Wave head calibration and fault positioning method and system based on sliding kurtosis extreme point

Abstract

The invention discloses a wave head calibration and fault positioning method and system based on a sliding kurtosis extreme point. The wave head calibration method comprises the steps of collecting a fault transient signal of a power distribution network line through an intelligent sensing unit installed on the power distribution network line; processing the fault transient signal based on a complete set empirical mode decomposition method of mean value adaptive noise to obtain an intrinsic mode component of a preset order of the fault transient signal; performing sliding windowing processing on the solid-state modal component of the fault transient signal in a preset order by adopting a pane with a preset length, and calculating kurtosis in each sliding window; and selecting the maximum value from the kurtosis values of all the sliding windows, wherein the time point corresponding to the maximum value is the traveling wave arrival time. And preferably selecting two intelligent sensing units from a plurality of intelligent sensing units of the power distribution network, and determining a fault position by adopting a double-end positioning method. After the kurtosis of the traveling wave signal is accurately reflected, the arrival time of the traveling wave head is calibrated, and accurate positioning of the fault traveling wave is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid fault detection, and particularly relates to a wavefront calibration and fault location method and system based on sliding kurtosis extreme points. Background Art

[0002] Faults frequently occur in the transmission and distribution lines of new power systems, and the fault points are highly concealed and difficult to find, resulting in long power outage times and seriously affecting residents' production and life. Reliable, accurate, and fast line fault location plays a crucial role in improving the accuracy of fault line patrol and maintaining the reliable operation of the power grid. Accurate fault location in the power grid is often affected by noise interference, resulting in low accuracy in identifying the wavefront of traveling waves.

[0003] The traveling wave positioning method has high theoretical positioning accuracy and is not affected by the system operation mode, transition resistance, and CT saturation. However, the calibration time of the traveling wavefront will greatly affect the positioning accuracy. The complex power grid of the new power system has the characteristics of long power grid lines, serious signal attenuation, and susceptibility to interference. The traditional positioning and ranging method that relies on threshold triggering to calibrate the arrival time of traveling waves has phenomena such as misoperation, refusal to operate, and inaccurate positioning, and can no longer meet the development needs of the new power system.

[0004] In order to achieve the comprehensive development of the power system and accelerate the construction of the smart grid, it is urgent to quickly determine the location of the fault point after the fault occurs and study a stable and reliable method to achieve fault location. The existing traveling wave positioning methods have weak anti-interference ability, are greatly affected by the noise interference of power electronic devices, and are difficult to distinguish weak fault signals, resulting in insufficient positioning accuracy. Therefore, there is an urgent need for a traveling wave positioning method that can accurately identify the wavefront in a complex noise environment.

[0005] Currently, the mainly used methods are the threshold method, wavelet transform method, and Hilbert-Huang transform. The traveling wave calibration method based on the software / hardware threshold method first filters the power frequency signal, and then detects the traveling wave according to whether the amplitude of the remaining higher-frequency transient signal exceeds the limit to determine the traveling wave time. However, due to its poor selectivity and difficulty in distinguishing noise interference and traveling wave signals, it has been gradually phased out. The wavelet transform method can better process signals, but this method requires selecting a specific wavelet basis, and its decomposition effect is difficult to balance the local and the whole. Therefore, the basis function of wavelet analysis lacks adaptability. The further signal processing method based on the Hilbert-Huang transform (HHT) can make full use of the frequency domain information in the traveling wave signal. For example, the empirical mode decomposition (EMD) algorithm can be decomposed without prior analysis, but there are problems such as modal component aliasing and end effect, resulting in incorrect calibration positions of the traveling wavefront, and the extraction effect of EMD is not ideal under strong noise. Summary of the Invention

[0006] The present invention provides a wavefront calibration and fault location method and system based on the extreme points of sliding kurtosis. By inversely differentiating and outputting the traveling wave signal, the kurtosis of the traveling wave signal is accurately reflected, and then the arrival time of the wavefront of the traveling wave is calibrated to achieve the accurate location of the fault traveling wave.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A wavefront calibration method based on the extreme points of sliding kurtosis, comprising:

[0009] Step 1, collecting the fault transient signal of the distribution network line through the intelligent sensing unit installed on the distribution network line ;

[0010] Step 2, processing the fault transient signal by the complete ensemble empirical mode decomposition method based on mean adaptive noise to obtain the intrinsic mode components of the preset order of the fault transient signal;

[0011] Step 3, performing a sliding windowing process on the solid mode components of the preset order of the fault transient signal with a window pane of a preset length, and calculating the kurtosis within each sliding window;

[0012] Step 4, selecting the maximum value from the kurtosis values of all sliding windows, and the time point corresponding to this maximum value is the arrival time of the wavefront of the fault transient signal.

