Method and system for high-resistance fault diagnosis of power distribution network

Abstract

The embodiment of the invention relates to the technical field of power grid operation and maintenance, and provides a power distribution network high-resistance fault diagnosis method and system, and the method comprises the steps: collecting the voltage and current of a feeder line and a branch node in a power distribution network, converting the voltage and current into a three-phase and zero-sequence V-I curve image through an artificial intelligence algorithm model, taking the three-phase and zero-sequence V-I curve image as double features, and carrying out the iterative calculation. According to the method, fault occurrence, fault types and fault reasons are distinguished, a fault phase V-I curve image is converted into a fault phase V-I curve image through an artificial intelligence algorithm model, then the fault phase V-I curve image is vertical into a vertical V-I curve image, iterative calculation is carried out as double features, a fault section and a fault distance are positioned, and a corresponding fault processing scheme is generated according to a calculation result. According to the method and the system provided by the embodiment of the invention, the high-resistance fault can be effectively identified, and accurate fault position information and stability and reliability in actual operation are provided.

Description

Technical Field

[0001] The present disclosure relates to the field of power grid operation and maintenance technology, and in particular to a method and system for diagnosing high-resistance faults in distribution networks based on a deep learning algorithm model and capable of identifying the causes of high-resistance faults. Background Art

[0002] The distribution network is a branch-based system that delivers electricity to the final consumption unit, which is spread across rural and urban areas. As the final end of the power system, the distribution network is directly connected to the user. Therefore, rapid fault diagnosis of the distribution network and minimizing the repair time are the prerequisites for providing users with high-quality power supply, which is related to the production and life of thousands of households.

[0003] High-resistance faults in distribution networks are a common type of fault in distribution networks. They refer to faults in which the line potential to the ground is increased due to insulation aging, animal intrusion, sand and vegetation intrusion, natural disasters, etc. Since high-resistance faults usually have weak characteristics and small fault currents, they are not easy to cause the line protection device to trip, so they are not easy to be detected and prevented. However, when a high-resistance fault occurs, an arc will be generated, which is very dangerous and can easily cause electrical fires and accidents such as high-voltage electric shock.

[0004] Existing power grid fault diagnosis and identification methods based on algorithm models, such as extracting features from current waveforms and then using threshold setting to determine whether a high-resistance fault has occurred, mainly build models to determine whether a high-resistance fault has occurred. There is no technology to determine the cause of a high-resistance fault, and it is impossible to effectively alert distribution network operation and maintenance personnel of the on-site situation, which is not conducive to maintenance personnel arranging emergency repairs.

[0005] The existing fault location method for distribution network mainly uses steady-state method to locate fault section and fault distance, which has low accuracy and poor anti-interference ability. In addition, the existing fault distance measurement method relying on artificial intelligence method adopts ANN model, which has few layers and shallow network, has not been tested by large data sets, has not taken advantage of deep learning, and cannot accurately locate the fault location, which is not conducive to the operation and maintenance personnel to repair the fault. Summary of the invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method and system for diagnosing high-resistance faults in distribution networks, which can effectively reflect fault characteristics, establish a mapping relationship with the fault characteristics, and fully develop and utilize artificial intelligence methods in fault distance research to solve the problems existing in the prior art.

[0007] In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:

[0008] The present disclosure provides a method for diagnosing a high-resistance fault in a power distribution network, comprising the following steps:

[0009] Collect raw characteristic data in the distribution network.

[0010] The original feature data is processed into first feature data and second feature data, and the first feature data and second feature data are input into a first model to be converted into a first image and a second image. The first model is used to calculate the first image and the second image to identify the occurrence of a fault, the type of fault and the cause of the fault.

[0011] The original feature data is converted into a third image, and then the third image is vertically converted into a fourth image. The third image and the fourth image are input into the second model for calculation to locate the fault section. The third image and the fourth image are input into the third model for calculation to locate the fault distance.

[0012] The nature of the fault is determined according to the fault type and the fault cause, the precise location of the fault is determined according to the fault section and the fault distance, and a corresponding fault handling plan is generated according to the nature of the fault and the precise location of the fault.

