Distribution network fault identification and location methods, devices, equipment, media and products

Abstract

The present application relates to a distribution network fault identification and location method, apparatus, equipment, medium, and product. In one aspect, the distribution network fault identification and location method provided herein comprises obtaining a zero-sequence electrical sequence and an admittance parameter matrix corresponding to each acquisition node in a distribution network line; performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence; and determining the target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets preset identification conditions. The distribution network fault identification and location method in this application can reduce computing resource usage and improve the efficiency of the distribution network fault identification and location method.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, equipment, medium and product for identifying and locating distribution network faults. Background Art

[0002] In power systems, distribution network lines are complex and prone to high-resistance ground faults due to external factors such as tree obstructions, damage from external forces, and lightning strikes. Due to the large ground impedance, the ground fault current is weak and changes rapidly. Therefore, distribution network fault identification and location are necessary to promptly detect faults in distribution network lines.

[0003] Traditional distribution network fault identification and location methods rely on machine learning models to extract frequency domain or time domain signal features, such as wavelet energy spectrum and multiple harmonic features, from the collected electrical signals. They then use trained neural networks, support vector machines, and other classifiers to identify the fault type and determine the fault location.

[0004] However, the traditional distribution network fault identification and location methods have low identification and location efficiency. Summary of the Invention

[0005] Based on this, it is necessary to provide a distribution network fault identification and positioning method, device, equipment, medium and product that can improve the efficiency of identification and positioning to address the above technical problems.

[0006] In a first aspect, the present application provides a method for identifying and locating a distribution network fault, the method comprising:

[0007] Obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line;

[0008] Performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence;

[0009] When the morphological feature data meets the preset identification conditions, the target fault location corresponding to the distribution network line is determined based on the admittance parameter matrix.

[0010] In one embodiment, performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence includes:

[0011] For each acquisition node, perform morphological analysis on the zero-sequence electrical data corresponding to the acquisition node in the zero-sequence electrical sequence to obtain highlighted electrical data and dimmed electrical data.

[0012] Determine the electrical morphological data corresponding to the acquisition node based on the ratio coefficient of the highlighted electrical data and the dimmed electrical data;

[0013] According to the electrical morphological data corresponding to each acquisition node, morphological characteristic data is obtained.

[0014] In one embodiment, determining electrical morphology data corresponding to a collection node based on a ratio coefficient of highlighted electrical data to dimmed electrical data includes:

[0015] For each collection node, the collection node is taken as the target collection node, and the previous collection node of the collection node is taken as the adjacent collection node;

[0016] Perform difference processing on the highlighted electrical data corresponding to the target acquisition node and the adjacent acquisition nodes to obtain the highlighted electrical difference;

[0017] Perform difference processing on the gray electrical data corresponding to the target acquisition node and the adjacent acquisition nodes to obtain the gray electrical difference;

[0018] The electrical morphology data corresponding to the target acquisition node is obtained by performing ratio processing on the highlighted electrical difference and the dark electrical difference.

[0019] In one embodiment, the electrical morphology data includes voltage morphology data and current morphology data; and morphology feature data is obtained based on the electrical morphology data corresponding to each acquisition node, including:

[0020] When the voltage shape data is greater than or equal to a preset voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as a salient voltage node;

[0021] When the voltage shape data is less than the voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as the concave point voltage node;

[0022] When the current shape data is greater than or equal to a preset current fluctuation rate threshold, the acquisition node corresponding to the current shape data is used as a salient current node;

[0023] When the current shape data is less than the current fluctuation rate threshold, the acquisition node corresponding to the current shape data is used as the concave current node;

[0024] The morphological feature data is determined according to the number of convex voltage nodes, concave voltage nodes, convex current nodes, and concave current nodes.

[0025] In one embodiment, when the morphological feature data meets the preset identification conditions, determining the target fault location corresponding to the distribution network line based on the admittance parameter matrix includes:

[0026] According to the admittance parameter matrix, the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the distribution network line are obtained;

[0027] Determine the first fault node according to the degree of deviation between the voltage difference of the forward adjacent nodes and the voltage difference of the backward adjacent nodes;

[0028] The target fault location is determined based on the branch situation corresponding to the distribution network line and the first fault node.

