Accurate distribution network fault positioning method and device based on high-frequency signal wave head recognition

Abstract

The invention relates to the technical field of power system distribution network fault detection, in particular to a distribution network fault accurate positioning method and device based on high-frequency signal wave head identification, and the device comprises a mutual inductor which is used for collecting voltage and current traveling wave signals and voltage and current power frequency signals of a detection line. The power frequency signals comprise a three-phase voltage, a three-phase current, a zero sequence voltage and a zero sequence current, the traveling wave signal and power frequency signal collection module is used for carrying out Karrenbel conversion on the three-phase voltage and current traveling wave signals collected by the mutual inductor to obtain line mode voltage signals, and the line mode voltage signals are connected with the traveling wave signal and power frequency signal collection module. And after amplification and filtering processing, the signals are collected through a digital-to-analog converter. According to the method, fault traveling waves can be identified more accurately, data can be stored more accurately, the correctness of waveform data in each transmission stage is ensured, the robustness in the data transmission process is enhanced, and the accuracy of traveling wave positioning during faults is improved.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network fault detection technology, specifically to a method and device for accurate fault location in distribution networks based on high-frequency signal wavefront identification. Background Technology

[0002] In the current power distribution network field, fault location typically relies on tools such as feeder terminals or fault indicators, which have significant limitations in accurately pinpointing fault locations. This forces personnel to expand the patrol area and increase the difficulty of tracing the fault after it occurs. To achieve precise fault location, traveling wave detection is now employed. The distance from the fault to the location device is calculated using the travel time and wave velocity of the traveling wave in the overhead cable.

[0003] However, generally speaking, there are two main types of traveling wave ranging methods for fault location: single-end ranging and double-end ranging. Single-end ranging works by calculating the distance from the fault point to the measuring device based on the time difference between the first traveling wave measured by the detection device and the reflected second traveling wave when a line fault occurs. However, considering the multi-branch structure of actual distribution network lines, multiple reflections and refractions of the traveling wave can introduce significant errors into the calculations of single-end ranging devices. Therefore, using only single-end traveling wave ranging is insufficient to meet the requirements for accurate fault location.

[0004] Based on this, the present invention provides a method and apparatus for accurate location of distribution network faults based on high-frequency signal wavefront identification, so as to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for accurate fault location in distribution networks based on high-frequency signal wavefront identification, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention proposes a precise fault location device for distribution networks based on high-frequency signal wavefront identification, comprising:

[0008] The instrument transformer is used to collect voltage and current traveling wave signals and voltage and current power frequency signals of the detection line, wherein the power frequency signals include three-phase voltage, three-phase current, zero-sequence voltage and zero-sequence current;

[0009] The traveling wave signal and power frequency signal collection module is used to perform Kallenbel conversion on the three-phase voltage and current traveling wave signals collected by the transformer to obtain line-mode voltage signals. After amplification and filtering, the signals are collected by a digital-to-analog converter. At the same time, the power frequency signals are processed into identifiable signals and then transmitted to the waveform data processing unit.

[0010] The waveform recording trigger and waveform storage device is based on an FPGA and is equipped with two random access memories and two synchronous dynamic random access memories. It is used for fault traveling wave signal identification, waveform data cyclic storage and waveform recording trigger control. After waveform recording is triggered, the sampling point data of the specified interval is transferred to the synchronous dynamic random access memory.

[0011] The waveform data processing unit is used for fault traveling wave data screening, power frequency fault signal identification and alarm signal output, traveling wave zero point identification, fault data compression and wireless uploading. It collects power frequency signals and judges the steady-state fault characteristics of traveling waves through a built-in digital-to-analog converter.

[0012] Preferably, in the traveling wave signal and power frequency signal collection module, the Karenbell conversion obtains the linear mode signal through the equivalent transformation formula, as shown in equation (1):

[0013] (1);

[0014] in, , , K1 and K2 are the high-frequency traveling wave voltage and current signals acquired by the current transformer, and K3 are the converted line-mode signals.

[0015] The digital-to-analog converter uses an AD sampling chip with a 10MHz sampling frequency and a 12-bit data width, and provides clock signals and sampling control through an FPGA.