[0013] In some embodiments, generating a corresponding fault handling solution according to the fault property and the precise fault location includes:

[0014] Generate a corresponding reclosing instruction according to the nature of the fault and the precise location of the fault to block and repair the fault point;

[0015] After the distribution network successfully recloses according to the reclosing instruction, corresponding notification information is sent to the operation and maintenance personnel to notify the operation and maintenance personnel to check.

[0016] In some embodiments, the fault causes include permanent faults and transient faults.

[0017] Generate a corresponding reclosing instruction according to the fault nature and the precise fault location to block and repair the fault point, including:

[0018] If the cause of the fault is a permanent fault, the relevant circuit breaker in the protection device at the precise location of the fault is controlled to accelerate the tripping through the reclosing command;

[0019] If the fault cause is a transient fault, the circuit breaker is controlled to reclose after tripping through the reclosing instruction.

[0020] In some embodiments, the original characteristic data includes: voltage recording data of the feeder, current recording data of the feeder, voltage recording data of the branch node, and current recording data of the branch node after denoising and preprocessing;

[0021] And / or, the fault types include a single-phase short circuit grounding fault, a single-phase line break grounding fault, a two-phase short circuit grounding fault, a two-phase phase-to-phase fault and a three-phase short circuit fault.

[0022] In some embodiments, the first characteristic data is obtained by three-phase transformation of the original characteristic data, and the first image is a three-phase VI curve image; the second characteristic data is obtained by zero-sequence transformation of the original characteristic data, and the second image is a zero-sequence VI curve image.

[0023] The first image and the second image are calculated by the first model to identify the occurrence of a fault, the type of fault and the cause of the fault, including: normalizing the three-phase VI curve image and the zero-sequence VI curve image to obtain a first dual feature.

[0024] Iterative calculation is performed based on the first dual features to extract features for identifying whether a fault has occurred, determining the type of fault and the cause of the fault.

[0025] In some embodiments, the third image is a fault phase VI curve image obtained from original feature data, and the fourth image is a verticalized VI curve generated by performing a vertical operation on the fault phase VI curve image.

[0026] Inputting the third image and the fourth image into the second model for calculation to locate the fault section includes:

[0027] Normalizing the fault phase VI curve image and the verticalized VI curve to obtain a second dual feature;

[0028] Performing iterative calculation according to the second dual features to extract features for identifying the fault section;

[0029] Inputting the third image and the fourth image into a third model for calculation to locate the fault distance includes:

[0030] Normalizing the fault phase VI curve image and the verticalized VI curve to obtain a third dual feature;

[0031] Iterative calculation is performed based on the third dual feature to extract features for identifying the fault distance.

[0032] In some embodiments, the construction of the first model includes: obtaining a three-phase and zero-sequence VI curve fault sample set at the feeder end; inputting the three-phase and zero-sequence VI curve fault sample set at the feeder end into an initial first model for training to obtain the first model.

[0033] In some embodiments, the construction of the second model includes: obtaining a fault sample set of the VI curve of the fault phase at the feeder end; inputting the fault sample set of the VI curve of the fault phase at the feeder end into an initial second model for training to obtain the second model.

[0034] The construction of the third model includes: a branch node fault phase VI curve fault sample set; inputting the branch node fault phase VI curve fault sample set into an initial third model for training to obtain the third model.

[0035] In some embodiments, the method further comprises:

[0036] The original data and the calculation results of the first model, the second model and the third model are stored; and the original data and the calculation results of the first model, the second model and the third model are added to the sample set used to train the first model, the second model and the third model.

[0037] Another aspect of an embodiment of the present disclosure provides a system for diagnosing high-resistance faults in a power distribution network, comprising:

[0038] Data acquisition module: used to collect original characteristic data in the distribution network.

[0039] Fault identification module: used to process the original feature data into first feature data and second feature data, input the first model into the first image and the second image for calculation, and identify the occurrence of the fault, the type of fault and the cause of the fault.

[0040] Fault location module: used for converting the original feature data into a third image, then verticalizing the third image into a fourth image, inputting the third image and the fourth image into the second model for calculation to locate the fault section, and then inputting the third image and the fourth image into the third model for calculation to locate the fault distance.