[0013] Further, step 2 specifically includes:

[0014] Step 2.1, adding Gaussian white noise to the original fault transient signal repeatedly N times to obtain N groups of noisy fault transient signals; denoting the Gaussian white noise added for the nth time as , and the nth group of noisy fault transient signal as ;

[0015] Step 2.2, performing the first EMD decomposition on each group of noisy fault transient signals to obtain its intrinsic mode components , then taking the mean of the N groups of obtained intrinsic mode components as the first-order intrinsic mode component of the fault transient signal; and then calculating and the original fault transient signal to obtain the first-order residual of the signal;

[0016] Step 2.3, setting i = 1;

[0017] Step 2.4, performing EMD decomposition on the added Gaussian white noise to obtain the ith-order intrinsic mode component , and then add it to the i-th order residual of the signal as the signal to be decomposed in the next order ;

[0018] Step 2.5, perform EMD decomposition on the signal to obtain the (i + 1)-th order intrinsic mode component of the noisy fault transient signal , then take the mean of N groups of the obtained intrinsic mode components as the (i + 1)-th order intrinsic mode component of the fault transient signal; then calculate the residual between and the i-th order residual of the signal, which is the (i + 1)-th order residual of the signal;

[0019] Step 2.6, determine whether the intrinsic mode components of the preset order of the fault transient signal have been obtained through decomposition: if they have been obtained, end the decomposition; if not, update i = i + 1, and repeat Steps 2.4 to 2.5 until the intrinsic mode components of the preset order are obtained.

[0020] Furthermore, set the preset order described in Step 2.6 according to the type of the distribution network line; if the distribution network line is an overhead line, the preset order is 3; if the distribution network line is a cable line, the preset order is 2.

[0021] Furthermore, the calculation formula for the kurtosis in each sliding window described in Step 3 is:

[0022]

[0023] In the formula, represents the kurtosis of the intrinsic mode component in the -th sliding window, represents the intrinsic mode component of the preset order of the fault transient signal, represents the intrinsic mode component at the m-th data point in the d-th sliding window, represents the mean of all data points of the intrinsic mode component , and is the window length of the sliding window.

[0024] The present invention also provides a fault location method, including:

[0025] First, install intelligent sensing units at the ends of each feeder of the distribution network;

[0026] Then, use the wavefront calibration method based on the extreme points of sliding kurtosis described in any one of the above to determine the arrival time of the wavefront of the fault transient signal of each intelligent sensing unit;

[0027] Then, select the two earliest intelligent sensing units from all the wavefront arrival times, and calculate the line distance between the two by extracting the installation positions of the two intelligent sensing units;

[0028] Finally, based on the two wavefront arrival times and the line distance between the two intelligent sensing units where they are located, calculate the position of the fault point.

[0029] Furthermore, the calculation of the fault point position is expressed as:

[0030]

[0031] In the formula, represents the two intelligent sensing units with the earliest arrival times of traveling waves, respectively represent the arrival times of traveling waves of the intelligent sensing units , is the traveling wave velocity, represents the line distance between the intelligent sensing units , represents the line distance from the fault point to the intelligent sensing unit .

[0032] The present invention also provides a fault location system based on the extreme points of sliding kurtosis, including several intelligent sensing units installed at the ends of each feeder in the distribution network and a central processor;

[0033] The intelligent sensing unit is used to implement the wavefront calibration method based on the extreme points of sliding kurtosis described in any one of the above;

[0034] The central processor is used for: first, obtaining the arrival times of the fault transient signal wavefronts from the intelligent sensing units; then, selecting the two earliest intelligent sensing units from all the wavefront arrival times, and calculating the line distance between the two by extracting the installation positions of the two intelligent sensing units; finally, calculating the position of the fault point based on the two wavefront arrival times and the line distance between the two intelligent sensing units where they are located.