[0016] Preferably, in the waveform recording trigger and waveform storage device, the FPGA is started by a 20M external crystal oscillator, and two random access memories synchronously and cyclically store the line mode signal. The traveling wave waveform length is 1024us, and the maximum number of waveform recordings in 1s is 976.

[0017] The waveform recording trigger logic is as follows: each sampling point is compared with a set threshold. If a specified number of consecutive sampling points exceed the threshold, a waveform recording signal is activated.

[0018] The waveform recording data consists of the first 3499 sampling points and the last 6740 sampling points of the trigger sampling point. It is transmitted to the waveform data processing unit via SPI communication at a baud rate of 30M. Serial communication is used for waveform reading control and waveform recording trigger control.

[0019] Preferably, the conditions for judging the steady-state fault characteristics of the traveling wave in the waveform data processing unit include:

[0020] The three-phase current increases, the zero-sequence voltage increases, the line voltage decreases, the current suddenly decreases, the three-phase current is greater than the disconnection current setting and the angle between any two phase currents is greater than the disconnection current setting.

[0021] The waveform filtering logic is as follows: record the time T when the traveling wave steady-state fault occurs, set a time period [T-T1, T+T2], and only read waveform data whose recording time is within this interval from the synchronous dynamic random access memory as valid fault waveforms.

[0022] This invention also proposes a method for accurate fault location in distribution networks based on a high-frequency signal wavefront identification device, comprising the following steps:

[0023] S1. The voltage and current traveling wave signals and voltage and current power frequency signals of the detection line are collected by the current transformer to ensure that the two sampling circuits do not interfere with each other;

[0024] S2. The traveling wave signal and power frequency signal collection module performs Karen Bell conversion, amplification, filtering and digital-to-analog conversion on the traveling wave signal, and processes the power frequency signal into a recognizable signal before transmitting it to the waveform data processing unit.

[0025] S3. The waveform recording trigger and waveform storage device synchronously stores the line mode signal through dual random access memory. Based on threshold comparison and continuous sampling point judgment, it realizes fault traveling wave recording trigger and transfers the waveform data of the specified interval to dual synchronous dynamic random access memory.

[0026] S4. The waveform data processing unit judges the steady-state fault characteristics of the traveling wave based on the power frequency signal, filters out the valid fault waveforms, and wirelessly uploads the traveling wave zero point identification and fault data compression.

[0027] Preferably, in step S3, the specific process of triggering waveform recording is as follows: Random access memory 2 stores the line mode signal in real time and compares it with a threshold. If the number of consecutive sampling points exceeds the threshold, waveform recording is triggered. Random access memory 1 synchronously transfers the waveform data of the corresponding interval to the synchronous dynamic random access memory. The two synchronous dynamic random access memories are mirror backups of each other to ensure data integrity.

[0028] Preferably, in step S4, the power frequency signal is converted and collected by the digital-to-analog converter built into the waveform data processing unit, and used as the measurement value for judging the steady-state fault characteristics of the traveling wave; the fault data is compressed and uploaded wirelessly for subsequent dual-end ranging calculation of the fault location.

[0029] Compared with the prior art, the beneficial effects of the present invention are: the present invention can more accurately identify and save fault traveling waves, ensure the correctness of waveform data in each transmission stage, enhance the robustness of data transmission, and improve the accuracy of traveling wave location during faults. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the distribution network fault precise location device of the present invention;

[0031] Figure 2 This is a schematic diagram of the waveform recording trigger and waveform storage device of the present invention;

[0032] Figure 3 This is a schematic diagram of the module for collecting traveling wave signals and power frequency signals in this invention;

[0033] Figure 4 This is a schematic diagram of the traveling wave recording triggering and storage logic of the present invention;

[0034] Figure 5 This is a schematic diagram of the traveling wave steady-state fault triggering logic of the present invention. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] For examples, please refer to Figures 1 to 5 This invention proposes a precise fault location device for distribution networks based on high-frequency signal wavefront identification, comprising:

[0037] Instrument transformers are used to collect and detect voltage and current traveling wave signals and voltage and current power frequency signals of the detection line. The power frequency signals include three-phase voltage, three-phase current, zero-sequence voltage and zero-sequence current.

