Power distribution network fault positioning detection method based on traveling wave detection

Abstract

The invention relates to the technical field of power distribution network fault detection, and discloses a power distribution network fault positioning detection method based on traveling wave detection, comprising the following steps: S1, traveling wave signal acquisition: deploying traveling wave measurement units at at least two monitoring points of a to-be-monitored line of a power distribution network, each traveling wave measurement unit comprising a traveling wave sensor and a data acquisition module, an adaptive traveling wave sensor is selected according to the bandwidth characteristic of the traveling wave signal of the power distribution network, and the sampling rate is dynamically adjusted through the data acquisition module, so that the sampling rate is matched with the bandwidth of the traveling wave signal to acquire a complete traveling wave signal; and S2, traveling wave head identification: processing the traveling wave signals acquired in the step S1 by adopting a preset traveling wave head accurate identification algorithm, identifying the traveling wave head and recording the timestamps of the traveling wave head reaching each monitoring point. The power distribution network fault positioning detection method based on traveling wave detection has the advantages that the power distribution network fault can be quickly positioned and detected according to traveling wave detection data, the positioning error is small and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network fault detection, in particular to a power distribution network fault positioning detection method based on traveling wave detection. BACKGROUND

[0002] The power distribution network fault refers to an abnormal state that the power grid operating state deviates from the normal range due to internal and external factors during the operation of the power distribution network, and the safe, stable and continuous transmission and distribution of electric energy cannot be realized. The power distribution network is a key component of the power system, which is responsible for converting high-voltage power from the transmission network into medium and low-voltage power and distributing it to industrial, commercial and residential end users. It is the "last mile" connecting power production and user electricity.

[0003] The power distribution network fault positioning is a key link to ensure power supply reliability. The existing positioning methods are usually impedance method and fault analysis method, but they are easily affected by line load fluctuation, transition resistance and distributed capacitance, and the positioning error is usually greater than 5%, which is difficult to meet the high-precision requirement. When the traveling wave distance measurement technology is used alone, there are problems such as poor sensor adaptability, low sampling rate matching degree and insufficient traveling wave head recognition accuracy. Therefore, a power distribution network fault positioning detection method based on traveling wave detection is proposed to solve the above problems. SUMMARY

[0004] (I) Technical problems solved In view of the shortcomings of the prior art, the present application provides a power distribution network fault positioning detection method based on traveling wave detection, which has the advantages of being able to quickly position and detect the power distribution network fault according to the traveling wave detection data, and the positioning error is small. The power distribution network fault positioning is a key link to ensure power supply reliability. The existing positioning methods are usually impedance method and fault analysis method, but they are easily affected by line load fluctuation, transition resistance and distributed capacitance, and the positioning error is usually greater than 5%, which is difficult to meet the high-precision requirement. When the traveling wave distance measurement technology is used alone, there are problems such as poor sensor adaptability, low sampling rate matching degree and insufficient traveling wave head recognition accuracy.

[0005] (II) Technical solutions The technical solutions of the present application to solve the above technical problems are as follows: a power distribution network fault positioning detection method based on traveling wave detection, comprising the following steps: S1, traveling wave signal acquisition: deploying traveling wave measurement units at at least two monitoring points of the power distribution network to be monitored, the traveling wave measurement unit comprising a traveling wave sensor and a data acquisition module, selecting an appropriate traveling wave sensor according to the bandwidth characteristics of the traveling wave signal of the power distribution network, and dynamically adjusting the sampling rate through the data acquisition module to match the sampling rate with the bandwidth of the traveling wave signal, so as to collect complete traveling wave signal; S2, traveling wave head recognition: a preset traveling wave head accurate recognition algorithm is used to process the traveling wave signals collected in step S1, to recognize the traveling wave head and record the time stamp of its arrival at each monitoring point; S3, multi-end time synchronization: a multi-end high-precision time synchronization technology is used to synchronize and calibrate the clocks of each monitoring point, to ensure that the time stamp recorded in step S2 has a consistent time reference; S4, big data processing and transmission: the traveling wave head data recognized in step S2 and the original traveling wave signals collected in step S1 are optimized, and the processed data are transmitted at high speed to the distribution network control center through a special communication link for distribution networks; S5, fusion of differential protection and traveling wave distance measurement data: the traveling wave data transmitted in step S4 and the protection data collected by the differential protection system of the distribution network are sampled and fused to obtain fusion data; S6, fault location: based on the synchronized time stamp in step S3, the fusion data in step S5 and the propagation speed of the traveling wave in the distribution network line, the location of the fault point of the distribution network is calculated.