[0029] In one embodiment, determining a target fault location based on a branch condition corresponding to a distribution network line and a first fault node includes:

[0030] In the case where there are branch lines in the distribution network, the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node are obtained according to the zero-sequence voltage and admittance parameters corresponding to the first fault node;

[0031] Determining a second fault node based on a degree of deviation between a voltage difference between a forward adjacent node and a voltage difference between a backward adjacent node corresponding to a fault node;

[0032] A target fault location is determined based on the first fault node and the second fault node.

[0033] In a second aspect, the present application further provides a distribution network fault identification and positioning device, the device comprising:

[0034] The data acquisition module is used to obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line;

[0035] A feature extraction module is used to extract the morphological features of the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence;

[0036] The fault location module is used to determine the target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets the preset recognition conditions.

[0037] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of the first aspect when executing the computer program.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method of the first aspect when the computer program is executed by a processor.

[0039] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the method of the first aspect when executed by a processor.

[0040] The distribution network fault identification and location methods, devices, equipment, media, and products described above include, on one hand, a distribution network fault identification and location method, which obtains the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line; performs morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence; and, when the morphological feature data meets preset identification conditions, determines the target fault location corresponding to the distribution network line based on the admittance parameter matrix. In this way, by extracting morphological feature data from the zero-sequence electrical sequence, the fault can be quickly and effectively identified and located, and the target fault location can be calculated using the admittance parameter matrix. The data acquisition complexity is low, and data can be acquired based on existing sensors in the distribution network line without the need for new equipment. The computational complexity is small, which can reduce the utilization of computing resources, thereby improving the efficiency of the distribution network fault identification and location method. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 any creative work.

[0042] Figure 1 This is a diagram of an application environment of a distribution network fault identification and location method in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a method for identifying and locating a distribution network fault in one embodiment;

[0044] Figure 3 A schematic flow chart of a method for identifying and locating a distribution network fault in another embodiment;

[0045] Figure 4 This is a structural block diagram of a distribution network fault identification and positioning device in one embodiment;

[0046] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] The distribution network fault identification and location method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 is used to obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line; perform morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence; when the morphological feature data meets the preset identification conditions, the target fault location corresponding to the distribution network line is determined based on the admittance parameter matrix.

[0049] Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0050] In an exemplary embodiment, Figure 2 As shown, a distribution network fault identification and positioning method is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 202 to 206.

[0051] Step 202: Obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line.

[0052] Among them, the zero-sequence electrical sequence includes the zero-sequence voltage sequence { } and zero sequence current sequence { }, where i represents the node number corresponding to each collection node in the distribution network line.

[0053] For example, if the distribution network has a transformer with a neutral-point grounding device, the zero-sequence voltage sequence can be directly obtained at the neutral-point voltage measurement point. If the neutral-point grounding is achieved using an arc suppression coil, the zero-sequence voltage can be calculated by measuring the voltage between the arc suppression coil's neutral point and ground. Alternatively, the zero-sequence voltage sequence can be detected using the open-delta winding of a voltage transformer. Alternatively, the three-phase voltages can be directly measured and zero-sequence calculation performed on the three-phase voltages to obtain the zero-sequence voltage sequence.

[0054] For example, the zero-sequence current sequence can be a sequence of current values flowing through the neutral point of each acquisition node; alternatively, a current transformer can be installed at the grounding resistor or arc suppression coil to measure the current flowing therethrough, thereby obtaining the zero-sequence current sequence; alternatively, the zero-sequence current sequence can be calculated by measuring the three-phase current; alternatively, the zero-sequence current sequence can be directly measured by passing the three-phase conductors and the neutral conductor through a zero-sequence current transformer. If protective equipment is installed at the end of the line or at the load, the equipment's built-in current sensor can be used to measure the three-phase current and calculate the zero-sequence current transformer.

[0055] Among them, the admittance parameter matrix corresponding to the distribution network line is , It represents the admittance between the collection node i and the collection node i-1 in the distribution network line.

[0056] Step 204 : performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence.