[0038] The traveling wave signal and power frequency signal collection module is used to perform Kelvin-Bell conversion on the three-phase voltage and current traveling wave signals collected by the transformer to obtain line-mode voltage signals. After amplification and filtering, the signals are collected by the digital-to-analog converter. At the same time, the power frequency signals are processed into recognizable signals and then transmitted to the waveform data processing unit.

[0039] The waveform recording trigger and waveform storage device is based on FPGA and is equipped with two random access memories and two synchronous dynamic random access memories. It is used for fault traveling wave signal identification, waveform data cyclic storage and waveform recording trigger control. After waveform recording is triggered, the sampling point data of the specified interval is transferred to the synchronous dynamic random access memory.

[0040] The waveform data processing unit is used for fault traveling wave data screening, power frequency fault signal identification and alarm signal output, traveling wave zero point identification, fault data compression and wireless uploading. It collects power frequency signals and judges the steady-state fault characteristics of traveling waves through the built-in digital-to-analog converter.

[0041] In this embodiment, it should also be noted that in the traveling wave signal and power frequency signal collection module, the Karenbell conversion obtains the line mode signal through the equivalent transformation formula, as shown in equation (1):

[0042] (1);

[0043] in, , , K1 and K2 are the high-frequency traveling wave voltage and current signals acquired by the current transformer, and K3 are the converted line-mode signals.

[0044] The digital-to-analog converter uses an AD sampling chip with a 10MHz sampling frequency and a 12-bit data width, and the clock signal and sampling control are provided by an FPGA;

[0045] In this embodiment, it should also be noted that in the waveform recording trigger and waveform storage device, the FPGA uses a 20M external crystal oscillator for oscillation, and two random access memories synchronously and cyclically store the line mode signal. The traveling wave waveform length is 1024us, and the maximum number of waveform recordings within 1 second is 976.

[0046] The waveform recording trigger logic is as follows: each sampling point is compared with a set threshold. If a specified number of consecutive sampling points exceed the threshold, a waveform recording signal is activated.

[0047] The waveform recording data consists of the first 3499 sampling points and the last 6740 sampling points of the trigger sampling point. It is transmitted to the waveform data processing unit via SPI communication at a baud rate of 30M. Serial communication is used for waveform reading control and waveform recording trigger control.

[0048] In this embodiment, it should also be noted that the conditions for judging the traveling wave steady-state fault characteristics of the waveform data processing unit include:

[0049] The three-phase current increases, the zero-sequence voltage increases, the line voltage decreases, the current suddenly decreases, the three-phase current is greater than the disconnection current setting and the angle between any two phase currents is greater than the disconnection current setting.

[0050] The waveform filtering logic is as follows: record the time T when the traveling wave steady-state fault is triggered, set a time period [T-T1, T+T2], and only read waveform data whose recording time is within this interval from the synchronous dynamic random access memory as valid fault waveforms;

[0051] Example 1: In practical applications, this invention also proposes a method for accurate fault location in distribution networks based on a high-frequency signal wavefront identification device. Specifically, it includes the following steps:

[0052] S1. The voltage and current traveling wave signals and voltage and current power frequency signals of the detection line are collected by the current transformer to ensure that the two sampling circuits do not interfere with each other;

[0053] S2. The traveling wave signal and power frequency signal collection module performs Karen Bell conversion, amplification, filtering and digital-to-analog conversion on the traveling wave signal, and processes the power frequency signal into a recognizable signal before transmitting it to the waveform data processing unit.

[0054] S3. The waveform recording trigger and waveform storage device synchronously stores the line mode signal through dual random access memory. Based on threshold comparison and continuous sampling point judgment, it realizes fault traveling wave recording trigger and transfers the waveform data of the specified interval to dual synchronous dynamic random access memory.

[0055] It should also be noted that the specific process of triggering waveform recording is as follows: Random access memory 2 stores the line mode signal in real time and compares it with the threshold. If the number of consecutive sampling points exceeds the threshold, waveform recording is triggered.

[0056] The random access memory (RAM) synchronously transfers the waveform data of the corresponding interval to the synchronous dynamic random access memory (DRAM) in a loop. The two synchronous DRAMs serve as mirror backups of each other to ensure data integrity.

[0057] S4. The waveform data processing unit judges the steady-state fault characteristics of the traveling wave based on the power frequency signal, filters out the valid fault waveforms, and wirelessly uploads the traveling wave zero point identification and fault data compression.