[0006] The beneficial effects of the present application are: The distribution network fault positioning detection method based on traveling wave detection has the advantages of being able to quickly position and detect the fault of the distribution network according to the traveling wave detection data, and having small positioning error.

[0007] On the basis of the above technical solutions, the present application can also be improved as follows.

[0008] Further, in step S1, the traveling wave sensor is a Rogowski coil sensor or a Hall current sensor; when the fault traveling wave signal bandwidth is in the range of 10kHz-500kHz, a Rogowski coil current sensor is selected; when the fault traveling wave signal bandwidth is in the range of 5kHz-200kHz, a Hall voltage sensor is selected.

[0009] Further, in step S2, the specific steps of the traveling wave head identification algorithm are: S21: wavelet transform denoising processing is performed on the traveling wave signal, db4 wavelet basis function is used for 4-layer wavelet decomposition of the traveling wave signal, the d4 layer high frequency detail component obtained by the decomposition is taken as the denoising processing object, soft threshold denoising method is used for denoising the d4 layer high frequency detail component, and the value of the soft threshold is 1.2 times the standard deviation of the d4 layer high frequency detail component signal; S22: modulus maximum value calculation is performed on the d4 layer high frequency detail component after denoising to obtain a modulus maximum value sequence, and modulus maximum values corresponding to three continuous sampling points are extracted from the modulus maximum value sequence; S23: a dynamic threshold is set, and the value of the dynamic threshold is 0.4 times the maximum modulus maximum value of the d4 layer high frequency detail component within 10 ms before the traveling wave signal triggers; S24: the modulus maximum values of the three continuous sampling points extracted are compared with the dynamic threshold, and when the modulus maximum values of two continuous sampling points both exceed the dynamic threshold, the time point corresponding to the first sampling point exceeding the dynamic threshold is recorded as the time stamp of the traveling wave head reaching the monitoring point.

[0010] Further, in step S3, the implementation mode of the multi-terminal high-precision time synchronization technology is: S31: a Beidou dual-mode receiver is built in each traveling wave measurement unit, the Beidou dual-mode receiver supports satellite signal reception of BDS B1I frequency band and GPS L1 frequency band, initial time synchronization is realized by receiving satellite synchronization signals, and the initial synchronization accuracy is ≤50 ns in an unobstructed environment; S32: PTPv2 protocol packets are transmitted through a power distribution network dedicated single-mode optical fiber communication link, the transmission rate of the power distribution network dedicated single-mode optical fiber communication link is ≥1 Gbps, and the packet transmission delay is ≤5 ms; S33: a time compensation module is built in each traveling wave measurement unit, the time compensation module performs real-time time compensation on the satellite synchronization signals obtained by the Beidou dual-mode receiver according to the round-trip delay difference in the PTPv2 protocol packet transmission process, and the time synchronization error between each monitoring point after compensation is ≤1 μs; S34: the time synchronization accuracy of each monitoring point is checked once every 10 s, and when the time synchronization error obtained by the checking is >1 μs, a time synchronization restart mechanism is triggered, and the time synchronization operation of steps S31-S33 is re-executed.

[0011] Further, in step S4, the fault point position calculation includes: S41: the physical parameters of the line to be monitored are obtained, the physical parameters include the line length L and the conductor type between adjacent traveling wave measurement units A and B, the line length L is obtained through a GIS system, and the accuracy is ≤1 m; and the conductor type is LGJ-120 / 20 to LGJ-240 / 30, and the conductor material is aluminum steel mixed strand; S42: the conductor wave speed v after temperature compensation is calculated, and the compensation formula is v=v0×[1-α×(T-T0)], wherein v0=2.95×108 m / s, α=4.5×10-3 / ℃, T0=20℃, and T is the ambient temperature of the line to be monitored. 8m / s, 25℃ base wave velocity, a = 1.7 x 10⁻ 5 / ℃, is the aluminum conductor temperature coefficient, T is the real-time conductor temperature, which is obtained by a line temperature sensor, T0=25℃; S43: calculating the fault point distance by using a double-end positioning formula: if the time stamp t_A of the traveling wave head reaching A is less than the time stamp t_B of reaching B, then the distance d of the fault point distance A is v x (t_B-t_A) / 2; if t_B is less than t_A, then the distance d of the fault point distance B is v x (t_A-t_B) / 2, and d≤L; if d>L, then it is determined as a remote fault, and multi-end cooperative positioning is triggered.

