Multi-branch line traveling wave positioning method based on fault ratio quantity

Abstract

The embodiment of the invention discloses a multi-branch line traveling wave positioning method based on fault ratio quantity. The method comprises the following steps: establishing a fault positioning algorithm according to the inherent length of a line and traveling wave refraction and reflection; in the multi-branch line, based on the fault positioning algorithm, establishing a fault position determination rule under different fault positions by using a numerical relationship between the distance from each end to the fault point and the length of the line branch; collecting fault voltage traveling wave data; based on a trained neural network model, processing the fault voltage traveling wave data to obtain traveling wave head arrival time reflecting fault distance information; according to the traveling wave head arrival time reflecting the fault distance information, using the fault position determination rule to realize fault positioning; accurate positioning of a power distribution network line fault position can be realized.

Description

Technical Field

[0001] The invention relates to the technical field of distribution network fault identification, and in particular to a multi-branch line traveling wave positioning method based on fault ratio quantity. Background Art

[0002] Distribution network is a key link in power transmission, and accurate location of line faults is of great significance for rapid repair of faulty lines and improving the reliability of power supply in distribution network.

[0003] The distribution network has obvious differences in topology from the traditional transmission network. Its radial distribution and complex multi-branch structure will make it impossible for traditional single-end traveling wave method and double-end traveling wave method to accurately determine the fault section. The change of line parameters has an important impact on the accuracy of traveling wave positioning. The accuracy of traveling wave ranging is also affected by the speed of the traveling wave. When using the speed to locate the fault, there will be errors of varying degrees.

[0004] Therefore, a more accurate distribution network line fault location method is urgently needed. Summary of the invention

[0005] The main purpose of the present invention is to provide a multi-branch line traveling wave positioning method based on fault ratio quantity, which can improve the accuracy of fault positioning.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a multi-branch line traveling wave positioning method based on fault ratio quantity, the method comprising:

[0007] Establish a fault location algorithm based on the inherent length of the line and the reflection of the traveling wave;

[0008] Based on the fault location algorithm in a multi-branch line, a fault location determination rule under different fault locations is established by utilizing the numerical relationship between the distance from each end to the fault point and the line branch length;

[0009] Collect fault voltage traveling wave data;

[0010] Based on the trained neural network model, the fault voltage traveling wave data is processed to obtain the traveling wave head arrival time reflecting the fault distance information;

[0011] According to the arrival time of the traveling wave head reflecting the fault distance information, the fault location is realized by using the fault position determination rule.

[0012] A second aspect of the present application provides a multi-branch line traveling wave positioning device based on a fault ratio, comprising:

[0013] Establishing a module for establishing a fault location algorithm based on the inherent length of the line and the reflection of the traveling wave;

[0014] The establishment module is further used to establish fault location determination rules under different fault locations in a multi-branch line based on the fault location algorithm and using the numerical relationship between the distance from each end to the fault point and the line branch length;

[0015] An acquisition module, used for acquiring fault voltage traveling wave data;

[0016] A prediction module, used to process the fault voltage traveling wave data based on a trained neural network model to obtain a traveling wave head arrival time reflecting fault distance information;

[0017] The processing module is used to realize fault location according to the arrival time of the traveling wave head reflecting the fault distance information and using the fault position determination rule.

[0018] A third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the first aspect and any possible implementation thereof.

[0019] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes each step of the method described in the first aspect.

[0020] The present application provides a multi-branch line traveling wave positioning method based on fault ratio quantity, by establishing a fault positioning algorithm based on the inherent length of the line and the refraction and reflection of the traveling wave; based on the fault positioning algorithm in the multi-branch line, using the numerical relationship between the distance from each end to the fault point and the line branch length, establishing a fault position determination rule under different fault positions; collecting fault voltage traveling wave data; based on a trained neural network model, processing the fault voltage traveling wave data to obtain the arrival time of the traveling wave head reflecting the fault distance information; according to the arrival time of the traveling wave head reflecting the fault distance information, using the fault position determination rule to achieve fault positioning; by analyzing the inherent length of the line and the refraction and reflection transmission path of the traveling wave, a fault positioning method based on refraction and reflection traveling waves is proposed, without the need to calculate the traveling wave transmission speed, thereby improving the influence of line parameters on the accuracy of traveling wave positioning; establishing the numerical relationship between the distance from multiple ends of the multi-branch line to the fault point and the line branch length, locating the section according to the fault position judgment criterion, and simultaneously cooperating with the neural network to analyze the fault information from the time-frequency domain perspective, thereby improving the accuracy of fault positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] in:

[0023] Figure 1 A flow chart of a method for locating a multi-branch line traveling wave based on a fault ratio value provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a distribution network topology structure provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of a CNN-LSTM hybrid neural network model provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the structure of a multi-branch line traveling wave positioning device based on fault ratio provided in an embodiment of the present application;

[0027] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0029] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] The traveling wave ranging method involved in the embodiments of the present application mainly utilizes the transient traveling wave signal generated by the fault point and transmitted along the transmission line to each substation in the power grid at a speed close to the speed of light, records the precise time value of the GPS synchronized clock when the fault traveling wave arrives at the measurement point of each substation, and calculates the precise location of the fault point based on the GPS time difference measured by the substations on both sides of the fault line.

[0032] The basic principle of traveling wave positioning technology in distribution network fault detection is mainly based on the traveling wave phenomenon in electromagnetic transient processes. When a fault occurs in the distribution network, two types of traveling waves will propagate on the power cables: voltage traveling waves and current traveling waves. Both types of traveling waves propagate along the line to both ends at a speed close to the speed of light. Traveling wave positioning technology uses this physical phenomenon to capture and record traveling wave signals in real time by setting up several traveling wave sensors or monitoring terminals along the distribution line. Once a fault occurs, these sensors will receive the initial traveling wave signal generated by the fault and send its time and amplitude information to the data analysis center. The data analysis center uses the traveling wave ranging principle to accurately measure and calculate the arrival time of the traveling waves received by each sensor, combined with the known traveling wave propagation speed, to deduce the approximate location of the fault.

[0033] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0034] See also Figure 1 , is a flow chart of a multi-branch line traveling wave positioning method based on fault ratio provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0035] 101. Establish a fault location algorithm based on the inherent length of the line and the reflection of the traveling wave.

[0036] The execution subject of the method in the embodiment of the present application can be a multi-branch line traveling wave positioning device based on the fault ratio quantity, which can be implemented using a terminal device in actual application, such as a server or a computer.

[0037] Specifically, when a fault occurs in a transmission line, the fault traveling wave will be refracted and reflected multiple times in the line, but the transmission speed of the traveling wave is closely related to the line parameters, and parameter changes will cause unstable changes in the speed of the traveling wave. In order to avoid this situation, the method in the embodiment of the present application is improved in the traditional single-end and double-end positioning, and a new fault point calculation formula is obtained, which omits the preset and calculation of the speed of the traveling wave, reducing the influence of the line parameters on the positioning.

[0038] In an optional implementation, the above-mentioned fault location algorithm based on the inherent length of the line and the traveling wave refraction and reflection includes a single-ended fault point distance measurement formula and a double-ended fault point distance measurement formula, wherein the above-mentioned single-ended fault point distance measurement formula includes:

[0039]

[0040] The above two-terminal fault point distance measurement formula includes:

[0041]

[0042] Where, d represents the distance from the fault point to the measuring end, and L represents the total length of the line;

[0043] Δt=t M -t N , Δt fM =t fM -t M , Δt fN =t fN -t N , t M and t N is the time it takes for the preceding traveling wave to reach the traveling wave detection devices at both ends, t fM and t fN It is the time it takes for the reflected traveling wave from the fault point to reach the traveling wave detection devices at both ends.

[0044] The above improved single-end and double-end positioning calculation formula omits the preset and calculation of the wave velocity of the traveling wave, reducing the influence of line parameters on positioning. Single-end fault point ranging focuses on the measurement of the signal at one end. After obtaining the arrival time of the preceding traveling wave, it is only necessary to identify the reflected traveling wave head of the signal at one end; double-end fault point ranging focuses on the measurement of the signals at both ends. After obtaining the arrival time of the preceding traveling wave, it is still necessary to identify the reflected traveling wave head of the signals at both ends. However, the latter can solve the problem of clock synchronization error of the acquisition device during the positioning process.