[0058] It should also be noted that the power frequency signal is converted and collected by the digital-to-analog converter built into the waveform data processing unit, and used as the measurement value for judging the characteristics of traveling wave steady-state faults.

[0059] The fault data is compressed and uploaded wirelessly for subsequent dual-end ranging calculation of the fault location.

[0060] In this embodiment, for details, please refer to... Figure 1 The embodiments of the present invention include:

[0061] Current transformers used for acquiring voltage / current traveling wave signals and voltage / current power frequency signals;

[0062] The module is used for collecting traveling wave signals and power frequency signals. It is used to convert the three-phase voltage / current traveling wave signals collected by the transformer into subsequent identifiable line-mode voltage signals and to convert the power frequency signals into identifiable signals.

[0063] A waveform recording trigger and waveform storage device is used for identifying fault traveling wave signals and collecting and storing waveform data;

[0064] The waveform data processing unit is used to filter fault traveling wave data, identify power frequency fault signals, and issue alarm signals.

[0065] Among them, the current transformer is the primary data collection device. It must not only be able to accurately collect power frequency signals such as three-phase voltages UA, UB, UC, three-phase currents IA, IB, IC, as well as zero-sequence voltage U0 and zero-sequence current I0, but also be able to collect high-frequency traveling wave voltage / current signals.

[0066] Furthermore, the two sampling loops should not have too much mutual influence in the design, so as not to affect the signal acquisition of each other;

[0067] The module for collecting traveling wave signals and power frequency signals is a module that further processes the power frequency and high frequency signals collected by the current transformer. Its purpose is to convert the data into recognizable and correct data for transmission to subsequent processing modules.

[0068] Further processing includes amplification and filtering of high-frequency voltage / current traveling wave signals, as well as Karenbell conversion;

[0069] The specific Kelvin transformation matrix is ​​as follows:

[0070] ;

[0071] To facilitate circuit processing and subsequent transformations, this invention directly employs an equivalent transformation formula to obtain the differential signal, wherein... , , The high-frequency traveling wave voltage / current signal acquired by the current transformer. , This is the linear mode signal after matrix transformation, which is also the linear mode signal for subsequent high-frequency traveling wave processing. The formula is as follows:

[0072] ;

[0073] The current transformer module and the module for collecting traveling wave signals and power frequency signals can be implemented using existing solutions, and no restrictions are imposed here.

[0074] This invention also uses an external digital-to-analog converter for the acquisition and processing of high-frequency traveling wave voltage / current signals. Since the frequency of high-frequency signals in power system distribution network faults can reach 100kHz or even higher, this invention uses an AD sampling chip with a sampling frequency of 10MHz to achieve higher sampling accuracy. The data bit width is 12 bits, and it mainly acquires the linear mode components after Kelvin matrix transformation.

[0075] The AD chip mainly uses a programmable gate array processor (FPGA) to provide clock signals and sampling control;

[0076] like Figure 2The image shows a waveform recording trigger and waveform storage device, which mainly uses an FPGA as the core module.

[0077] To ensure that the FPGA can meet the 10MHz sampling frequency of the external digital-to-analog converter, it needs to be started by an external crystal oscillator with a frequency of 20MHz or even higher.

[0078] In order to achieve more precise data processing and peripheral control, a 20MHz crystal oscillator is used as the external crystal oscillator for the FPGA in this invention;

[0079] In addition, two random access memories are used in this module to cyclically store the line mode signal at each sampling point. The traveling wave waveform length is 1024us, and the maximum number of times the waveform can be recorded in 1 second is 976.

[0080] The waveform recording trigger logic is as follows: Figure 4 As shown, for each sampling point, it is compared with the set threshold. If the threshold is not exceeded, it waits for the next comparison.

[0081] If the threshold is exceeded at any point, the waveform recording trigger judgment logic will be initiated. If the threshold is exceeded for a specified number of consecutive sampling points, it is considered that a fault traveling wave has generated a traveling wave recording signal.

