is applied to the input voltage traveling wave VTW signal, and the discrete Fourier transform is calculated accordingly.

[0128] Step 2: Select the Hanning window function as the window function to reduce spectral leakage.

[0129] Step 3: After the calculation, the window function advances in steps of "H", and the DFT is calculated again, and this process is repeated.

[0130] It should be noted that the traveling wave in the voltage signal is characterized by the amplitude distortion of the updated frequency bin. The present invention selects the Hanning window function as the window function to reduce spectral leakage.

[0131] In the embodiment of the present application, for the Hanning window function, the updated frequency components will appear at and so on. Because

[0132] Therefore, in order to better observe the updated frequency components in the (STFT) spectrum, the window length is usually 8 - 16, the sampling frequency is set to 100 kHz. When the window length is 8, They are 25000 Hz, 37500 Hz, 50000 Hz respectively. When the updated frequency is 37500 Hz, the distortion of the traveling wave characteristics in the updated frequency bin is the most obvious. As the window length increases, the time resolution decreases, and it is not easy to observe the traveling wave characteristics, so the window length is 8.

[0133] In the embodiment of the present application, when the STFT spectrum is applied to fault detection, the updated frequency component will be selected as the identification frequency. At the same time, in the short-time Fourier transform spectrum, due to the relatively serious amplitude distortion in the high-frequency range, the selection of the identification frequency should be the middle value of the updated frequency components corresponding to the selected window length.

[0134] In the embodiment of the present application, the traveling wave head extraction technology is often applied to the field of fault location. The traveling wave fault location method can be studied from different angles. According to the different sources of traveling wave information, it can be divided into the single-ended traveling wave method and the double-ended traveling wave method. The double-ended method is less affected by the transition resistance and the line distributed capacitance, and there is no problem of identifying the single-ended reflected wave. Therefore, the double-ended method is more reliable than the single-ended method, and the double-ended ranging method is mainly used in practical applications.

[0135] This embodiment also provides a traveling wave head extraction system based on the short-time Fourier transform, including:

[0136] A signal acquisition module for acquiring a first voltage traveling wave signal;

[0137] A preprocessing module for performing first preprocessing on the first voltage traveling wave signal using a first window function to obtain a second voltage traveling wave signal;

[0138] A Fourier transform calculation module for obtaining a first spectrogram through first Fourier transform calculation based on the second voltage traveling wave signal;

[0139] A time-frequency analysis module for extracting fault information in the traveling wave through first time-frequency analysis based on the first spectrogram.

[0140] Furthermore, it further includes:

[0141] A memory for storing programs;

[0142] A processor for loading the program to execute the traveling wave head extraction method based on the short-time Fourier transform.

[0143] This embodiment also provides a computer-readable storage medium storing a program, which when executed by a processor, implements the traveling wave head extraction method based on the short-time Fourier transform.

[0144] The storage medium proposed in this embodiment and the traveling wave head extraction method based on the short-time Fourier transform proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0145] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0146] Embodiment 3

[0147] This is an embodiment of the present invention, which provides a traveling wave head extraction system based on short-time Fourier transform, including a signal acquisition module, a preprocessing module, a Fourier transform calculation module, and a time-frequency analysis module;

[0148] In the embodiment of the present application, the signal acquisition module includes acquiring a first voltage traveling wave signal; acquiring the original first voltage traveling wave signal from a sensor or a data acquisition system. The original voltage traveling wave signal provided by the sensor or the data acquisition system. To ensure the integrity and accuracy of the signal, preliminary filtering or denoising processing may be required.

[0149] In the embodiment of the present application, the preprocessing module includes performing a first preprocessing on the first voltage traveling wave signal using a first window function to obtain a second voltage traveling wave signal; selecting a suitable window function and setting appropriate window length and step size to balance time and frequency resolution.

[0150] In the embodiment of the present application, the Fourier transform calculation includes obtaining a first spectrogram based on the second voltage traveling wave signal through a first Fourier transform calculation; using the FFT algorithm to improve the calculation efficiency and setting a suitable number of FFT points N to balance the calculation complexity and the spectral resolution.

[0151] In the embodiment of the present application, the time-frequency analysis module includes capturing and extracting the transient traveling wave head after the fault based on the first spectrogram through a first time-frequency analysis. Using multiple time-frequency analysis methods to accurately capture the traveling wave head and selecting a suitable identification frequency to avoid the amplitude distortion problem in the high-frequency range.

[0152] These modules together constitute a complete traveling wave head extraction method based on short-time Fourier transform (STFT), ensuring high precision and reliability in fault detection and location.

[0153] Embodiment 4

[0154] Referring to Figures 3 - 6 , this is an embodiment of the present invention, which provides a traveling wave head extraction method based on short-time Fourier transform. To verify its beneficial effects, the comparison results of two schemes are provided.