[0045] 102. Based on the above fault location algorithm in a multi-branch line, the numerical relationship between the distance from each end to the fault point and the line branch length is used to establish a fault location determination rule under different fault locations.

[0046] In multi-branch lines, each end of the complex distribution network topology has one or more nodes, and more complex node refraction and reflection will occur, so double-end positioning is difficult to apply to multi-branch lines. Therefore, based on the aforementioned fault location algorithm, the fault location determination rules in multi-branch lines can be expanded, mainly the numerical relationship between the distance from each end to the fault point and the length of the line branch, and the branch determination and fault location algorithm can be combined to solve the fault location.

[0047] In an optional implementation, the above step 102 includes:

[0048] Based on the above fault location algorithm, in multi-branch lines, it is extended to the distance between the fault point and the endpoints between any two end points, and the fault point distance measurement formula of multi-branch lines is obtained;

[0049] Based on the above-mentioned fault point distance measurement formula for the multi-branch line, a fault ratio relationship is defined, and the fault location determination rules under the above-mentioned different fault locations are determined.

[0050] Further optionally, the above-mentioned fault point distance measurement formula for the multi-branch line includes:

[0051]

[0052] in, The fault first traveling wave reaches line M x M y The time difference between the two ends, The fault reflected wave reaches line M respectively. x M y Time at both ends.

[0053] Figure 2 A schematic diagram of a distribution network topology structure provided in an embodiment of the present application. Figure 2 As shown, an example analysis is performed based on this topology:

[0054] In such Figure 2 In the multi-branch line shown, the fault point may exist in the MxPx section or in the PxPy section.

[0055] When the fault point is in the M1P1 section, the difference in distance between the two-end preceding traveling waves and the time it takes for the reflected traveling wave to reach M1 can be calculated by measuring M1M2 as the two ends:

[0056]

[0057] If the fault point is on the P1P2 segment:

[0058]

[0059] The time difference between the arrival of the M1 end and the preceding traveling wave.

[0060] Therefore, in complex multi-branch lines, the fault point between any two endpoints and the endpoint M x The distance can be expressed as:

[0061]

[0062] Among them, MxMy is Figure 2 The lines at any two endpoints of the multi-branch line shown, The fault first traveling wave reaches line M x M y The time difference between the two ends, The fault reflected wave reaches line M respectively. x M y Time at both ends.

[0063] From the above formula, we can know that after the fault traveling wave is generated, the fault point and the endpoint M x The distance is divided into the end point M x The distance to the fault point and the endpoint M x To the neighboring node P x When the distribution network system structure is known, the distance from any line endpoint to its adjacent node is constant. Therefore, the ratio relationship S is defined as:

[0064]

[0065] Indicates line M x P y The fixed length of the line; since the fault point can occur at any position on the line, the S value of each endpoint can be known:

[0066]

[0067] Therefore, different fault conditions can be analyzed according to different S values ​​to determine the fault branch.

[0068] 103. Collect fault voltage traveling wave data.

[0069] The accuracy of fault location is closely related to time. No matter which traveling wave location method is used, it is necessary to detect the time when the traveling wave head arrives at the detection position, so the calibration of the traveling wave head will affect the accuracy. In the embodiment of the present application, a neural network is used to extract the typical wave head arrival time sequence reflecting the fault distance information to achieve accurate location of distribution network faults.

[0070] In practical applications, the fault voltage traveling wave data can be collected, and then steps 104 and 105 are performed. The traveling wave head arrival time reflecting the fault distance information can be obtained through the trained neural network model, and then the fault location can be realized by using the above-established fault location determination rules. The above-mentioned fault voltage traveling wave data can be real data or simulated data.

[0071] 104. Based on the trained neural network model, the above fault voltage traveling wave data is processed to obtain the traveling wave head arrival time reflecting the fault distance information.

[0072] The input of the trained neural network model in the embodiment of the present application can be the fault voltage traveling wave waveform X(t), and the output can be the time T of the fault first traveling wave reaching the detection devices at both ends. Mx 、T My and the time T for the reflected wave to reach both ends of the line PxMx 、T PxMy And other parameters.