[0041] Fault repair module: used to judge the nature of the fault according to the fault type and the fault cause, determine the precise location of the fault according to the fault section and the fault distance, send a reclosing instruction, and notify the operation and maintenance personnel to check after the reclosing is successful.

[0042] Data storage module: stores the original data and the calculation results of the first model, the second model, and the third model, and adds a sample set for training the first model, the second model, and the third model.

[0043] The system for diagnosing high-resistance faults in a distribution network can implement the method for diagnosing high-resistance faults in a distribution network according to any one of claims 2 to 8.

[0044] The disclosed embodiments provide a method and system for diagnosing high-resistance faults in distribution networks, which can realize fault occurrence judgment, fault cause identification, fault type identification, fault section location, fault distance measurement and fault recovery, and can help power system operation and maintenance personnel to more comprehensively understand and handle fault conditions, and improve the efficiency and accuracy of fault handling. The system is integrated with a fault identification model, a fault section identification model and a fault distance measurement model. The VI curves obtained by different processing methods can fully and truly reflect the cause, type, section and distance of the fault. Compared with the prior art, the method and system have more systematic functions.

[0045] For high-resistance faults, the model can effectively identify the cause, type, section and distance of the fault, avoiding the failure of protection to trip due to weak high-resistance fault characteristics, thereby causing disasters such as electrical fires. At the same time, the model can help to timely troubleshoot high-resistance fault points and provide accurate fault information, thereby avoiding potential catastrophic consequences. For non-high-resistance faults, the model has a more accurate identification effect. The model system can effectively deal with interference factors such as load imbalance, line parameter errors and transformer errors, and can ensure the stability and reliability of the model system in actual operation, and improve the accuracy of fault judgment and identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present disclosure 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 recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A schematic diagram of the structure of a distribution network fault diagnosis system of a disclosed embodiment;

[0048] Figure 2 A schematic diagram of the steps of a distribution network fault diagnosis method according to a disclosed embodiment;

[0049] Figure 3 It is a structural schematic diagram of the first model in the distribution network fault diagnosis method of the disclosed embodiment;

[0050] Figure 4 A schematic diagram of the structure of the second model in the distribution network fault diagnosis method of the disclosed embodiment;

[0051] Figure 5 It is a structural schematic diagram of the third model in the distribution network fault diagnosis method of the disclosed embodiment;

[0052] Figure 6 A verticalized VI curve diagram of the disclosed embodiment;

[0053] Figure 7 A single-outlet multi-branch distribution network model of the disclosed embodiment;

[0054] Figure 8 A multi-outlet multi-branch distribution network model of a disclosed embodiment. DETAILED DESCRIPTION

[0055] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0056] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0057] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0058] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0059] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0060] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0061] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0062] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0063] The first embodiment of the present disclosure provides a distribution network fault diagnosis method, which is applied to Figure 1 The distribution network fault diagnosis system shown in the figure combines Figure 2 From the perspective of power supply and distribution network fault diagnosis method, the following steps are included:

[0064] S1: Data acquisition module 1 collects original characteristic data in the distribution network.

[0065] In this step, the original characteristic data in the distribution network collected by the data acquisition module 1 are the voltage and current recording data of the feeder and the voltage and current recording data of the branch node, that is, including: the voltage recording data of the feeder, the current recording data of the feeder, the voltage recording data of the branch node, and the current recording data of the branch node. The above data all need to be denoised and preprocessed, and the subsequent steps will use the original characteristic data for calculation.

[0066] Preferably, in this step, the voltage recording data of the feeder, the current recording data of the feeder, the voltage recording data of the branch node, and the current recording data of the branch node are extracted in real time with a data window of two cycles in length, and are denoised and preprocessed. More preferably, two cycle signals after the fault moment are extracted, the signals are processed using a low-pass filter with a high-frequency cutoff frequency of 150 Hz, and the secondary signals are converted into primary signals through a transformer ratio.

[0067] S2: The fault identification module 2 processes the original feature data into first feature data and second feature data, and inputs the first feature data and the second feature data into the first model to convert them into first images and second images, and calculates the first image and the second image through the first model to identify the occurrence of the fault, the type of fault and the cause of the fault;

[0068] Specifically, in this step, the first feature data is obtained by three-phase transformation of the original feature data, and the first image is a three-phase VI curve image, that is, the original feature data is transformed into the form of three-phase current and three-phase voltage, and then a curve image is generated with three-phase voltage and three-phase current as coordinate points on the vertical coordinate axis.