[0012] Further, in the step S5, the sampling fusion comprises: first, time stamp alignment of the traveling wave data and the differential protection data, and then weight distribution according to data reliability-the traveling wave data weight is 0.6-0.8, and the differential protection data weight is 0.2-0.4, and the current and voltage data are fused by a weighted average algorithm.

[0013] Further, it further comprises the step: S7, a fault processing and self-healing cooperation step: based on the fault point position, the differential protection system triggers a circuit breaker to trip and isolate the fault, and the self-healing system selects an optimal recovery path according to the power grid topology, and controls the closing of a tie switch to restore power supply in a non-fault area.

[0014] Further, in the step S7, it further comprises anti-interference and fault-tolerant processing: an adaptive trap filter is used to suppress 3rd and 5th harmonic interference, when the data transmission of a certain monitoring point is interrupted, the historical traveling wave data of a standby monitoring point of the monitoring point is enabled, and the predicted data generated by an ARIMA prediction model is combined to supplement the interrupted data, so that the fault processing process is not interrupted. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a structural schematic diagram of the application; Figure 2 It is a schematic diagram of the step S2 of the application; Figure 3 It is a schematic diagram of the step S3 of the application; Figure 4 It is a schematic diagram of the step S4 of the application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0017] Embodiment one, by Figures 1-4A kind of power distribution network fault location detection method based on travelling wave detection is given, the present application includes the following steps: S1, travelling wave signal acquisition: at least two monitoring points of the line to be monitored in power distribution network are deployed travelling wave measurement unit, the travelling wave measurement unit includes travelling wave sensor and data acquisition module, according to the bandwidth characteristics of power distribution network travelling wave signal, select the appropriate travelling wave sensor, and by the data acquisition module dynamic adjustment sampling rate, make sampling rate and travelling wave signal bandwidth match, to collect complete travelling wave signal; S2, travelling wave head identification: the travelling wave signal collected in step S1 is processed using a preset travelling wave head accurate identification algorithm, identifies travelling wave head and records its time stamp arriving at each monitoring point; S3, multi-end time synchronization: each monitoring point clock is synchronized and calibrated using multi-end high-precision time synchronization technology, to ensure that the time stamp time reference recorded in step S2 is consistent; S4, big data processing and transmission: the travelling wave head data identified in step S2 and the original travelling wave signal collected in step S1 are optimized and processed, and the processed data are transmitted to the power distribution network control center through the special communication link of power distribution network; S5, differential protection and travelling wave distance data fusion: the travelling wave data transmitted in step S4 are fused with the protection data collected by the differential protection system of power distribution network, to obtain fusion data; S6, fault location: based on the time stamp synchronized in step S3, the fusion data in step S5 and the propagation speed of travelling wave in power distribution network line, the position of power distribution network fault point is calculated.

[0018] The present application ensures the integrity of travelling wave signal acquisition by deploying travelling wave measurement unit at least two monitoring points and adapting travelling wave sensor and sampling rate, provides reliable raw data for subsequent fault location;The application of travelling wave head identification algorithm realizes accurate identification and time stamp recording of travelling wave head, multi-end high-precision time synchronization technology ensures that the time stamp reference is consistent, avoids the influence of time deviation on fault location;Big data processing and transmission link improves data quality and transmission efficiency, data fusion operation enhances the reliability of data, and finally based on these data, the position of power distribution network fault point can be accurately calculated, to provide effective support for power distribution network fault investigation and repair.