[0073] In an optional embodiment, the above-mentioned neural network model is a CNN-LSTM hybrid neural network model, wherein CNN is used to extract the frequency distribution characteristics of each wave head, and LSTM is used to extract the time correlation characteristics of typical wave heads.

[0074] Figure 3 A schematic diagram of the structure of a CNN-LSTM hybrid neural network model provided in the embodiment of the present application. Figure 3 As shown in the figure, the CNN-LSTM hybrid neural network model includes a CNN layer and a LSTM layer.

[0075] Convolutional Neural Network layer is a network layer used in deep learning to process data with a similar grid structure (such as images). It extracts the features of the input data by using convolution operations.

[0076] The Long Short-Term Memory layer is a special recurrent neural network (RNN) layer that can learn and memorize long-term dependent information. LSTM performs well in processing sequence data, such as time series analysis, natural language processing, speech recognition, etc., because it can effectively avoid the gradient vanishing and gradient exploding problems in traditional RNN.

[0077] Specifically, the CNN-LSTM hybrid neural network model built in the embodiment of the present application may include an input layer, a convolutional layer, a pooling layer, an LSTM layer, a fully connected layer and an output layer. The deep learning model has many hidden layers and is composed of multiple layers of nonlinear operations, which makes the model more complicated while enhancing the learning ability of the model, and the parameters during operation become more. When there is less training data and other factors affect it, the model will overfit. In order to reduce the interference of the overfitting phenomenon on the judgment process, the Dropout algorithm is used to reduce the overfitting phenomenon in data training, and then the Dropout layer is added. The CNN in the embodiment of the present application extracts the frequency distribution characteristics of each wave head, and the LSTM extracts the time correlation characteristics of the typical wave head.

[0078] 105. According to the arrival time of the traveling wave head reflecting the fault distance information, the fault location is located using the fault location determination rule.

[0079] As mentioned above, based on the above fault location algorithm in multi-branch lines, the numerical relationship between the distance from each end of the line to the fault point and the length of the branch line is used to establish a basis for determining the fault location under different fault locations. In practical applications, the fault branch can be determined based on the established determination basis, and at the same time, the above neural network can be used to analyze the fault information from the time-frequency domain perspective to improve the fault location accuracy.

[0080] The neural network model in the embodiments of the present application is introduced in detail below.

[0081] The training process of the hybrid neural network model may include:

[0082] Build a distribution network simulation model, set a traveling wave detection point at the distribution network bus, set multiple simulated fault points at all wave impedance discontinuity points and on any branch line, set parameters of multiple fault types at each simulated fault point for batch simulation; detect the traveling wave head through a traveling wave sensor to obtain simulation data;

[0083] The simulation data and the experimental data are integrated to construct a time domain traveling wave signal database;

[0084] The time-domain traveling wave signal database is divided into a training set and a test set, and the neural network model is iteratively trained using the training set data to obtain the trained neural network model.

[0085] Specifically, in the data collection stage, a distribution network simulation model is built in PSCAD / EMTDC, and traveling wave detection points are set at the distribution network busbar. Multiple simulated fault points are set at all wave impedance discontinuity points and on any branch line. Parameters of various fault types are set at each simulated fault point for batch simulation. The traveling wave head is detected by the traveling wave sensor to obtain the fault voltage traveling wave waveform. The simulation data and experimental data can be integrated to build a time domain traveling wave signal database, and the establishment of a multi-point fault holographic database can be realized.

[0086] In the training phase, the database is divided into a training set and a test set, a CNN-LSTM hybrid neural network model is built, and the training set data is used for iterative training. The fault voltage traveling wave waveform X(t) is input, the trained hybrid neural network model is located, and the training results are output to obtain the time T when the fault first traveling wave reaches the detection devices at both ends. Mx 、T My and the time T for the reflected wave to reach both ends of the line PxMx 、T PxMy And other parameters to accurately obtain the time.

[0087] The following fault location methods are currently proposed:

[0088] 1. In the complex distribution network topology, the traveling wave detection device is located at the end of the branch line. According to the arrival time of the fault traveling wave at the end of each line, the fault traveling wave arrival time matrix is ​​established. The difference characteristics of the time matrix before and after the traveling wave mutation are used to realize fault location. However, this method has certain limitations in the distribution network structure with multiple branches along the line.