[0069] The second characteristic data is obtained by zero-sequence transformation of the original characteristic data, and the second image is a zero-sequence VI curve image, that is, the original characteristic data is transformed into the form of zero-sequence current and zero-sequence voltage, and then a curve image is generated with zero-sequence voltage and zero-sequence current as coordinate points on the vertical coordinate axis.

[0070] The first model is used to identify whether a fault has occurred, and if so, determine the fault type and cause.

[0071] Specifically, Figure 3 As shown, the first model includes an input layer, an intermediate layer and an output layer.

[0072] The input layer converts the feeder end and node voltage and current data into three-phase VI curve images and zero-sequence VI curve images and normalizes them, and inputs the normalized three-phase VI curve images and zero-sequence VI curve images as dual image features into the middle layer.

[0073] Preferably, the three-phase VI curve image is normalized by the following method: the x range of the coordinates is limited to [-X, X] and the y range is [-Y, Y], and each phase curve is distinguished by different colors. Preferably, X = 400A, Y = 10000V. The zero-sequence VI curve image is normalized by the following method: the maximum absolute value of the voltage and current is used as the reference value to obtain the normalized curve, the range of x and y are both [-1, 1], the RGB value of the color of the curve is [r, r, r], the value of r is measured by the zero-sequence current, and the zero-sequence current range [-Imax, Imax] corresponds to the RGB value [0, 0, 0] to [255, 255, 255], preferably, Imax = 100A.

[0074] The middle layer is composed of a convolutional layer and a channel attention mechanism. Specifically, two channels are composed of two residual structure blocks, which extract two image features respectively, convert them into feature atlases, and merge them into a comprehensive feature map set along the channel direction. The features of the comprehensive feature map set are then extracted by three residual block structures.

[0075] The output layer consists of an average pooling layer, a fully connected layer, a Softmax layer, and a classification layer. It can determine whether a fault has occurred based on the calculation results of the intermediate layer, and when a fault is determined to have occurred, it can determine the fault type and cause.

[0076] Specifically, the fault types include: single-phase short circuit grounding fault, single-phase line break grounding fault, two-phase short circuit grounding fault, two-phase phase-to-phase fault, and three-phase short circuit fault.

[0077] The fault causes include wet sand and gravel fault, dry sand and gravel fault, tree-touching fault, animal-related fault, lightning fault and mountain fire fault. Among them, wet sand and gravel fault, dry sand and gravel fault, tree-touching fault, animal-related fault and volcano fault are permanent faults, and lightning fault is regarded as transient fault. It is worth noting that in the practical meaning, wet sand and gravel fault, dry sand and gravel fault, tree-touching fault and animal-related fault belong to high-resistance grounding fault type.

[0078] S3: The fault location module 3 converts the original feature data into a third image, and then verticalizes the third image into a fourth image, and inputs the third image and the fourth image into the second model for calculation to locate the fault section; and inputs the third image and the fourth image into the third model for calculation to locate the fault distance.

[0079] Specifically, in this step, the original feature data is converted into a third image, and then the third image is vertically converted into a fourth image. The third image refers to a fault phase VI curve image generated by taking the voltage and current of the original feature data as coordinate points, and the fourth image refers to a verticalized VI curve generated by verticalizing the fault phase VI curve image.

[0080] In a specific embodiment, see Figure 6 The fault phase VI curve is generated by fitting the fault phase VI curve with an ellipse, and the verticalization method is to rotate the fault phase VI curve counterclockwise by 90°-θ angle, and divide the curve horizontally into four parts with the same height and width, and merge them from left to right and from top to bottom in the form of a 2×2 matrix into a verticalized VI curve image.

[0081] The advantage of verticalizing the curve is that it can better highlight the changing characteristics of the curve in the coordinate graph and avoid masking the changing characteristics of the curve due to the reduction of the curve through the image dimension due to slight changes in the characteristics.

[0082] The second model is used for fault section identification, that is, to determine the location of the fault section.