[0019] In the embodiment, in the process of collecting the traveling wave signal in step S1, firstly, the fault traveling wave signal bandwidth possibly occurring in the power distribution network to be monitored is detected. If the fault traveling wave signal bandwidth is in the range of 10 kHz-500 kHz, a Rogowski coil current sensor is selected for the traveling wave measurement unit. If the fault traveling wave signal bandwidth is in the range of 5 kHz-200 kHz, a Hall voltage sensor is selected as the traveling wave sensor of the traveling wave measurement unit, so as to ensure that the traveling wave sensor can adapt to the current fault traveling wave signal bandwidth and effectively collect the traveling wave signal. Meanwhile, in the process of identifying the traveling wave head in step S2, the traveling wave head is accurately identified by using an algorithm. Firstly, in step S21, the collected traveling wave signal is decomposed into four layers by using a db4 wavelet base function. The d4 layer high-frequency detail component obtained after the decomposition is determined as the object of denoising processing. The standard deviation of the d4 layer high-frequency detail component signal is calculated, and 1.2 times the standard deviation is used as a soft threshold. The d4 layer high-frequency detail component is denoised by using a soft threshold denoising method. Then, in step S22, the modulus maximum of the denoised d4 layer high-frequency detail component is calculated to generate a modulus maximum sequence, and three consecutive sampling points corresponding to the modulus maximum are selected from the sequence. Then, in step S23, the maximum modulus maximum of the d4 layer high-frequency detail component within 10 ms before the traveling wave signal is triggered is obtained, and 0.4 times the maximum modulus maximum is set as a dynamic threshold. Finally, in step S24, the modulus maximum of the three consecutive sampling points is compared with the dynamic threshold. If the modulus maximum of two consecutive sampling points exceeds the dynamic threshold, the time point corresponding to the first sampling point exceeding the dynamic threshold is recorded as the time stamp of the traveling wave head arriving at the monitoring point.

[0020] In this embodiment, in the step S3 multi-terminal time synchronization process, the multi-terminal high-precision time synchronization technology is used to realize the clock synchronization and calibration of each monitoring point. First, step S31 is performed, a Beidou dual-mode receiver supporting BDS B1I and GPS L1 satellite signal reception is installed inside each traveling wave measurement unit, each receiver receives satellite synchronization signals of corresponding frequency bands to complete the initial time synchronization of each monitoring point, and in the unobstructed environment, the initial synchronization accuracy is ensured to meet the requirement of ≤50ns; then step S32 is performed, the PTPv2 protocol message is transmitted by using the power distribution network dedicated single-mode optical fiber communication link, which needs to meet the conditions of transmission rate ≥1Gbps and message transmission delay ≤5ms; then step S33 is performed, the time compensation module inside each traveling wave measurement unit calculates the round-trip delay difference of the PTPv2 protocol message in the transmission process in real time, and according to the delay difference, the satellite synchronization signal obtained by the Beidou dual-mode receiver is compensated in real time, so that the time synchronization error between the compensated monitoring points is controlled within ≤1μs; finally, step S34 is performed, the time synchronization accuracy of each monitoring point is checked every 10s, if the checking result shows that the time synchronization error is >1μs, the time synchronization restart mechanism is triggered immediately, and the time synchronization operation of steps S31-S33 is re-executed to maintain the synchronization accuracy of the clock of each monitoring point. Meanwhile, in the step S4 fault point position calculation process, first, step S41 is performed, the line length L between adjacent traveling wave measurement units A and B on the monitored line is obtained through the GIS system, and the length accuracy is ensured to be ≤1m, and the conductor type of the line is determined, which needs to be within the range of LGJ-120 / 20 to LGJ-240 / 30, and the conductor material is aluminum steel strand; then step S42 is performed, the real-time conductor temperature T is collected by the line temperature sensor, the 25℃ reference wave speed v0=2.95×10 8 m / s, the aluminum conductor temperature coefficient α=1.7×10⁻ 5 / ℃, T0=25℃, these parameters are substituted into the compensation formula v=v0×[1-α×(T-T0)] to calculate the wave speed v of the conductor after temperature compensation; finally, step S43 is performed, the time stamp t_A of the traveling wave head arriving at the traveling wave measurement unit A and the time stamp t_B of the traveling wave head arriving at the traveling wave measurement unit B are obtained, if t_A In the process of executing step S5, differential protection and traveling wave distance measurement data fusion, first, the time stamp alignment operation is performed on the traveling wave data transmitted to the distribution network control center in step S4 and the protection data collected by the distribution network differential protection system to ensure that the two types of data are consistent in the time dimension; then, according to the reliability of the traveling wave data and the differential protection data, the traveling wave data is assigned a weight of 0.6-0.8, and the differential protection data is assigned a weight of 0.2-0.4; finally, the current data and voltage data in the aligned time stamp traveling wave data and differential protection data are fused and calculated respectively by using the weighted average algorithm to obtain the fused current data and voltage data, that is, the sampling fusion process is completed.