[0089] 2. Using the numerical relationship between the length of multi-branch lines and the location of the fault point, a multi-branch transmission line fault location method based on double-terminal traveling waves is proposed, combining branch determination and fault location algorithms to solve the fault location. However, this method requires calculation and location based on the pre-set traveling wave velocity. When the line parameters change, the changing wave velocity will have a great impact on the traveling wave ranging accuracy.

[0090] 3. The traveling wave fault location method based on natural frequency extracts the relationship between the main natural frequency and multiple natural frequency values ​​and the fault distance, which can realize the transmission line fault location and effectively avoid the problem of difficult traveling wave calibration. However, in the numerous outgoing lines of each node in the complex distribution network, frequency aliasing will occur, the natural frequency cannot be correctly identified, and the fault location accuracy is not high.

[0091] The embodiment of the present application proposes a fault location method based on the inherent length of the line and the refraction and reflection of the traveling wave, ignoring the speed of the traveling wave, reducing the influence of the line parameters on the positioning accuracy, and making the positioning result more accurate. Based on the above-mentioned fault location algorithm in a multi-branch line, the numerical relationship between the distance from each end of the line to the fault point and the length of the branch line is used to establish a basis for determining the fault location under different fault locations, so as to achieve accurate positioning of the fault point of the multi-branch line. In addition, a CNN-LSTM hybrid neural network model is adopted, and the spatial features of the input data can be quickly extracted using the CNN layer, and the long-term and short-term dependencies in the time series data can be extracted using the LSTM. The fault distance information is introduced, and the frequency distribution of each wave head in the traveling wave is mined using the convolution layer, and the time correlation information is mined by the long and short-term memory layer, so as to achieve accurate positioning of the distribution network fault.

[0092] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a multi-branch line traveling wave positioning device based on fault ratio quantity.

[0093] Figure 4 The present invention provides a schematic diagram of a multi-branch line traveling wave positioning device based on a fault ratio value according to an embodiment of the present invention. Figure 4 As shown, the multi-branch line traveling wave positioning device 400 based on the fault ratio includes:

[0094] Establishing module 410, for establishing a fault location algorithm based on inherent line length and traveling wave refraction and reflection;

[0095] The establishment module 410 is further used to establish fault location determination rules under different fault locations based on the fault location algorithm in the multi-branch line by using the numerical relationship between the distance from each end to the fault point and the line branch length;

[0096] The acquisition module 420 is used to acquire fault voltage traveling wave data;

[0097] A prediction module 430 is used to process the fault voltage traveling wave data based on a trained neural network model to obtain a traveling wave head arrival time reflecting fault distance information;

[0098] The processing module 440 is used to locate the fault using the fault position determination rule according to the arrival time of the traveling wave head reflecting the fault distance information.

[0099] Understandably, this involves Figure 4 The relevant contents of each module in the above method embodiment have been described in detail, and the details can be referred to the contents in the method embodiment; that is, Figure 4 The multi-branch line traveling wave positioning device 400 provided based on fault ratio can be performed as follows: Figure 1Any steps in the illustrated embodiment will not be described in detail here. The multi-branch line traveling wave positioning device 400 based on fault ratio in the embodiment of the present application can analyze the inherent length of the line and the traveling wave refraction and reflection transmission path, and propose a fault positioning method based on refraction and reflection traveling waves, without the need to calculate the traveling wave transmission speed, thereby improving the influence of line parameters on the accuracy of traveling wave positioning; establish a numerical relationship between the distance from the multi-end of the multi-branch line to the fault point and the length of the line branch, locate the section according to the fault location criterion, and cooperate with the hybrid neural network to analyze the fault information from the time-frequency domain perspective, thereby improving the accuracy of fault positioning.

[0100] In one embodiment of the present application, an electronic device is also provided. Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502, wherein the memory 502 stores a computer program. When the computer program is executed by the processor 501, the following operations are performed: Figure 1 Any step in the method embodiment shown. The electronic device 500 may also include an input / output device, etc. In a specific implementation, the electronic device may be a terminal device, etc.

[0101] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor 501, the processor 501 executes any step in the above method embodiment.