[0083] Furthermore, when it is judged that the fault occurs in a certain section on the outgoing line, the fault phase VI curve and the verticalized VI curve at the upstream branch point of the section are taken as dual features, and the fault distance is input into the fault distance measurement model; when it is judged that the fault occurs at the branch point on the outgoing line, the fault phase VI curve and the verticalized VI curve at the branch point are taken as dual features, and the fault distance is input into the fault distance measurement model.

[0084] For further information, see Figure 4 ,The second model includes: input layer, intermediate layer and output layer.

[0085] The input layer converts the fault phase voltage and current data taken at the feeder end into the fault phase VI curve image and normalizes it. The normalization method can refer to the normalization method of the three-phase VI curve image and the zero-sequence VI curve image in step 2, which will not be repeated here.

[0086] The middle layer is composed of convolutional layers, preferably, 5 residual structure blocks, to extract image features.

[0087] The output layer consists of an average pooling layer, a fully connected layer, a Softmax layer, and a classification layer to determine the fault section or branch point.

[0088] The third model is used to determine the fault distance.

[0089] In a specific embodiment, see Figure 5 ,The third model includes: input layer, intermediate layer and output layer.

[0090] The input layer converts the branch point voltage and current data taken into the fault phase VI curve image and normalizes it as the first feature. The normalized fault phase VI curve is vertically divided into four parts along the y-axis direction and spliced ​​into a verticalized VI curve image as the second feature. The normalization method can refer to the normalization method of the three-phase VI curve image and the zero-sequence VI curve image in step 2, which will not be repeated here.

[0091] The middle layer is composed of a convolutional layer and a channel attention mechanism. Preferably, two paths are formed by 8 residual structure blocks respectively, two image features are extracted respectively, converted into feature atlases, and merged into a comprehensive feature map set in the channel direction.

[0092] The output layer consists of an average pooling layer, a fully connected layer, and a regression layer, and outputs the distance between fault branch points.

[0093] The fault section is located by the second model, and the distance of the fault branch point is located by the third model, so that the fault position can be accurately located.

[0094] S4: The fault recovery module 4 determines the nature of the fault according to the fault type and the fault cause, determines the precise location of the fault according to the fault section and the fault distance, and generates a corresponding fault handling solution according to the nature of the fault and the precise location of the fault.

[0095] Specifically, the fault recovery module determines the nature of the fault according to the fault type and fault cause output by the fault identification module 2 in step S2, and outputs a fault reclosing instruction according to the fault nature and fault section. The corresponding protection device performs the protection action according to the reclosing instruction, and visualizes the fault type, cause, nature, section and distance for the operation and maintenance personnel to check. In this way, high-resistance faults in the distribution network can be quickly blocked to prevent arcs from causing fires or accidents that cause electric shocks to people.

[0096] Specifically, generating a corresponding fault handling solution according to the fault property and the precise fault location also includes:

[0097] For the fault section, a reclosing decision instruction is given. After the line circuit breaker trips the corresponding phase, the upstream circuit breaker of the fault section is accelerated to reclose. After the reclosing time T1, preferably 0.8s, the reclosed fault section is close to the power supply side. If it is identified as a permanent fault, the three-phase circuit breaker is accelerated to trip. If it is identified as a transient fault, the circuit breaker recloses successfully.

[0098] For branch lines, a reclosing decision instruction is given; after the line circuit breaker trips the corresponding phase, the non-fault line section and non-fault branch line circuit breaker are accelerated to reclose. After the reclosing time T1, preferably 0.8s, the branch line circuit breaker is reclosed. If it is identified as a permanent fault, the three-phase circuit breaker is accelerated to trip; if it is identified as a transient fault, the circuit breaker is reclosed successfully.

[0099] Preferably, the data storage module 5 uses the data collected by the data acquisition module and the results calculated by the fault identification module and the fault location module as historical samples to expand the data set, and the data set is used to train the first model, the second model, and the third model.

[0100] Specifically, the data storage module 5 stores the information and reclosing instructions extracted by the data acquisition module after fault recovery as historical reclosing samples to expand the sample set. When a certain size of sample set is accumulated, the parameters of the fault identification module 2 and the fault location module 3 are updated.