[0155] Figure 3 The STFT responses of TW during PG faults under different conditions at the moment before the traveling wave arrives and at the moment when the traveling wave arrives are compared. The moment before arrival represents the moment before VTW arrives, and the moment of arrival represents the moment when VTW arrives. The fault location is at 10 km, Figure 3 The transition resistance in (a) is 1 Ω, Figure 3 (b) is 100 Ω.

[0156] Figure 4Shows the time-varying STFT amplitude of the 0-mode VTW signal measured at both ends during a PG fault. As shown, the first TW wavefront is different, and a time difference can be obtained to estimate the fault location regardless of the fault level and fault distance. The fault location is at 10 km, Figure 4 The transition resistance in (a) is 1 Ω, Figure 4 and in (b) it is 100 Ω.

[0157] Figure 5 Compares the STFT responses of TW during a PP fault under different conditions at the moment before and when the traveling wave arrives. As shown, when the TW wavefront arrives, the amplitude distortion is severe at the updated frequency bins. The fault location is at 10 km, Figure 5 The transition resistance in (a) is 1 Ω, Figure 5 and in (b) it is 100 Ω.

[0158] Figure 6 Shows the time-varying STFT amplitude of the 0-mode VTW signal measured at both ends during a PG fault. As shown, the first TW wavefront is different, and a time difference can be obtained to estimate the fault location regardless of the fault level and fault distance. The fault location is at 10 km, Figure 6 The transition resistance in (a) is 1 Ω, Figure 6 and in (b) it is 100 Ω.

[0159] From the above experimental results, it can be seen that when the TW wavefront arrives, the amplitude distortion is relatively severe at the updated frequency bins. At the same time, the fault location results are not affected by the fault conditions and the transition resistance.

[0160] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for extracting the traveling wave head based on short-time Fourier transform, characterized in that Including: Obtain a first voltage traveling wave signal; Perform a first preprocessing on the first voltage traveling wave signal using a first window function to obtain a second voltage traveling wave signal; Based on the second voltage traveling wave signal, through the calculation of the first Fourier transform, obtain a first spectrogram; Based on the first spectrogram, through the first time-frequency analysis, capture and extract the transient traveling wave head after the fault.

2. The method for extracting traveling wave head based on short-time Fourier transform according to claim 1, characterized in that: The obtaining of the voltage traveling wave signal includes determining whether a first voltage traveling wave signal is detected; If the first voltage traveling wave signal is obtained, determine the corresponding window length and step size; If the first voltage traveling wave signal is not obtained, enter the next time interval and continue the detection.

3. The method for extracting traveling wave head based on short-time Fourier transform according to claim 1 or 2, characterized in that: The calculation through the first Fourier transform includes: Use the fast Fourier transform algorithm to perform the first Fourier transform on the second voltage traveling wave signal in each window to obtain a first spectrogram.

4. The method for extracting traveling wave head based on short-time Fourier transform according to claim 3, wherein: The first time-frequency analysis includes the following steps: Determine the identification frequency; The first window function moves the window according to the set step size; Calculate the first Fourier transform again for the signal segment in the new window position; Repeat the steps of applying the window function, calculating the first Fourier transform, and moving the window until the entire signal is processed to obtain a second spectrogram.

5. The method for extracting traveling wave head based on short-time Fourier transform according to claim 4, characterized in that: The determination of the identification frequency includes: According to the window length, select a suitable identification frequency in the second spectrogram based on the sampling frequency; The selection of the identification frequency is the middle value of the updated frequency components corresponding to the selected window length.

6. The method for extracting traveling wave head based on short-time Fourier transform according to claim 5, wherein: The first time-frequency analysis further includes: Analyze the second spectrogram, observe the amplitude change under different frequency components, and find the first feature related to the traveling wave head; Utilize the first feature to capture and extract the transient traveling wave head after the fault.

7. The method for extracting traveling wave head based on short-time Fourier transform according to claim 6, characterized in that: The use of the fast Fourier transform algorithm to perform the first Fourier transform on the second voltage traveling wave signal in each window to obtain a first spectrogram is expressed as: In the formula, N is the number of FFT points, n is the time domain index of the input sample, h[n] is the input sample, g[n] is the window function, m is the position of g[n], h is the jump number between consecutive windows, and k is the frequency index.

8. A system for the traveling wave head extraction method based on short-time Fourier transform according to claim 1, characterized in that: A signal acquisition module for acquiring a first voltage traveling wave signal; A preprocessing module for performing a first preprocessing on the first voltage traveling wave signal using a first window function to obtain a second voltage traveling wave signal; A Fourier transform calculation module for obtaining a first spectrogram through the calculation of the first Fourier transform based on the second voltage traveling wave signal; A time-frequency analysis module for extracting fault information in the traveling wave through the first time-frequency analysis based on the first spectrogram.

9. A computing device, characterized in that, Including: A memory for storing programs; A processor for loading the program to execute the steps of the traveling wave head extraction method based on short-time Fourier transform according to any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, the steps of the traveling wave head extraction method based on short-time Fourier transform according to any one of claims 1-7 are implemented.

Images