[0102] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-branch line traveling wave positioning method based on fault ratio quantity, characterized in that: The method comprises: Establish a fault location algorithm based on the inherent length of the line and the reflection of the traveling wave; Based on the fault location algorithm in a multi-branch line, a fault location determination rule under different fault locations is established by utilizing the numerical relationship between the distance from each end to the fault point and the line branch length; Collect fault voltage traveling wave data; Based on the trained neural network model, the fault voltage traveling wave data is processed to obtain the traveling wave head arrival time reflecting the fault distance information; According to the arrival time of the traveling wave head reflecting the fault distance information, the fault location is realized by using the fault position determination rule.

2. The method for locating multi-branch line traveling waves based on fault ratio according to claim 1, characterized in that: The fault location algorithm based on the inherent length of the line and the traveling wave refraction and reflection includes a single-end fault point distance measurement formula and a double-end fault point distance measurement formula, wherein the single-end fault point distance measurement formula includes: The double-terminal fault point distance measurement formula includes: Where, d represents the distance from the fault point to the measuring end, and L represents the total length of the line; Δt=t M -t N , Δt fM =t fM -t M , Δt fN =t fN -t N , t M and t N is the time it takes for the preceding traveling wave to reach the traveling wave detection devices at both ends, t fM and t fN It is the time it takes for the reflected traveling wave from the fault point to reach the traveling wave detection devices at both ends.

3. The method for locating multi-branch line traveling waves based on fault ratio according to claim 2, characterized in that: The fault location algorithm in the multi-branch line is based on the numerical relationship between the distance from each end to the fault point and the line branch length to establish a fault location determination rule under different fault locations, including: Based on the fault location algorithm, in a multi-branch line, the distance between the fault point and the endpoint is extended to any two end points to obtain a fault point distance measurement formula for the multi-branch line; Based on the fault point distance measurement formula of the multi-branch line, a fault ratio relationship is defined, and the fault position determination rules under the different fault positions are determined.

4. The method for locating multi-branch line traveling waves based on fault ratio according to claim 3, characterized in that: The fault point distance measurement formula of the multi-branch line includes: in, The fault first traveling wave reaches line M x M y The time difference between the two ends, The fault reflected wave reaches line M respectively. x M y Time at both ends.

5. The multi-branch line traveling wave positioning method based on fault ratio according to claim 3 or 4, characterized in that: The fault ratio relationship is: Among them, M x M y Represents the line between any two endpoints of a multi-branch line, d MxMy Indicates line M x M y The distance from the fault point to the end point, L MxPx Represents endpoint M x To the neighboring node P x distance; The fault location determination rules under different fault locations include:

6. The method for locating multi-branch line traveling waves based on fault ratio according to claim 1, characterized in that: The neural network model is a CNN-LSTM hybrid neural network model, in which CNN is used to extract the frequency distribution characteristics of each wave head, and LSTM is used to extract the time correlation characteristics of typical wave heads.

7. The method for locating multi-branch line traveling waves based on fault ratio according to claim 6, characterized in that: The training process of the hybrid neural network model includes: Build a distribution network simulation model, set a traveling wave detection point at the distribution network bus, set multiple simulated fault points at all wave impedance discontinuity points and on any branch line, set parameters of multiple fault types at each simulated fault point for batch simulation; detect the traveling wave head through a traveling wave sensor to obtain simulation data; The simulation data and the experimental data are integrated to construct a time domain traveling wave signal database; The time-domain traveling wave signal database is divided into a training set and a test set, and the neural network model is iteratively trained using the training set data to obtain the trained neural network model.

8. A multi-branch line traveling wave positioning device based on fault ratio quantity, characterized in that: include: Establishing a module for establishing a fault location algorithm based on the inherent length of the line and the reflection of the traveling wave; The establishment module is further used to establish fault location determination rules under different fault locations in a multi-branch line based on the fault location algorithm and using the numerical relationship between the distance from each end to the fault point and the line branch length; An acquisition module, used for acquiring fault voltage traveling wave data; A prediction module, used to process the fault voltage traveling wave data based on a trained neural network model to obtain a traveling wave head arrival time reflecting fault distance information; The processing module is used to realize fault location according to the arrival time of the traveling wave head reflecting the fault distance information and using the fault position determination rule.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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