[0082] The advantages of using this triggering mechanism are: it can filter out some non-faulty interference;

[0083] It can also identify waveforms with different degrees of attenuation in different scenarios;

[0084] The above-mentioned starting logic is based on the values ​​in random access memory 2. The data contents of random access memory 1 and random access memory 2 are completely consistent. The advantage of this method is that the data storage content will not be affected in the logic of data processing and data storage, thus ensuring the accuracy of the data and the real-time performance of the logic processing.

[0085] In this invention, after the signal of the fault traveling wave recording is generated, the first 3499 sampling points and the last 6740 sampling points are used as the recording data and stored cyclically in the synchronous dynamic random access memory (SDRAM). In order to ensure the accuracy of the data, two SDRAMs are used to store the recording data.

[0086] In the event of a special circumstance that causes damage to one SDRAM or a partial data error, the other SDRAM, as a mirror backup of the correct data, can provide the correct waveform data for subsequent data processing units.

[0087] By taking the above steps, the robustness of data content accuracy during data transmission is improved, ensuring that the subsequent data processing unit can obtain the correct waveform data and improving the accuracy of traveling wave positioning.

[0088] In terms of FPGA control, serial port and SPI communication are used for external communication. This is because the amount of waveform data is huge and needs to be transmitted to the subsequent processing unit quickly.

[0089] Therefore, a faster SPI is needed for waveform data transmission. This invention uses a 30M baud rate for communication.

[0090] The main purpose of serial communication is for the signal processing unit to control the waveform reading and recording triggering of the FPGA.

[0091] The present invention also includes a waveform data processing unit for filtering fault traveling wave data, identifying power frequency fault signals and issuing alarm signals, and compressing and uploading waveform data.

[0092] like Figure 5 The logic diagram for the traveling wave steady-state fault characteristics is shown in the diagram. The voltage and current measurements used in this diagram come from the digital-to-analog converter built into the MCU, and the input of the digital-to-analog converter comes from the module that collects the traveling wave signal and the power frequency signal.

[0093] The reason for identifying the steady-state fault characteristics of this type of wave is to eliminate traveling wave signals of similar faults caused by other non-faults, thus avoiding misjudgment;

[0094] The logic of the waveform data processing unit to read waveforms from the waveform recording trigger and the waveform storage device is to record the steady-state fault start time T of the traveling wave and set the time period [T-T1, T+T2].

[0095] The waveform data processing unit reads the waveform recording time in the SDRAM and determines whether it is within the interval [T-T1, T+T2]. If it is within the interval, it is determined to be a valid fault waveform and read for further processing.

[0096] If the recording time is not within the range, or if there are no traveling wave steady-state fault characteristics, then the waveform is filtered out and no further processing is performed.

[0097] In summary, the distribution network fault accurate location method and device based on high-frequency signal wavefront identification of the present invention further refines the analysis of traveling wave signal identification on the basis of traditional dual-end ranging. It can effectively avoid various interference signals, clutter signals, and non-fault traveling wave signals generated by switch opening and closing, etc., and can more effectively identify fault information, increasing the accuracy of positioning. At the same time, it enhances the robustness of data processing and reduces the probability of large-distance positioning deviation caused by accidental partial data errors.

[0098] Through the above steps, the present invention can more accurately identify and save fault traveling waves, ensure the correctness of waveform data at each transmission stage, enhance the robustness of data transmission, and improve the accuracy of traveling wave location during faults.

[0099] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0100] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A precise fault location device for distribution networks based on high-frequency signal wavefront identification, characterized in that, include: The instrument transformer is used to collect voltage and current traveling wave signals and voltage and current power frequency signals of the detection line, wherein the power frequency signals include three-phase voltage, three-phase current, zero-sequence voltage and zero-sequence current; The traveling wave signal and power frequency signal collection module is used to perform Kallenbel conversion on the three-phase voltage and current traveling wave signals collected by the transformer to obtain line-mode voltage signals. After amplification and filtering, the signals are collected by a digital-to-analog converter. At the same time, the power frequency signals are processed into identifiable signals and then transmitted to the waveform data processing unit. The waveform recording trigger and waveform storage device is based on an FPGA and is equipped with two random access memories and two synchronous dynamic random access memories. It is used for fault traveling wave signal identification, waveform data cyclic storage and waveform recording trigger control. After waveform recording is triggered, the sampling point data of the specified interval is transferred to the synchronous dynamic random access memory. The waveform data processing unit is used for fault traveling wave data screening, power frequency fault signal identification and alarm signal output, traveling wave zero point identification, fault data compression and wireless uploading. It collects power frequency signals and judges the steady-state fault characteristics of traveling waves through a built-in digital-to-analog converter.