[0101] A second embodiment of the present disclosure provides an initial construction method of a first model, a second model, and a third model, including:

[0102] Three sample sets of feeder and branch node faults are obtained. Specifically, the sample sets include: a feeder end three-phase and zero-sequence VI curve fault sample set for the first training and test, a feeder end fault phase VI curve fault sample set for the second model training and test, and a branch node fault phase VI curve fault sample set for the third training and test.

[0103] The three fault sample sets obtained are respectively input into the first model, the second model, and the third model for training until convergence, so as to obtain the trained first model, the second model, and the third model.

[0104] Among them, the three fault sample sets are generated by building a simulated distribution network model, such as Figure 7 , 8 As shown, the generation steps include:

[0105] Fault sample data used as samples are obtained. The fault causes include wet gravel high-resistance grounding fault, dry gravel high-resistance grounding fault, tree-touching high-resistance grounding fault, animal-related high-resistance grounding fault, forest fire fault and lightning strike fault. The fault distance is randomly set on the line section or branch line.

[0106] Based on the acquired fault sample data, the corresponding three-phase VI curve, zero-sequence VI curve, fault phase VI curve, and vertical VI curve are constructed and normalized.

[0107] The three-phase VI curves and the zero-sequence VI curve are used to form a first sample set for the first model.

[0108] The fault phase VI curve and the verticalized VI curve of the feeder are used to form a second sample set for the second model.

[0109] The fault phase VI curve and the verticalized VI curve of the branch node are used to form a third sample set for the third model.

[0110] In summary, the embodiments of the present disclosure provide a method and system for diagnosing high-resistance faults in distribution networks, which can realize fault occurrence judgment, fault cause identification, fault type identification, fault section location, fault distance measurement and fault recovery, and can help power system operation and maintenance personnel to more comprehensively understand and handle fault conditions, and improve the efficiency and accuracy of fault handling. The system is integrated with a fault identification model, a fault section identification model and a fault distance measurement model. The VI curves obtained by different processing methods can fully and truly reflect the cause, type, section and distance of the fault. Compared with the prior art, the method and system have more systematic functions.

[0111] For high-resistance faults, the model can effectively identify the cause, type, section and distance of the fault, avoiding the failure of protection to trip due to weak high-resistance fault characteristics, thereby causing disasters such as electrical fires. At the same time, the model can help to timely troubleshoot high-resistance fault points and provide accurate fault information, thereby avoiding potential catastrophic consequences. For non-high-resistance faults, the model has a more accurate identification effect. The model system can effectively deal with interference factors such as load imbalance, line parameter errors and transformer errors, and can ensure the stability and reliability of the model system in actual operation, and improve the accuracy of fault judgment and identification.

[0112] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0113] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0114] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0115] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0116] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0117] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0119] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0120] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0122] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0123] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0124] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A method for diagnosing high-resistance faults in a distribution network, characterized in that: The following steps are involved: Collect original characteristic data in the distribution network; Processing the original feature data into first feature data and second feature data, and inputting the first feature data and the second feature data into a first model to convert them into a first image and a second image, and calculating the first image and the second image through the first model to identify the occurrence of a fault, the type of fault, and the cause of the fault; Converting the original feature data into a third image, verticalizing the third image into a fourth image, inputting the third image and the fourth image into a second model for calculation to locate the fault section, and inputting the third image and the fourth image into a third model for calculation to locate the fault distance; The nature of the fault is determined according to the fault type and the fault cause, the precise location of the fault is determined according to the fault section and the fault distance, and a corresponding fault handling plan is generated according to the nature of the fault and the precise location of the fault.

2. The method for diagnosing high-resistance faults in a distribution network according to claim 1, characterized in that: Generate a corresponding fault handling solution according to the fault nature and the precise fault location, including: Generate a corresponding reclosing instruction according to the nature of the fault and the precise location of the fault to block and repair the fault point; After the distribution network successfully recloses according to the reclosing instruction, corresponding notification information is sent to the operation and maintenance personnel to notify the operation and maintenance personnel to check.

3. The method for diagnosing high-resistance faults in a distribution network according to claim 2, characterized in that: The fault causes include permanent faults and transient faults. Generate a corresponding reclosing instruction according to the fault nature and the precise fault location to block and repair the fault point, including: If the cause of the fault is a permanent fault, the relevant circuit breaker in the protection device at the precise location of the fault is controlled to accelerate the tripping through the reclosing command; If the fault cause is a transient fault, the circuit breaker is controlled to reclose after tripping through the reclosing instruction.