[0057] The morphological feature extraction process refers to performing morphological calculations on the zero-sequence electrical sequence, performing morphological processing such as erosion, expansion, opening, or closing operations on each electrical data in the zero-sequence electrical sequence using selected structural elements to obtain electrical morphological data, and then performing feature extraction on the electrical morphological data to obtain morphological feature data. Exemplary feature extraction processes may include extracting an envelope curve of the electrical morphological data, marking mutation points in the electrical morphological data, or extracting peak features as morphological feature data.

[0058] Step 206 : When the morphological feature data meets the preset identification conditions, the target fault location corresponding to the distribution network line is determined based on the admittance parameter matrix.

[0059] The admittance parameter matrix is a mathematical model that describes the electrical relationships between each collection node in a distribution network. Each admittance parameter in the matrix reflects the impedance, distributed capacitance, and other electrical parameters of the line between each collection node. Using the zero-sequence electrical data and admittance parameters corresponding to the collection nodes, the distance between the fault location and the collection node can be accurately calculated, thereby pinpointing the target fault location.

[0060] In the above-mentioned distribution network fault identification and location method, the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line are obtained; morphological feature extraction and processing are performed on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence; and when the morphological feature data meets preset identification conditions, the target fault location corresponding to the distribution network line is determined based on the admittance parameter matrix. In this way, by extracting morphological feature data from the zero-sequence electrical sequence, faults can be quickly and effectively identified and located, and the target fault location is calculated using the admittance parameter matrix. Data acquisition is low in complexity, and data can be acquired from existing sensors in the distribution network line without the need for new equipment. Furthermore, the computational complexity is low, which can reduce computing resource utilization, thereby improving the efficiency of the distribution network fault identification and location method.

[0061] In an exemplary embodiment, based on Figure 2 In the embodiment shown, the method provided herein further includes the following steps: performing morphological feature extraction on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence.

[0062] For each acquisition node, the zero-sequence electrical data corresponding to the acquisition node in the zero-sequence electrical sequence is subjected to morphological analysis to obtain highlighted electrical data and dimmed electrical data; the electrical morphological data corresponding to the acquisition node is determined based on the proportional coefficient of the highlighted electrical data and the dimmed electrical data; and the morphological feature data is obtained based on the electrical morphological data corresponding to each acquisition node.

[0063] Among them, the morphological analysis processing includes: performing open operation processing on the zero-sequence electrical data to obtain open operation data; performing difference processing on the zero-sequence electrical data and the open operation data to obtain highlighted electrical data; performing closed operation processing on the zero-sequence electrical data to obtain closed operation data; performing difference processing on the zero-sequence electrical data and the closed operation data to obtain dim electrical data.

[0064] For example, for the zero-sequence voltage corresponding to the acquisition node i , for zero sequence voltage Perform opening operation to obtain opening operation voltage; convert zero sequence voltage Perform difference processing on the open operation voltage to obtain the highlight voltage corresponding to the acquisition node i The highlight voltage sequence corresponding to each acquisition node can be expressed as { }. Perform closed operation on the zero-sequence voltage Ui to obtain the closed operation voltage; The difference between the closed operation voltage and the gray voltage corresponding to the acquisition node i is obtained. The gray voltage sequence corresponding to each acquisition node can be expressed as { }.

[0065] For example, for the zero-sequence current corresponding to the acquisition node i , for zero sequence current Perform opening operation to obtain opening operation current; convert zero sequence current Perform difference processing on the open operation current to obtain the highlighted current corresponding to the acquisition node i The highlighted current sequence corresponding to each acquisition node can be expressed as { }. For zero sequence current Perform closed operation processing to obtain closed operation current; convert zero sequence current Perform difference processing on the closed-loop current to obtain the gray current corresponding to the acquisition node i The gray current sequence corresponding to each acquisition node can be expressed as { }.

[0066] In one possible embodiment, in the provided method, the process of obtaining a highlighted electrical sequence and a dimmed electrical sequence further includes: for each acquisition node, preprocessing each electrical data in the zero-sequence electrical sequence to obtain sharpened electrical data; performing morphological feature extraction processing on the sharpened electrical data to obtain morphological feature data corresponding to the zero-sequence electrical sequence.