2. The distribution network fault precise location device based on high-frequency signal wavefront identification according to claim 1, characterized in that, In the traveling wave signal and power frequency signal collection module, the Karenbell conversion obtains the linear mode signal through the equivalent transformation formula, as shown in equation (1): （1）； in, , , K1 and K2 are the high-frequency traveling wave voltage and current signals acquired by the current transformer, and K3 are the converted line-mode signals. The digital-to-analog converter uses an AD sampling chip with a 10MHz sampling frequency and a 12-bit data width, and provides clock signals and sampling control through an FPGA.

3. The distribution network fault precise location device based on high-frequency signal wavefront identification according to claim 2, characterized in that, In the waveform recording trigger and waveform storage device, the FPGA is started by a 20M external crystal oscillator, and two random access memories synchronously and cyclically store the line mode signal. The traveling wave waveform length is 1024us, and the maximum number of waveform recordings in 1s is 976. The waveform recording trigger logic is as follows: each sampling point is compared with a set threshold. If a specified number of consecutive sampling points exceed the threshold, a waveform recording signal is activated. The waveform recording data consists of the first 3499 sampling points and the last 6740 sampling points of the trigger sampling point. It is transmitted to the waveform data processing unit via SPI communication at a baud rate of 30M. Serial communication is used for waveform reading control and waveform recording trigger control.

4. The distribution network fault precise location device based on high-frequency signal wavefront identification according to claim 3, characterized in that, The traveling wave steady-state fault characteristic judgment conditions of the waveform data processing unit include: The three-phase current increases, the zero-sequence voltage increases, the line voltage decreases, the current suddenly decreases, the three-phase current is greater than the disconnection current setting and the angle between any two phase currents is greater than the disconnection current setting. The waveform filtering logic is as follows: record the time T when the traveling wave steady-state fault occurs, set a time interval [T - T1, T + T2], and only read waveform data whose recording time is within this interval from the synchronous dynamic random access memory as valid fault waveforms.

5. A method for accurately locating distribution network faults using a distribution network fault accurate location device based on high-frequency signal wavefront identification as described in any one of claims 1-4, characterized in that, Includes the following steps: S1. The voltage and current traveling wave signals and voltage and current power frequency signals of the detection line are collected by the current transformer to ensure that the two sampling circuits do not interfere with each other; S2. The traveling wave signal and power frequency signal collection module performs Karen Bell conversion, amplification, filtering and digital-to-analog conversion on the traveling wave signal, and processes the power frequency signal into a recognizable signal before transmitting it to the waveform data processing unit. S3. The waveform recording trigger and waveform storage device synchronously stores the line mode signal through dual random access memory. Based on threshold comparison and continuous sampling point judgment, it realizes fault traveling wave recording trigger and transfers the waveform data of the specified interval to dual synchronous dynamic random access memory. S4. The waveform data processing unit judges the steady-state fault characteristics of the traveling wave based on the power frequency signal, filters out the valid fault waveforms, and wirelessly uploads the traveling wave zero point identification and fault data compression.

6. The method for accurate fault location in distribution networks based on high-frequency signal wavefront identification according to claim 5, characterized in that, In step S3, the specific process of triggering waveform recording is as follows: Random access memory 2 stores the line mode signal in real time and compares it with the threshold. If the number of consecutive sampling points exceeds the threshold, waveform recording is triggered. Random access memory 1 synchronously transfers the waveform data of the corresponding interval to the synchronous dynamic random access memory. The two synchronous dynamic random access memories are mirror backups of each other to ensure data integrity.

7. The method for accurate fault location in distribution networks based on high-frequency signal wavefront identification according to claim 6, characterized in that, In step S4, the power frequency signal is converted and collected by the digital-to-analog converter built into the waveform data processing unit, and used as the measurement value for judging the steady-state fault characteristics of the traveling wave; the fault data is compressed and uploaded wirelessly for subsequent dual-end ranging calculation of the fault location.

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