[0021] In this embodiment, step S7, fault handling and self-healing cooperation step, is added to the original fault location method: based on the fault point position, the differential protection system triggers the circuit breaker to trip and isolate the fault, and the self-healing system selects the optimal recovery path according to the power grid topology to control the closing of the tie switch to restore power supply to the non-fault area; In the process of executing step S7, fault handling and self-healing cooperation step, anti-interference and fault tolerance processing are simultaneously performed. On the one hand, an adaptive notch filter is used to suppress the 3rd and 5th harmonic interference in the distribution network to reduce the influence of harmonic interference on the transmission of various signals and data processing in the fault handling process, and to ensure the stability of the signals and data. On the other hand, the data transmission state of each monitoring point is monitored in real time. When it is monitored that the data transmission of a certain monitoring point is interrupted, the historical traveling wave data stored by the standby monitoring point corresponding to the monitoring point is immediately enabled, and the ARIMA prediction model is started. According to the historical traveling wave data trend, corresponding prediction data is generated. The historical traveling wave data and the prediction data are combined to supplement the interrupted data of the monitoring point, ensuring the continuity of the required data in the fault handling process, so that the fault handling process can continue without interruption.

[0022] It should be noted that, in this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the listed element.

[0023] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for fault location and detection in distribution networks based on traveling wave detection, characterized in that, Includes the following steps: S1. Traveling wave signal acquisition: Deploy traveling wave measurement units at at least two monitoring points of the line to be monitored in the distribution network. The traveling wave measurement unit includes a traveling wave sensor and a data acquisition module. Select an appropriate traveling wave sensor according to the bandwidth characteristics of the traveling wave signal in the distribution network, and dynamically adjust the sampling rate through the data acquisition module to match the sampling rate with the bandwidth of the traveling wave signal in order to acquire the complete traveling wave signal. S2, Traveling wave head identification: The traveling wave signal collected in step S1 is processed using a preset traveling wave head accurate identification algorithm to identify the traveling wave head and record the timestamp of its arrival at each monitoring point; S3. Multi-terminal time synchronization: Multi-terminal high-precision time synchronization technology is used to synchronize and calibrate the clocks of each monitoring point to ensure that the timestamp time base recorded in step S2 is consistent. S4. Big Data Processing and Transmission: The traveling wave head data identified in step S2 and the raw traveling wave signal collected in step S1 are optimized and processed, and the processed data is transmitted to the distribution network control center at high speed through a dedicated communication link for the distribution network. S5. Differential protection and traveling wave ranging data fusion: The traveling wave data transmitted in step S4 is sampled and fused with the protection data collected by the distribution network differential protection system to obtain fused data; S6. Fault location: Based on the timestamp after synchronization in step S3, the fused data in step S5, and the propagation speed of traveling waves in the distribution network line, calculate the location of the fault point in the distribution network.

2. The method for fault location and detection in distribution networks based on traveling wave detection according to claim 1, characterized in that: In step S1, the traveling wave sensor is a Rogowski coil sensor or a Hall current sensor; wherein, when the bandwidth of the fault traveling wave signal is in the range of 10kHz-500kHz, a Rogowski coil current sensor is selected; when the bandwidth of the fault traveling wave signal is in the range of 5kHz-200kHz, a Hall voltage sensor is selected.

3. A method for fault location and detection in a distribution network based on traveling wave detection according to claim 1 or 2, characterized in that: In step S2, the specific steps of the traveling wave head accurate identification algorithm are as follows: S21: Perform wavelet transform denoising on the traveling wave signal. Use the db4 wavelet basis function to perform 4-level wavelet decomposition on the traveling wave signal. Use the d4-level high-frequency detail components obtained by decomposition as the denoising object. Use the soft threshold denoising method to denoise the d4-level high-frequency detail components. The soft threshold value is 1.2 times the standard deviation of the d4-level high-frequency detail component signal. S22: Calculate the modulus maxima of the high-frequency detail components of the denoised d4 layer to obtain a modulus maxima sequence, and extract the modulus maxima corresponding to three consecutive sampling points from the modulus maxima sequence; S23: Set a dynamic threshold, wherein the value of the dynamic threshold is 0.4 times the maximum modulus of the high-frequency detail component of layer d4 within 10ms before the traveling wave signal is triggered; S24: Compare the maximum modulus of the extracted three consecutive sampling points with the dynamic threshold. When the maximum modulus of two consecutive sampling points exceeds the dynamic threshold, record the time corresponding to the first sampling point that exceeds the dynamic threshold as the timestamp of the traveling wave head arriving at the monitoring point.