[0067] Among them, for the zero-sequence voltage sequence in the zero-sequence electrical sequence { }, the preprocessing process can be to use the Prewitt operator to calculate the zero-sequence voltage sequence { } in each zero sequence voltage Perform edge detection processing to obtain sharpening voltage ; For the zero-sequence current sequence in the zero-sequence electrical sequence { }, the Roberts operator can be used to calculate the zero-sequence current sequence { } in each zero sequence current Processing to obtain sharpening current In this way, the signal curve edge can be effectively sharpened, noise interference can be eliminated, and the accuracy of the distribution network fault identification and positioning method can be improved.

[0068] In one possible embodiment, the process of determining the electrical morphology data corresponding to the acquisition node based on the proportional coefficient of the highlighted electrical data and the dimmed electrical data in the provided method may further include: for each acquisition node, taking the acquisition node as the target acquisition node and the previous acquisition node of the acquisition node as the adjacent acquisition node; performing difference processing on the highlighted electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain a highlighted electrical difference; performing difference processing on the dimmed electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain a dimmed electrical difference; and performing ratio processing on the highlighted electrical difference and the dimmed electrical difference to obtain the electrical morphology data corresponding to the target acquisition node.

[0069] For example, for the target acquisition node i and the adjacent acquisition node i-1, the electrical morphology data may include voltage morphology data and current morphology data. The highlighted voltage difference may be expressed as: , the gray electrical difference can be expressed as: , the voltage shape data corresponding to the target acquisition node i can be expressed as:

[0070] .

[0071] For example, for the target collection node i and the adjacent collection node i-1, the highlight current difference can be expressed as: , the gray electrical difference can be expressed as: , the current shape data corresponding to the target acquisition node i can be expressed as:

[0072] .

[0073] In one possible embodiment, the process of obtaining morphological feature data based on the electrical morphological data corresponding to each acquisition node in the provided method may further include: when the voltage morphological data is greater than or equal to a preset voltage fluctuation rate threshold, using the acquisition node corresponding to the voltage morphological data as a convex voltage node; when the voltage morphological data is less than the voltage fluctuation rate threshold, using the acquisition node corresponding to the voltage morphological data as a concave voltage node; when the current morphological data is greater than or equal to a preset current fluctuation rate threshold, using the acquisition node corresponding to the current morphological data as a convex current node; when the current morphological data is less than the current fluctuation rate threshold, using the acquisition node corresponding to the current morphological data as a concave current node; and determining the morphological feature data based on the number of convex voltage nodes, concave voltage nodes, convex current nodes, and concave current nodes.

[0074] For example, the acquisition node i corresponds to the voltage fluctuation rate threshold and current fluctuation rate threshold ,if:

[0075] ,

[0076] The acquisition node i is considered to be a convex voltage node, otherwise it is considered to be a concave voltage node, and the number of concave voltage nodes is counted. and the number of bump voltage nodes .

[0077] if:

[0078] ,

[0079] The acquisition node i is considered to be a convex current node, otherwise it is considered to be a concave current node, and the number of concave current nodes is counted. and the number of bump current nodes .

[0080] The morphological feature data can be expressed as:

[0081] .

[0082] In an exemplary embodiment, based on Figure 2 In the embodiment shown, the method provided is to determine the target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets the preset identification conditions, including: obtaining the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the distribution network line according to the admittance parameter matrix; determining the first fault node according to the degree of deviation between the forward adjacent node voltage difference and the backward adjacent node voltage difference; and determining the target fault location according to the branch situation corresponding to the distribution network line and the first fault node.

[0083] In a possible implementation, the recognition condition may include: the morphological feature data is greater than or equal to a preset determination coefficient ,Right now:

[0084] .

[0085] When the morphological characteristic data is greater than or equal to the determination coefficient In the case of , it means that a weak fault has occurred in the distribution network line, and the positioning and identification algorithm is started to locate the distribution network fault.

[0086] When the morphological feature data meets the preset recognition conditions, the forward-pushed adjacent node voltage difference corresponding to the acquisition node i in the distribution network line is It can be expressed as:

[0087] ;

[0088] Push back the voltage difference of adjacent nodes It can be expressed as:

[0089] .

[0090] Among them, the above-mentioned forward adjacent node voltage difference And push back the adjacent node voltage difference It represents the voltage difference corresponding to the collection node i on the main line L of the distribution network.

