Traveling wave sampling rate enhancing method suitable for traveling wave distance measuring device

Abstract

The invention relates to a traveling wave sampling rate enhancement method suitable for a traveling wave distance measurement device, and the method comprises the steps: carrying out the collection of homologous traveling wave data through two collection circuits, and obtaining first sampling data and second sampling data; a signal preprocessor is used at the front end of an analog-to-digital converter (ADC), and useful signals are reserved and enhanced; a time synchronization technology is adopted, and the time synchronization precision between systems is optimized; a multi-channel parallel processing technology is adopted, and a plurality of signals are collected and processed at the same time; presetting a break variable threshold, calculating a break variable value of the data, judging whether the break variable value is greater than the preset break variable threshold, and outputting a fault position or a fault distance; the digital signal processing technology, the time synchronization technology and the multi-channel parallel processing technology are combined, so that the overall sampling capacity is improved, the system processing efficiency is optimized, more signal samples can be processed in unit time, the ADC is allowed to sample at a higher rate, and the sampling rate is directly improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric power engineering, and in particular to a traveling wave sampling rate enhancement method suitable for a traveling wave ranging device. Background Art

[0002] With the widespread application of high-voltage direct current transmission technology, the stable operation of power systems has increasingly higher requirements for fault detection and location technology. In high-voltage direct current transmission systems, grounding pole faults are a common type of fault, and their rapid and accurate location is crucial to reducing the scope of fault impact and ensuring safe and stable operation of the system.

[0003] In order to solve the above problems, the Chinese patent publication number CN117686842A discloses a grounding electrode fault ranging method suitable for a high-voltage direct current transmission system. The method utilizes the reverse traveling wave at the measuring point under the conditions of a unipolar grounding fault, a bipolar cross-line grounding fault and a broken line fault, adds an exponential decay term, has strong resistance to high-resistance faults and noise resistance, uses the sampling point difference generated by the forward traveling wave and the reverse traveling wave, and combines the wave speed for calculation to obtain an accurate grounding electrode fault distance. However, the traditional traveling wave ranging method is easily limited by the sampling rate and cannot accurately capture the rapidly changing traveling wave signal. For this reason, the present invention proposes a traveling wave sampling rate enhancement method suitable for a traveling wave ranging device, which can greatly improve the sampling rate and capture more subtle traveling wave signal changes, thereby achieving more accurate fault location. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a traveling wave sampling rate enhancement method applicable to a traveling wave ranging device, so as to overcome the deficiencies in the above-mentioned prior art.

[0005] The technical solution of the present invention to solve the above technical problem is as follows: A traveling wave sampling rate enhancement method applicable to a traveling wave ranging device comprises the following steps:

[0006] Step S01: using an analog-to-digital converter ADC to collect the same-source traveling wave data through two acquisition circuits to obtain first sampling data and second sampling data;

[0007] Step S02: using a signal preprocessor at the front end of the analog-to-digital converter ADC to retain and enhance useful signals, using digital signal processing technology to improve signal recognition and processing speed, using time synchronization technology to optimize the time synchronization accuracy between systems, and using multi-channel parallel processing technology to simultaneously collect and process multiple signals;

[0008] Step S03: Preset a mutation threshold, calculate the mutation value of the data according to the first sampling data, and determine whether the mutation value is greater than the preset mutation threshold:

[0009] If the mutation value is greater than the mutation threshold, the traveling wave head search is performed using the first sampled data that has not been amplified, and the fault position or fault distance is output;

[0010] If the mutation value is less than the mutation threshold, the amplified second sampling data is used to perform traveling wave head search, and the fault position or fault distance is output.

[0011] Based on the above technical solution, the present invention can also be improved as follows.

[0012] Furthermore, the digital signal processing technology in step S02 includes fast Fourier transform FFT and adaptive filtering, wherein the calculation formula of fast Fourier transform is as follows:

[0013]

[0014] Where X(k) is the result of the DFT, x(n) is the time domain signal, N is the number of samples of the signal, i is the imaginary unit, and k is the frequency index.