[0035] In view of the problems that the current method of wavelet transform for extracting the traveling wave head needs to determine the wavelet basis function, and the current EMD decomposition method has mode mixing, resulting in misjudgment, etc., the present invention uses the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ACEEMDAN) based on the mean to decompose the collected fault transient signal to obtain the intrinsic mode components. It selects the IMF components after the EMD decomposition of white noise for addition, gradually reducing the noise intensity, effectively avoiding excessive addition of noise, especially avoiding the noise signal amplitude exceeding the effective signal in the case of a higher decomposition level. Therefore, it has high adaptability in the scale of signal decomposition, and while reducing mode mixing, it can effectively filter out the line noise signal, thereby improving the accuracy of wave head calibration.

[0036] In view of the problem that the current method of using the Teager energy operator to find the mutation point is easily affected by noise, and is greatly affected by the occasional partial discharge noise of the power grid, which easily leads to inaccurate calibration of the traveling wave time, after the intrinsic mode components are decomposed in the present invention, the sliding kurtosis maximum (SKM) is used to enhance and identify the traveling wave mutation characteristics, so that the traveling wave head can still be well calibrated in a complex noise environment, realizing the improvement of the anti-noise performance under a high-noise background. Description of the Drawings

[0037] Figure 1 is the configuration diagram of the intelligent sensing unit in the distribution network described in the embodiment of the present application;

[0038] Figure 2 is the flowchart of the fault location method described in the embodiment of the present application;

[0039] Figure 3 are the intrinsic mode components obtained by decomposing the recorded wave segment using the ACEEMDAN method in the embodiment of the present application;

[0040] Figure 4 is the enhanced result of the SKM wave head characteristics of the IMF3 component obtained by decomposition in the embodiment of the present application. Detailed Embodiments

[0041] The following details the embodiments of the present invention. This embodiment is carried out based on the technical solution of the present invention, and gives the detailed implementation manner and specific operation process, further explaining the technical solution of the present invention.

[0042] Embodiment 1

[0043] This embodiment provides a wave head calibration method based on the sliding kurtosis maximum, including:

[0044] Step 1: Collect the fault transient signals of the distribution network line through the intelligent sensing units installed on the distribution network line .

[0045] Take Figure 1 the new type of distribution system shown as an example. Install intelligent sensing units at the ends of each feeder of the distribution network, namely A~H. The intelligent sensing unit in this embodiment is a signal sensing and processing device snap-mounted on the three-phase wire, mainly composed of a tunneling magnetoresistance current sensor, a data calculation processor, and a network communication module. To cover a larger range of lines, the intelligent sensing units are generally installed at the beginning of the line and on the transformer leads at the end of the line, so as to locate the fault points on the lines between the units within the zone.

[0046] The intelligent sensing unit uses a tunneling magnetoresistance current sensor with better performance to continuously measure the distribution network current signal. After the fault occurs at point f, the fault transient signal propagates along the arrow direction in the figure.

[0047] The fault transient signal is a mutant surge wave that travels along both sides of the line from the fault point after the fault occurs. When there is no fault, the current should be a relatively standard sine signal, and the current magnitude is within the rated current and the instantaneous change is small. After the fault, the current will suddenly increase within a short time and even exceed the rated value, and this is used as the starting basis for subsequent operations.

[0048] Step 2: Process the fault transient signal using the complete ensemble empirical mode decomposition method based on mean adaptive noise to obtain the intrinsic mode components of the preset order of the fault transient signal.

[0049] Step 2.1: Add Gaussian white noise to the original fault transient signal repeatedly N times to obtain N groups of noisy fault transient signals; denote the Gaussian white noise added for the nth time as , and the nth group of noisy fault transient signal as , . Among them, adding Gaussian white noise to for the nth time is expressed as:

[0050]

[0051] In the formula, e is the signal-to-noise ratio of the Gaussian white noise.

[0052] Step 2.2: Perform the first EMD decomposition on each group of noisy fault transient signals to obtain its intrinsic mode components , and then take the mean of the N groups of obtained intrinsic mode components as the first-order intrinsic mode component of the fault transient signal; then calculate The signal first-order residual between the signal and the original fault transient signal is expressed as:

[0053]

[0054]

[0055] Step 2.3, let i = 1.

[0056] Step 2.4, perform EMD decomposition on the added Gaussian white noise to obtain the i-th order intrinsic mode component of the noise and then add it to the i-th order residual of the signal as the signal to be decomposed at the next order .