4. The method for diagnosing high-resistance faults in a distribution network according to claim 1, characterized in that: The original characteristic data includes: the voltage recording data of the feeder, the current recording data of the feeder, the voltage recording data of the branch node, and the current recording data of the branch node after denoising and preprocessing; and / or The fault types include a single-phase short circuit grounding fault, a single-phase disconnection grounding fault, a two-phase short circuit grounding fault, a two-phase interphase fault and a three-phase short circuit fault.

5. The method for diagnosing high-resistance faults in a distribution network according to claim 1, characterized in that: The first characteristic data is obtained by three-phase transformation of the original characteristic data, and the first image is a three-phase VI curve image. The second characteristic data is obtained by zero-sequence transformation of the original characteristic data, and the second image is a zero-sequence VI curve image. The first model is used to calculate the first image and the second image to identify the occurrence of a fault, the type of fault and the cause of the fault, including: normalizing the three-phase VI curve image and the zero-sequence VI curve image to obtain a first dual feature; Iterative calculation is performed based on the first dual features to extract features for identifying whether a fault has occurred, determining the type of fault and the cause of the fault.

6. The method for diagnosing high-resistance faults in a distribution network according to claim 1, characterized in that: The third image is a fault phase VI curve image obtained from the original characteristic data, and the fourth image is a verticalized VI curve generated by performing a vertical operation on the fault phase VI curve image. Inputting the third image and the fourth image into the second model for calculation to locate the fault section includes: Normalizing the fault phase VI curve image and the verticalized VI curve to obtain a second dual feature; Performing iterative calculation according to the second dual features to extract features for identifying the fault section; Inputting the third image and the fourth image into a third model for calculation to locate the fault distance includes: Normalizing the fault phase VI curve image and the verticalized VI curve to obtain a third dual feature; Iterative calculation is performed based on the third dual feature to extract features for identifying the fault distance.

7. The method for diagnosing high-resistance faults in a distribution network according to claim 5, characterized in that: The construction of the first model includes: Obtain a sample set of three-phase and zero-sequence VI curve faults at the feeder end; The feeder end three-phase and zero-sequence VI curve fault sample set is input into the initial first model for training to obtain the first model.

8. The method for diagnosing high-resistance faults in a distribution network according to claim 6, characterized in that: The construction of the second model includes: Obtain a fault sample set of VI curve of the fault phase at the feeder end; Inputting the fault sample set of the VI curve of the fault phase at the feeder end into the initial second model for training to obtain the second model; The construction of the third model includes: Fault sample set of VI curve of fault phase of branch node; The fault sample set of the branch node fault phase VI curve is input into the initial third model for training to obtain the third model.

9. The method for diagnosing high-resistance faults in a distribution network according to claim 1, characterized in that: The method further comprises: storing the original data and calculation results of the first model, the second model, and the third model; The original data and the calculation results of the first model, the second model and the third model are added to a sample set used to train the first model, the second model and the third model.

10. A system for diagnosing high-resistance faults in a distribution network, characterized in that: include: Data acquisition module: used to collect original characteristic data in the distribution network. Fault identification module: used to process the original feature data into first feature data and second feature data, input the first model into the first image and the second image for calculation, identify the occurrence of the fault, the type of fault and the cause of the fault, Fault location module: used to convert the original feature data into a third image, then verticalize the third image into a fourth image, input the third image and the fourth image into the second model for calculation, locate the fault section, then input the third image and the fourth image into the third model for calculation, locate the fault distance, Fault repair module: used to determine the nature of the fault according to the fault type and the fault cause, determine the precise location of the fault according to the fault section and the fault distance, send a reclosing instruction, and notify the operation and maintenance personnel to check after the reclosing is successful. Data storage module: stores the original data and the calculation results of the first model, the second model, and the third model and adds the sample set used to train the first model, the second model, and the third model. The system for diagnosing high-resistance faults in a distribution network can implement the method for diagnosing high-resistance faults in a distribution network according to any one of claims 2 to 8.

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