[0015] Further, step S02 also includes analyzing the non-stationary signal by using a wavelet transform (CWT) time-frequency domain analysis tool, and the calculation formula is:

[0016]

[0017] Among them, W(f, a, b) is the result of wavelet transform, where f is the scale parameter, a and b are the parameters of the wavelet function, x(t) is the original signal, ψ a，b (t) is the wavelet function, which is the product of the Gaussian function and the oscillating function. is the complex conjugate of the wavelet function; the wavelet function ψ in the wavelet transform a，b (t) can be transformed according to the scale a and position b, the formula is as follows:

[0018]

[0019] Further, step S02 also includes expressing the signal as a combination of cosine waves of different frequencies by discrete cosine transform DCT, and making it easy to identify important frequency components in the signal, wherein the DCT-II transform for the one-dimensional signal x[n] is defined as:

[0020]

[0021] Where X(u) is the coefficient after discrete cosine transform (DCT), representing the cosine wave component of the signal at frequency u, x(n) is the nth sample of the original signal, N is the length of the signal, and u and n are integer indices representing the transformed frequency and the position of the original signal.

[0022] Further, the time synchronization technology in step S02 is specifically:

[0023] Step S211: Accurately measure and calibrate the equipment delay of the satellite and the ground station;

[0024] Step S212: using frequency synchronization technology to maintain frequency accuracy between the satellite and the ground station;

[0025] Step S213: Apply the time synchronization protocol in the ground network to synchronize the time of each node in the network, and model and compensate for the tropospheric and ionospheric delays.

[0026] Furthermore, the multi-channel parallel processing technology in step S02 specifically includes the following steps:

[0027] Step S221: split the signal source into multiple sub-signals or channels, each channel can be collected and processed independently;

[0028] Step S222: using multiple analog-to-digital converters ADC to digitize analog signals of different channels simultaneously to obtain digital data;

[0029] Step S223: Buffering the digital data of each channel to match the processing speed of different channels;

[0030] Step S224: designing a parallel processing unit, including one of FPGA, DSP, and GPU, for simultaneously processing digital data of multiple channels;

[0031] Step S225: ensuring that the digital data of all channels are synchronized during processing;

[0032] Step S226: applying corresponding algorithms to the data of each channel, including filtering, Fourier transform, and wavelet transform;

[0033] Step S227: Integrate the processed results of each channel to obtain the final analysis data;

[0034] Step S228: Optimize the parallel processing flow, reduce processing delays, and improve data throughput; develop software to manage parallel tasks, including task scheduling, load balancing, and error handling.

[0035] Further, step S221 is specifically as follows:

[0036] The signal of the signal source is divided into multiple sub-signals through the frequency division multiplexing technology FDM, and these sub-signals are transmitted on different frequency bands, and each sub-signal occupies an independent frequency band.

[0037] Furthermore, the mutation value in step S03 is calculated by calculating the derivative of the signal, and the calculation formula is as follows:

[0038]

[0039] Among them, x(i) and x(i+1) are the signal values ​​of two consecutive sampling points, and t(i) and t(i+1) are the corresponding timestamps.

[0040] Further, the fault location and fault distance in step S03 are calculated as follows:

[0041] Assume that the total length of the line is L, and the propagation speed of the traveling wave in the line is v. When a fault occurs, a traveling wave will be generated at the fault point, and the traveling wave will propagate to both ends of the line. The expression formula is as follows:

[0042]

[0043] Where x is the distance from the fault point to the monitoring point; v is the propagation speed of the traveling wave in the transmission line; Δt is the time required for the traveling wave to propagate from the fault point to the monitoring point;

[0044] Measure the time t from the moment the fault occurs to the moment the traveling wave reaches the monitoring point m1 and the time t when the reflected wave returns to the monitoring point m2 , calculate the time difference Δt = t m2 -t m1 , one-way time

[0045] The distance from the monitoring point to the beginning of the line is x0, and the distance X from the fault point to the beginning of the line is X=x0+x. If X is greater than the total length of the line L, the fault point is actually located opposite the monitoring point, then X=L-(X-x0).

[0046] The beneficial effects of the present invention are as follows: the present invention optimizes the traditional sampling rate so that the traveling wave current sampling rate is not less than 2MHz, and the synchronization timing accuracy is better than 20ns, thereby providing higher time resolution, which is helpful to distinguish the time point when the fault occurs, especially when evaluating the fault characteristics, and greatly improves the sampling rate, so that more subtle traveling wave signal changes can be captured, thereby achieving more accurate fault location, and enabling the system to respond to signal changes faster, which is conducive to capturing transient events and rapid fault detection. The synchronization timing accuracy is better than 20ns, which ensures time synchronization between different measurement points and improves the overall reliability and stability of the system; by increasing parallel processing channels, multiple signals can be collected and processed at the same time, thereby improving the overall sampling capability and optimizing the system processing efficiency. The improvement in response speed means that the system can process more signal samples per unit time, thereby allowing the analog-to-digital converter ADC to sample at a higher rate, directly improving the sampling rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a framework flow chart of the present invention. DETAILED DESCRIPTION