[0057]

[0058] Step 2.5, perform EMD decomposition on the signal to obtain the (i + 1)-th order intrinsic mode component of the noisy fault transient signal , and then take the mean of N groups of the obtained intrinsic mode components as the (i + 1)-th order intrinsic mode component of the fault transient signal ; then calculate the residual between and the i-th order residual of the signal , which is the (i + 1)-th order residual of the signal .

[0059]

[0060]

[0061] Step 2.6, determine whether the intrinsic mode component of the preset order of the fault transient signal has been obtained in the current decomposition: if it has been obtained, end the decomposition; if not, update i = i + 1 and repeat steps 2.4 to 2.5 until the intrinsic mode component of the preset order is obtained.

[0062] ​​​Decomposing non-stationary and non-linear traveling wave signals (i.e., fault transient signals) into multiple stationary local oscillation modes can, to a certain extent, eliminate some high-frequency noises, extract fault feature signals, and facilitate further analysis and processing. For traveling wave detection, the present invention proposes to decompose fault transient signals using ACEEMDAN, which can better capture the traveling wave characteristics in the signals. The larger the decomposition times i, the closer the equivalent frequency of the obtained intrinsic mode function (IMF) is to the fundamental frequency. In this optimized embodiment, the preset order described in step 2.6 is set according to the type of the distribution network line; if the distribution network line is an overhead line, since there is more noise in the overhead line, to avoid false triggering, the preset order is 3; if the distribution network line is a cable line, since the cable line has relatively weak shielding layer noise, the preset order is set to 2.

[0063] The complete ensemble empirical mode decomposition method based on mean adaptive noise used in step 2 is different from CEEMDAN (complete ensemble empirical mode decomposition method based on adaptive noise) which directly adds Gaussian white noise during the decomposition process. It selects the IMF components after the EMD decomposition of white noise for addition, gradually reducing the noise intensity, effectively avoiding excessive addition of noise, especially avoiding the amplitude of the noise signal exceeding the effective signal when the decomposition layer is relatively high.

[0064] However, the IMF components after being decomposed in step 2 are all functions that oscillate around zero, and the position of the traveling wave head cannot be directly and accurately determined. Therefore, steps 3 and 4 need to be executed to determine the exact position of the wave head.

[0065] Step 3: Perform a sliding windowing process on the solid state mode components of the fault transient signal with a preset order using a window pane of a preset length, and calculate the kurtosis within each sliding window.

[0066] The calculation of the kurtosis within each sliding window described in step 3 is calculated by the formula:

[0067]

[0068] In the formula, represents the kurtosis of the intrinsic mode component in the th sliding window, represents the intrinsic mode component of the fault transient signal with a preset order, represents the intrinsic mode component at the mth data point in the dth sliding window, represents the intrinsic mode component the mean value of all data points, is the window pane length of the sliding window.

[0069] The solid modal components of the preset order of the fault transient signal are processed by sliding windowing to calculate the kurtosis within each sliding window, and the kurtosis of all sliding windows constitutes the kurtosis sequence. Its pulse nature is more prominent. is the sequence length of the intrinsic mode component , that is, the number of sampling data points of the original fault transient signal .

[0070] Step 4, select the maximum value from the kurtosis values of all sliding windows , and this maximum value The corresponding time point is the traveling wave arrival time, that is, the wavefront arrival time of the fault transient signal.

[0071] For the IMF components obtained by the complete ensemble empirical mode decomposition method based on mean adaptive noise (ACEEMDAN), the following constraint conditions must be satisfied: at any time, the average value of the upper envelope formed by local maximum points and the lower envelope formed by local minimum points is zero, that is, the upper and lower envelopes are locally symmetric with respect to the time axis. Therefore, the mean value of the signal decomposed by ACEEMDAN in the present invention is approximately 0. Although, for example, the VMD method can also effectively decompose the traveling wave signal in the case of high noise, it cannot guarantee that the decomposed components have the characteristic of a mean value of 0, which will affect the effect of the SKM method. The core index of the sliding kurtosis extreme point SKM is kurtosis. The larger the kurtosis of the original signal, the easier it is to distinguish the traveling wave pulse from other noises, and the position of the wavefront is more easily and accurately located. Therefore, the accuracy and robustness of wavefront calibration can be improved.