4. The method for fault location and detection in a distribution network based on traveling wave detection according to claim 1, characterized in that: In step S3, the multi-terminal high-precision time synchronization technology is implemented as follows: S31: Each traveling wave measurement unit has a built-in Beidou dual-mode receiver. The Beidou dual-mode receiver supports the reception of satellite signals in the BDSB1I band and the GPSL1 band. It achieves initial time synchronization by receiving satellite synchronization signals. In an unobstructed environment, the initial synchronization accuracy is ≤50ns. S32: Transmit PTPv2 protocol messages through a dedicated single-mode fiber optic communication link for power distribution networks, wherein the transmission rate of the dedicated single-mode fiber optic communication link for power distribution networks is ≥1Gbps and the message transmission delay is ≤5ms. S33: Each traveling wave measurement unit has a built-in time compensation module. The time compensation module performs real-time time compensation on the satellite synchronization signal acquired by the Beidou dual-mode receiver based on the round-trip delay difference in the PTPv2 protocol message transmission process. After compensation, the time synchronization error between each monitoring point is ≤1μs. S34: The time synchronization accuracy of each monitoring point is checked every 10 seconds. When the time synchronization error obtained by the check is >1μs, the time synchronization restart mechanism is triggered, and the time synchronization operation of steps S31-S33 is re-executed.

5. The method for fault location and detection in a distribution network based on traveling wave detection according to claim 4, characterized in that: In step S4, the calculation of the fault location includes: S41: Obtain the physical parameters of the line to be monitored. The physical parameters include the line length L between adjacent traveling wave measurement units A and B and the conductor type. The line length L is obtained through the GIS system with an accuracy of ≤1m. The conductor type is LGJ-120 / 20 to LGJ-240 / 30, and the conductor material is aluminum-steel stranded. S42: Calculate the conductor wave velocity v after temperature compensation. The compensation formula is: v = v0 × [1 - α × (T - T0)], Where v0 = 2.95 × 10 8 m / s, which is the reference wave velocity at 25℃, α=1.7×10⁻ 5 / ℃ is the temperature coefficient of the aluminum conductor, and T is the real-time conductor temperature, which is obtained through the line temperature sensor. T0=25℃; S43: Calculate the distance to the fault point using the dual-end positioning formula: If the timestamp t_A of the traveling wave head arriving at A is less than the timestamp t_B of arriving at B, then the distance from the fault point to A is d = v × (t_B - t_A) / 2; if t_B < t_A, then the distance from the fault point to B is d = v × (t_A - t_B) / 2, and d ≤ L; if d > L, then it is determined to be a remote fault, triggering multi-end collaborative positioning.

6. The method for fault location and detection in a distribution network based on traveling wave detection according to claim 1, characterized in that: In step S5, the sampling fusion includes: first, aligning the traveling wave data and differential protection data with timestamps, then assigning weights according to data reliability—traveling wave data weight 0.6-0.8, differential protection data weight 0.2-0.4, and fusing current and voltage data through a weighted average algorithm.

7. A method for fault location and detection in a distribution network based on traveling wave detection according to any one of claims 1-6, characterized in that: It also includes the following steps: S7. Fault handling and self-healing coordination steps: Based on the location of the fault point, the differential protection system triggers the circuit breaker to trip and isolate the fault. The self-healing system selects the optimal recovery path according to the power grid topology and controls the tie switch to close to restore power supply to the non-faulty area.

8. The method for fault location and detection in a distribution network based on traveling wave detection according to claim 7, characterized in that: Step S7 also includes anti-interference and fault tolerance processing: an adaptive notch filter is used to suppress the 3rd and 5th harmonic interference. When the data transmission of a certain monitoring point is interrupted, the historical traveling wave data of the backup monitoring point of that monitoring point is activated, and the interrupted data is supplemented by the prediction data generated by the ARIMA prediction model to ensure that the fault handling process is not interrupted.

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