[0048] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0049] like Figure 1 As shown, in embodiment 1, a traveling wave sampling rate enhancement method applicable to a traveling wave ranging device comprises the following steps:

[0050] Step S01: using an analog-to-digital converter ADC to collect the same source traveling wave data through two acquisition circuits to obtain first sampling data and second sampling data; in the specific implementation, a high-speed analog-to-digital converter is selected to achieve a sampling rate of at least 2 MHz;

[0051] Step S02: using a signal preprocessor at the front end of the analog-to-digital converter ADC to retain and enhance useful signals, and using digital signal processing technology to improve signal recognition and processing speed;

[0052] The signal preprocessor includes a low-pass filter and a band-pass filter, which are used to filter out high-frequency noise and unnecessary signal components in the signal, retain and enhance the useful signal, thereby improving the signal-to-noise ratio of the signal. When performing analog-to-digital conversion, the analog-to-digital converter ADC can sample the useful signal more accurately, reduce the sampling error caused by noise, and help improve the sampling rate;

[0053] Use time synchronization technology to optimize the time synchronization accuracy between systems; in specific implementation, use high-precision time synchronization technology, such as GPS or Beidou satellite synchronization, to make the time synchronization accuracy between systems better than 20ns; in specific implementation, design an efficient hardware architecture to reduce the propagation delay of signals in hardware and improve the response speed of the system; develop efficient software algorithms to quickly process and analyze sampled data and reduce data processing time; the improvement in response speed means that the system can process more signal samples per unit time, thereby allowing the analog-to-digital converter ADC to sample at a higher rate, directly increasing the sampling rate;

[0054] Adopt multi-channel parallel processing technology to collect and process multiple signals at the same time; improve the overall sampling and processing capabilities;

[0055] In a specific implementation, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) is used to customize the hardware to meet specific high-speed sampling and processing requirements;

[0056] Step S03: Preset a mutation threshold, calculate the mutation value of the data according to the first sampling data, and determine whether the mutation value is greater than the preset mutation threshold:

[0057] If the mutation value is greater than the mutation threshold, the traveling wave head search is performed using the first sampled data that has not been amplified, and the fault position or fault distance is output;

[0058] If the mutation value is less than the mutation threshold, the amplified second sampling data is used to perform traveling wave head search, and the fault position or fault distance is output.

[0059] The mutation threshold is used to distinguish between normal signal fluctuations and mutations of fault traveling waves. The setting of the mutation threshold is based on historical data and signal characteristics. It determines whether the calculated mutation is greater than the preset mutation threshold. If the mutation > the mutation threshold, a fault may have occurred. If the mutation exceeds the mutation threshold, it indicates that a fault traveling wave may have occurred. According to the mutation detection result, the first sampling data or the second sampling data (if the mutation is small, the second sampling data may need to be amplified) is used to search for the traveling wave head. According to the head search result, combined with the traveling wave propagation speed and time delay, the fault position or fault distance is calculated.

[0060] The present invention optimizes the traditional sampling rate so that the traveling wave current sampling rate is not less than 2MHz, and the synchronization timing accuracy is better than 20ns, thereby providing higher time resolution, which is helpful to distinguish the time point when the fault occurs, especially when evaluating the fault characteristics, and greatly improves the sampling rate, so that more subtle traveling wave signal changes can be captured, thereby achieving more accurate fault location, and enabling the system to respond to signal changes faster, which is conducive to capturing transient events and rapid fault detection. The synchronization timing accuracy is better than 20ns, which ensures time synchronization between different measurement points and improves the overall reliability and stability of the system; by increasing parallel processing channels, multiple signals can be collected and processed at the same time, thereby improving the overall sampling capability and optimizing the system processing efficiency. The improvement in response speed means that the system can process more signal samples per unit time, thereby allowing the analog-to-digital converter ADC to sample at a higher rate, directly improving the sampling rate.

[0061] Embodiment 2: This embodiment is a further improvement on the basis of embodiment 1, and its details are as follows:

[0062] The digital signal processing technology in step S02 includes fast Fourier transform FFT and adaptive filtering, wherein the calculation formula of fast Fourier transform is as follows:

[0063]

[0064] Where X(k) is the result of the DFT, x(n) is the time domain signal, N is the number of samples of the signal, i is the imaginary unit, and k is the frequency index.