[0091] The process of determining the first fault node may include: performing difference processing on the voltage difference of the forward adjacent nodes and the voltage difference of the backward adjacent nodes to obtain the deviation degree corresponding to each acquisition node; and taking the acquisition node with the largest deviation degree as the first fault node. The deviation degree corresponding to acquisition node i It can be expressed as:

[0092] .

[0093] The deviation degree corresponding to the first fault node can be expressed as .

[0094] In a possible implementation, the distribution network line does not have branch lines, and the distance between the target fault location and the first fault node is calculated based on the voltage difference between the backward adjacent nodes corresponding to the first fault node. Assuming that the acquisition node i is the first fault node, the ratio of the distance between the target fault location and the first fault node to the total length of the distribution network line is It can be expressed as:

[0095] .

[0096] In one possible embodiment, the process of determining the target fault location according to the branch situation corresponding to the distribution network line and the first fault node in the provided method may further include: in the case where there is a branch line in the distribution network line, obtaining the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node according to the zero-sequence voltage and admittance parameters corresponding to the first fault node; determining the second fault node according to the degree of deviation between the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node; and determining the target fault location according to the first fault node and the second fault node.

[0097] Among them, the zero-sequence voltage corresponding to the first fault node can be obtained from the zero-sequence electrical sequence, and the admittance parameter can be obtained from the admittance parameter matrix. The forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node are the branch lines with the first fault node as the head end. The forward-pushing adjacent node voltage difference and the backward-pushing adjacent node voltage difference corresponding to each acquisition node on .

[0098] For example, branch lines The forward adjacent node voltage difference corresponding to the acquisition node j on the can be expressed as , the voltage difference between adjacent nodes can be expressed as , branch line The collection node with the largest deviation is taken as the second fault node. The deviation degree corresponding to collection node j is It can be expressed as:

[0099] .

[0100] The deviation degree corresponding to the second fault node can be expressed as .

[0101] In a possible implementation, the method provided herein determines the target fault location based on the first fault node and the second fault node, including: when the first fault node and the second fault node are the same fault node, indicating that the branch line There is no secondary branch on , the second fault node is taken as the fault base point, and the target fault location is the branch line with the second fault node as the head end. On; if , the first fault node is taken as the fault base point, the target fault location is on the main line L with the first fault node as the head end, and the ratio of the distance from the target fault location to the first fault node to the total length of the distribution network line is calculated. The target fault location can be determined accurately. Otherwise, the second fault node is used as the fault base point, and the target fault location is located on the branch line with the second fault node as the head end. superior.

[0102] If the first fault node and the second fault node are not the same fault node, it means there is a secondary branch line. According to the zero-sequence voltage and admittance parameters corresponding to the second fault node, the voltage difference of the forward adjacent node and the voltage difference of the backward adjacent node corresponding to the second fault node are obtained; according to the degree of deviation between the voltage difference of the forward adjacent node and the voltage difference of the backward adjacent node corresponding to the two fault nodes, the third fault node is determined. The method of determining the third fault node is the same as that of the branch line. The situation is similar and will not be described here.

[0103] Since there is no third-level branch in the general distribution network line, the third fault node can be used as the fault base point. Determine the target fault location.

[0104] In an exemplary embodiment, Figure 3 As shown, a distribution network fault identification and positioning method is provided, which is applied to Figure 1The server 104 in the example is used as an example to illustrate the process, including the following steps 301 to 206.

[0105] Step 301: Obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line.

[0106] Step 302 : For each acquisition node, pre-process each electrical data in the zero-sequence electrical sequence to obtain sharpened electrical data.

[0107] Step 303 : performing morphological analysis on the zero-sequence electrical data corresponding to the acquisition node in the zero-sequence electrical sequence to obtain highlighted electrical data and dimmed electrical data.

[0108] Step 304 : determining electrical morphological data corresponding to the acquisition node according to a ratio coefficient between the highlighted electrical data and the dimmed electrical data.