[0072] Embodiment 2

[0073] This embodiment provides a fault location method. Referring to Figure 2 shown, the method includes the following steps:

[0074] First, install intelligent sensing units at the ends of each feeder in the distribution network. Referring to Figure 1 shown.

[0075] Then, use the wavefront calibration method based on sliding kurtosis extreme points described in Embodiment 1 to determine the wavefront arrival time of the fault transient signal of each intelligent sensing unit.

[0076] Then, select the two earliest intelligent sensing units from all the wavefront arrival times, and calculate the line distance between the two by extracting the installation positions of the two intelligent sensing units. Taking Figure 1 the fault occurring at point f as an example, the traveling wave signal propagates to the three intelligent sensing units E, F, and G. After analysis, the respective traveling wave arrival times O E , O F , O G, According to the traveling wave transmission theory, the measurement point closer to the fault point has an earlier time. Therefore, the earliest time and the second time are selected for double - ended traveling wave location, that is, O E 、O G 。

[0077] Finally, based on the arrival times of these two wavefronts and the line distance between the two intelligent sensing units where they are located, calculate the location of the fault point:

[0078]

[0079] In the formula, represents the two intelligent sensing units with the earliest arrival times of traveling waves, respectively represent the arrival times of traveling waves of the intelligent sensing unit , is the traveling wave velocity, represents the line distance between the intelligent sensing units , represents the line distance from the fault point to the intelligent sensing unit .

[0080] Embodiment 3

[0081] This embodiment provides a fault location system based on the extreme points of sliding kurtosis, including several intelligent sensing units installed at the ends of each feeder in the distribution network and a central processor;

[0082] The intelligent sensing unit is used to implement the wavefront calibration method based on the extreme points of sliding kurtosis described in Embodiment 1;

[0083] The central processor is used to: first obtain the arrival times of the fault transient signal wavefronts from the intelligent sensing units; then select the two intelligent sensing units with the earliest arrival times from all the arrival times of the wavefronts, calculate the line distance between the two by extracting the installation positions of the two intelligent sensing units; finally, based on the arrival times of these two wavefronts and the line distance between the two intelligent sensing units where they are located, calculate the location of the fault point:

[0084]

[0085] In the formula, represents the two intelligent sensing units with the earliest arrival times of traveling waves, respectively represent the arrival times of traveling waves of the intelligent sensing unit , is the traveling wave velocity, represents the line distance between the intelligent sensing units , represents the line distance from the fault point to the intelligent sensing unit Line distance.

[0086] Example analysis:

[0087] Build a simulation model as Figure 1 shown. Simulate a single-phase grounding fault at point f, set the fault transition resistance to 200 Ω, and the A~H intelligent sensing units continuously measure signals and obtain the oscillogram segments x A ~x H .

[0088] Take the oscillogram segment x G of the measurement point G as an example for analysis and calculation. Use the ACEEMDAN method to decompose x G . The IMFs 1~3 obtained by decomposing using the ACEEMDAN method are as Figure 3 shown.

[0089] Since the simulated line is an overhead line, select the decomposed IMF 3 for further wavefront calibration. Use the SKM method to process the IMF 3 to obtain the spectrogram with enhanced wavefront mutation as Figure 4 shown.

[0090] According to the SKM wavefront feature enhancement result, the SKM maximum point K a (147, 504) can be obtained, and the corresponding traveling wave arrival time O G is 0.147 ms. Similarly, O E = 0.158 ms and O F = 0.166 ms can be obtained.

[0091] Furthermore, select the earliest time and the second time for double-ended traveling wave positioning, that is, O E , O G . Then, according to the double-ended traveling wave positioning formula, L Ef = 5.595 km can be obtained, and the positioning error is 95 m. It shows a high positioning accuracy.

[0092] The above embodiments are the preferred embodiments of the present application. Those of ordinary skill in the art can also make various transformations or improvements on this basis. Without departing from the general concept of the present application, these transformations or improvements should all fall within the scope of protection required by the present application.

Claims

1. A wave head calibration method based on sliding kurtosis extreme point, characterized in that: include: Step 1: Collect the fault transient signal of the distribution network line through the intelligent sensor unit installed on the distribution network line ; Step 2, processing the fault transient signal based on the complete set empirical mode decomposition method of mean adaptive noise to obtain the inherent mode components of the fault transient signal of a preset order; Step 3, using a window pane of a preset length to perform sliding windowing processing on the solid-state modal component of a preset order of the fault transient signal, and calculating the kurtosis in each sliding window; Step 4: Select the maximum value from the kurtosis values ​​of all sliding windows. The time point corresponding to the maximum value is the wave head arrival time of the fault transient signal.