[0065] The FFT algorithm reduces the amount of calculation by decomposing the DFT into smaller DFTs; for a sequence of length N, the calculation formula of FFT can be expressed as:

[0066]

[0067] Among them, J and I are factors that decompose the sequence into smaller sequences, satisfying N = J·I, c ji is the coefficient in the butterfly operation, which is usually pre-calculated in the FFT algorithm implementation; W N is the basic Wignor D function W N =e -i·2π / N , where the butterfly operation is used to recursively combine two frequency components, and the calculation formula is as follows:

[0068] X(2m+1)=x(m)+W N ·x(m+M)

[0069] X(2m+2)=x(m)-W N x(m+M),

[0070] Among them, X is the output frequency domain sequence, x is the input time domain sequence, m is the sample index currently being processed, and M is the number of samples merged in each butterfly operation.

[0071] Embodiment 3, this embodiment is a further improvement on the basis of embodiment 1, and its details are as follows:

[0072] Step S02 also includes analyzing the non-stationary signal by using a wavelet transform (CWT) time-frequency domain analysis tool, and the calculation formula is:

[0073]

[0074] Among them, W(f, a, b) is the result of wavelet transform, where f is the scale parameter, a and b are the parameters of the wavelet function, x(t) is the original signal, ψ a，b (t) is the wavelet function, which is the product of the Gaussian function and the oscillating function. is the complex conjugate of the wavelet function; the wavelet function ψ in the wavelet transform a，b (t) can be transformed according to the scale a and position b, the formula is as follows:

[0075]

[0076] In this embodiment, the wavelet transform CWT can be realized by performing a sliding window analysis of the wavelet function on the signal, wherein the size and shape of the window are controlled by the scale parameter a.

[0077] When processing non-stationary signals, wavelet transform (CWT) can reduce the aliasing and noise effects of signals and improve the signal-to-noise ratio of signals; it can also improve the quality of traveling wave sampling, which is conducive to increasing the sampling rate.

[0078] Embodiment 4: This embodiment is a further improvement on the basis of embodiment 1, and its details are as follows:

[0079] Step S02 also includes expressing the signal as a combination of cosine waves of different frequencies by discrete cosine transform DCT, and making it easy to identify important frequency components in the signal, wherein the DCT-II transform for the one-dimensional signal x[n] is defined as:

[0080]

[0081] Where X(u) is the coefficient after discrete cosine transform (DCT), representing the cosine wave component of the signal at frequency u, x(n) is the nth sample of the original signal, N is the length of the signal, and u and n are integer indices representing the transformed frequency and the position of the original signal.

[0082] For two-dimensional signals, such as images, the DCT transform can be performed on the rows and columns of the image separately:

[0083]

[0084] Among them, X(u, v) is the coefficient after two-dimensional DCT transformation, f(x, y) is the pixel value of the image at position (x, y), M and N are the number of rows and columns of the image respectively; the coefficients after DCT transformation are sorted from low to high according to frequency, the low-frequency coefficients contain the main information of the image, and the high-frequency coefficients contain the detail information; in image compression, the amount of data is reduced by quantizing and encoding the coefficients, which is beneficial to maintaining image quality.

[0085] Embodiment 5, this embodiment is a further improvement on the basis of embodiment 1, and its details are as follows:

[0086] The time synchronization technology in step S02 is specifically:

[0087] Step S211: Accurately measure and calibrate the equipment delay of the satellite and the ground station to eliminate the influence of the zero value difference of the equipment on the time synchronization accuracy;

[0088] Step S212: using frequency synchronization technology to maintain the frequency accuracy between the satellite and the ground station and reduce the impact of frequency errors on time synchronization accuracy;

[0089] Step S213: Apply a time synchronization protocol, such as the Precision Time Protocol (PTP) or the Network Time Protocol (NTP), in the ground network to synchronize the time of each node in the network, and model and compensate for tropospheric and ionospheric delays to reduce the impact of these environmental factors on the accuracy of time synchronization.

[0090] Embodiment 6: This embodiment is a further improvement on the basis of embodiment 1, and its details are as follows:

[0091] The multi-channel parallel processing technology in step S02 specifically includes the following steps:

[0092] Step S221: split the signal source into multiple sub-signals or channels, each channel can be collected and processed independently;

[0093] Step S222: using multiple analog-to-digital converters ADC to digitize analog signals of different channels simultaneously to obtain digital data;

[0094] Step S223: Buffering the digital data of each channel to match the processing speed of different channels;

[0095] Step S224: designing a parallel processing unit, including one of FPGA, DSP, and GPU, for simultaneously processing digital data of multiple channels;

[0096] Step S225: ensuring that the digital data of all channels are synchronized during processing, so as to facilitate subsequent digital data analysis or fusion;

[0097] Step S226: applying corresponding algorithms to the data of each channel, including filtering, Fourier transform, and wavelet transform;

[0098] Step S227: Integrate the processed results of each channel to obtain the final analysis data;

[0099] Step S228: Optimize the parallel processing flow, reduce processing delays, and improve data throughput; develop software to manage parallel tasks, including task scheduling, load balancing, and error handling.

[0100] Connect multiple analog-to-digital converters (ADCs) to the corresponding analog signal channels, and configure the sampling rate and clock so that all analog-to-digital converters (ADCs) can start sampling at the same time; the analog-to-digital converters (ADCs) convert the analog signals into digital data, and temporarily store the digital data in a buffer; read the digital data from the analog-to-digital converters (ADCs) to the processor, and apply a filtering algorithm to the digital data to remove noise; time-align the digital data of each channel, merge the synchronized digital data, and prepare for parallel processing; apply a parallel processing algorithm to the digital data of multiple channels; adjust the process to reduce delays and improve efficiency, and detect and correct errors in data acquisition.

[0101] Embodiment 7, this embodiment is a further improvement on the basis of embodiment 6, and its details are as follows:

[0102] Step S221 is specifically as follows:

[0103] Frequency division multiplexing (FDM) is used to divide the signal source into multiple sub-signals, which are transmitted on different frequency bands, with each sub-signal occupying an independent frequency band. To prevent interference between signals, FDM can effectively allocate the bandwidth of the signal and improve data transmission efficiency.

[0104] Embodiment 8: This embodiment is a further improvement on the basis of Embodiment 1, and its details are as follows:

[0105] In this embodiment, when multiple channels are processed in parallel, the processing of each channel may be independent, but the overall system needs to coordinate the work of each channel to ensure the consistency and accuracy of the data. If each channel is processed by FFT, the FFT calculation of each channel can be performed independently, but the final result needs to be aligned with the time of the original signal; considering that each channel needs to be processed by FFT, the FFT calculation formula of each channel is as follows:

[0106]

[0107] Among them, X k (n) is the nth FFT coefficient of the kth channel; x k (i) is the i-th sampling point of the k-th channel; N is the number of sampling points in each channel.

[0108] The mutation value in step S03 is calculated by calculating the derivative (i.e., slope) of the signal, and the calculation formula is as follows:

[0109]

[0110] Among them, x(i) and x(i+1) are the signal values ​​of two consecutive sampling points, and t(i) and t(i+1) are the corresponding timestamps.

[0111] Embodiment 9, this embodiment is a further improvement on the basis of embodiment 8, and its details are as follows:

[0112] The fault location and fault distance in step S03 are calculated as follows:

[0113] Assume that the total length of the line is L, and the propagation speed of the traveling wave in the line is v. When a fault occurs, a traveling wave will be generated at the fault point, and the traveling wave will propagate to both ends of the line. The expression formula is as follows:

[0114]

[0115] Where x is the distance from the fault point to the monitoring point; v is the propagation speed of the traveling wave in the transmission line; Δt is the time required for the traveling wave to propagate from the fault point to the monitoring point;

[0116] Measure the time t from the moment the fault occurs to the moment the traveling wave reaches the monitoring point m1 and the time t when the reflected wave returns to the monitoring point m2 , calculate the time difference Δt = t m2 -t m1 Since Δt includes the time for the traveling wave to reach the fault point and reflect back from the fault point, the one-way time is

[0117] The distance from the monitoring point to the beginning of the line is x0, and the distance X from the fault point to the beginning of the line is X=x0+x. If X is greater than the total length of the line L, the fault point is actually located opposite the monitoring point, then X=L-(X-x0).

[0118] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A traveling wave sampling rate enhancement method applicable to a traveling wave ranging device, characterized in that: The steps include: Step S01: using an analog-to-digital converter ADC to collect the same-source traveling wave data through two acquisition circuits to obtain first sampling data and second sampling data; Step S02: using a signal preprocessor at the front end of the analog-to-digital converter ADC to retain and enhance useful signals, using digital signal processing technology to improve signal recognition and processing speed, using time synchronization technology to optimize the time synchronization accuracy between systems, and using multi-channel parallel processing technology to simultaneously collect and process multiple signals; Step S03: Preset a mutation threshold, calculate the mutation value of the data according to the first sampling data, and determine whether the mutation value is greater than the preset mutation threshold: If the mutation value is greater than the mutation threshold, the traveling wave head search is performed using the first sampling data that has not been amplified, and the fault position or fault distance is output; If the mutation value is less than the mutation threshold, the amplified second sampling data is used to perform traveling wave head search, and the fault position or fault distance is output.

2. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The digital signal processing technology in step S02 includes fast Fourier transform FFT and adaptive filtering, wherein the calculation formula of the fast Fourier transform is as follows: Where X(k) is the result of the DFT, x(n) is the time domain signal, N is the number of samples of the signal, i is the imaginary unit, and k is the frequency index.

3. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The step S02 also includes analyzing the non-stationary signal by using a wavelet transform (CWT) time-frequency domain analysis tool, and the calculation formula is: Among them, W(f, a, b) is the result of wavelet transform, where f is the scale parameter, a and b are the parameters of the wavelet function, x(t) is the original signal, ψ a，b (t) is the wavelet function, which is the product of the Gaussian function and the oscillating function. is the complex conjugate of the wavelet function; the wavelet function ψ in the wavelet transform a，b (t) can be transformed according to the scale a and position b, the formula is as follows:

4. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The step S02 also includes expressing the signal as a combination of cosine waves of different frequencies by discrete cosine transform DCT, and making it easy to identify important frequency components in the signal, wherein the DCT-II transform for the one-dimensional signal x[n] is defined as: Where X(u) is the coefficient after discrete cosine transform (DCT), representing the cosine wave component of the signal at frequency u, x(n) is the nth sample of the original signal, N is the length of the signal, and u and n are integer indices representing the transformed frequency and the position of the original signal.

5. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The time synchronization technology in step S02 is specifically: Step S211: Accurately measure and calibrate the equipment delay of the satellite and the ground station; Step S212: using frequency synchronization technology to maintain frequency accuracy between the satellite and the ground station; Step S213: Apply the time synchronization protocol in the ground network to synchronize the time of each node in the network, and model and compensate for the tropospheric and ionospheric delays.

6. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The multi-channel parallel processing technology in step S2 specifically includes the following steps: Step S221: split the signal source into multiple sub-signals or channels, each channel can be collected and processed independently; Step S222: using a plurality of analog-to-digital converters ADC to simultaneously digitize analog signals of different channels to obtain digital data; Step S223: buffering the digital data of each channel to match the processing speed of different channels; Step S224: designing a parallel processing unit, including one of FPGA, DSP, and GPU, for simultaneously processing the digital data of multiple channels; Step S225: ensuring that the digital data of all channels are synchronized during processing; Step S226: applying corresponding algorithms to the data of each channel, including filtering, Fourier transform, and wavelet transform; Step S227: Integrate the processed results of each channel to obtain the final analysis data; Step S228: Optimize the parallel processing flow, reduce processing delays, and improve data throughput; develop software to manage parallel tasks, including task scheduling, load balancing, and error handling.

7. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 6, characterized in that: The step S221 is specifically as follows: The signal of the signal source is divided into multiple sub-signals through the frequency division multiplexing technology FDM, and these sub-signals are transmitted on different frequency bands, and each sub-signal occupies an independent frequency band.

8. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 1, characterized in that: The mutation value in step S03 is calculated by calculating the derivative of the signal, and the calculation formula is as follows: Among them, x(i) and x(i+10) are the signal values ​​of two consecutive sampling points, and t(i) and t(i+1) are the corresponding timestamps.

9. The method for enhancing the traveling wave sampling rate applicable to a traveling wave ranging device according to claim 8, characterized in that: The fault location and fault distance in step S03 are calculated as follows: Assume that the total length of the line is L, and the propagation speed of the traveling wave in the line is V. When a fault occurs, a traveling wave will be generated at the fault point, and the traveling wave will propagate to both ends of the line. The expression formula is as follows: Where x is the distance from the fault point to the monitoring point; v is the propagation speed of the traveling wave in the transmission line; Δt is the time required for the traveling wave to propagate from the fault point to the monitoring point; Measure the time t from the moment the fault occurs to the moment the traveling wave reaches the monitoring point m1 and the time t when the reflected wave returns to the monitoring point m2 , calculate the time difference Δt = t m2 -t m1 , one-way time The distance from the monitoring point to the beginning of the line is x0, and the distance X from the fault point to the beginning of the line is X=x0+x. If Z is greater than the total length of the line L, the fault point is actually located opposite the monitoring point, then X=L-(X-x0).

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