2. The wave head calibration method based on sliding kurtosis extreme point according to claim 1 is characterized in that: Step 2 specifically includes: Step 2.1, repeat N times to the original fault transient signal Gaussian white noise is added to obtain N groups of noisy fault transient signals; the Gaussian white noise added for the nth time is recorded as , the fault transient signal of the nth group with noise is ; Step 2.2: for each group of fault transient signals with added noise Perform the first EMD decomposition to obtain its intrinsic mode components , and then take the mean of the N groups of obtained natural modal components as the first-order natural modal component of the fault transient signal ; recalculate The original fault transient signal The first-order residual of the signal between ; Step 2.3, let i=1; Step 2.4, add Gaussian white noise Perform EMD decomposition to obtain the i-th order intrinsic modal component of the noise , and then added to the signal i-th order residual As the next order signal to be decomposed ; Step 2.5, signal Perform EMD decomposition to obtain the noisy fault transient signal The i+1th order natural mode component , and then take the mean of the N groups of obtained natural modal components as the i+1th order natural modal component of the fault transient signal ; recalculate and the i-th order residual of the signal The residual between is the i+1th order residual of the signal ; Step 2.6, determine whether the inherent modal components of the preset order of the fault transient signal are currently decomposed: if they are, end the decomposition; if not, update i=i+1, and repeat steps 2.4 to 2.5 until the inherent modal components of the preset order are decomposed.

3. The wave head calibration method based on sliding kurtosis extreme point according to claim 2 is characterized in that: Set the preset order described in step 2.6 according to the type of distribution network line; if the distribution network line is an overhead line, the preset order is 3; if the distribution network line is a cable line, the preset order is 2.

4. The wave head calibration method based on sliding kurtosis extreme point according to claim 1 is characterized in that: The kurtosis in each sliding window is calculated as described in step 3, and the calculation formula is: ； In the formula, Represents the natural mode component at the The kurtosis within the sliding window, Represents the inherent modal component of the preset order of the fault transient signal, Represents the intrinsic modal components For the mth data point in the dth sliding window, Represents the intrinsic modal components The mean of all data points, The length of the sliding window pane.

5. A fault location method, characterized in that: include: First, smart sensor units are installed at the end of each feeder in the distribution network; Then, the wave head calibration method based on sliding kurtosis extreme point as described in any one of claims 1 to 4 is used to determine the arrival time of the wave head of the fault transient signal of each intelligent sensor unit; Then, the two earliest intelligent sensor units are selected from all wave head arrival times, and the line distance between the two intelligent sensor units is calculated by extracting the installation positions of the two intelligent sensor units; Finally, the location of the fault point is calculated based on the arrival time of the two wave heads and the line distance between the two intelligent sensor units.

6. The fault location method according to claim 5, characterized in that: The calculated fault point location is expressed as: ； In the formula, Indicates the two smart sensor units with the earliest arrival time of the traveling wave, Intelligent sensor unit The arrival time of the traveling wave, is the traveling wave speed, Intelligent sensor unit The line distance between Indicates the fault point To Intelligent Sensing Unit line distance.

7. A fault location system based on sliding kurtosis extreme point, characterized in that: It includes several intelligent sensor units and central processors installed at the end of each feeder of the distribution network; The intelligent sensing unit is used to implement the wave head calibration method based on sliding kurtosis extreme point according to any one of claims 1 to 4; The central processor is used to: first obtain the respective fault transient signal wave head arrival times from the intelligent sensor units; then select the earliest two intelligent sensor units from all wave head arrival times, and calculate the line distance between the two intelligent sensor units by extracting the installation positions of the two intelligent sensor units; finally calculate the position of the fault point based on the two wave head arrival times and the line distance between the two intelligent sensor units.

8. The fault location system according to claim 7, characterized in that: The calculated fault point location is expressed as: ； In the formula, Indicates the two smart sensor units with the earliest arrival time of the traveling wave, Intelligent sensor unit The arrival time of the traveling wave, is the traveling wave speed, Intelligent sensor unit The line distance between Indicates the fault point To Intelligent Sensing Unit line distance.

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