[0109] Among them, the process of determining the electrical morphology data may include: for each acquisition node, taking the acquisition node as the target acquisition node and taking the previous acquisition node of the acquisition node as the adjacent acquisition node; performing difference processing on the highlighted electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain the highlighted electrical difference; performing difference processing on the dim electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain the dim electrical difference; performing ratio processing on the highlighted electrical difference and the dim electrical difference to obtain the electrical morphology data corresponding to the target acquisition node.

[0110] Step 305 , determining whether the voltage shape data is greater than or equal to a voltage fluctuation rate threshold.

[0111] Step 306 : When the voltage shape data is greater than or equal to a preset voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as a salient voltage node.

[0112] Step 307 : When the voltage shape data is less than the voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as a concave voltage node;

[0113] Step 308 : Determine whether the current shape data is greater than or equal to a current fluctuation rate threshold.

[0114] Step 309 , when the current shape data is greater than or equal to a preset current fluctuation rate threshold, the acquisition node corresponding to the current shape data is used as a salient current node;

[0115] Step 310 , when the current shape data is less than the current fluctuation rate threshold, the acquisition node corresponding to the current shape data is used as a concave current node;

[0116] Step 311 : determining morphological feature data according to the number of convex voltage nodes, concave voltage nodes, convex current nodes, and concave current nodes.

[0117] Step 312: determine whether the morphological feature data meets the preset recognition conditions.

[0118] Step 313: When the morphological feature data meets the preset recognition conditions, the forward-pushed adjacent node voltage difference and the backward-pushed adjacent node voltage difference corresponding to the distribution network line are obtained according to the admittance parameter matrix;

[0119] Step 314: determining a first fault node based on the degree of deviation between the forward-determined adjacent node voltage difference and the backward-determined adjacent node voltage difference;

[0120] Step 315: Determine whether there is a branch line in the distribution network line.

[0121] Step 316 : When there are branch lines in the distribution network, obtain the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node based on the zero-sequence voltage and admittance parameters corresponding to the first fault node.

[0122] Step 317: Determine a second fault node based on the degree of deviation between the voltage difference of the forward adjacent node and the voltage difference of the backward adjacent node corresponding to the first fault node;

[0123] Step 318: Determine whether the first faulty node and the second faulty node are the same faulty node.

[0124] Step 319 : When the first fault node and the second fault node are the same fault node, determine whether the deviation degree of the first fault node is greater than the deviation degree of the second fault node.

[0125] Step 320: If the deviation degree of the first fault node is greater than the deviation degree of the second fault node, the first fault node is used as the fault base point.

[0126] Step 321: If the deviation degree of the first fault node is less than or equal to the deviation degree of the second fault node, the second fault node is used as the fault base point.

[0127] Step 322 : When the first fault node and the second fault node are not the same fault node, determine the third fault node according to the degree of deviation between the voltage difference of the forward adjacent node and the voltage difference of the backward adjacent node corresponding to the two fault nodes.

[0128] Step 323: Use the third fault node as the fault base point.

[0129] Step 324: Determine the target fault location.

[0130] It should be understood that, although the various steps in the flow charts involved in the above embodiments are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flow charts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same node, but can be performed at different nodes, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps.

[0131] Based on the same inventive concept, embodiments of the present application also provide a distribution network fault identification and location device for implementing the distribution network fault identification and location method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more distribution network fault identification and location device embodiments provided below can be found in the limitations of the distribution network fault identification and location method described above and will not be repeated here.

[0132] In an exemplary embodiment, Figure 4 As shown, a distribution network fault identification and positioning device is provided, including: a data acquisition module 402, a feature extraction module 404 and a fault positioning module 406, wherein:

[0133] The data acquisition module 402 is used to obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line;

[0134] The feature extraction module 404 is used to perform morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence;

[0135] The fault location module 406 is configured to determine a target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets a preset recognition condition.

[0136] In one embodiment, the feature extraction module 404 is also used to perform morphological analysis on the zero-sequence electrical data corresponding to the acquisition node in the zero-sequence electrical sequence for each acquisition node to obtain highlighted electrical data and dimmed electrical data; determine the electrical morphological data corresponding to the acquisition node based on the proportional coefficient of the highlighted electrical data and the dimmed electrical data; and obtain morphological feature data based on the electrical morphological data corresponding to each acquisition node.

[0137] In one embodiment, the feature extraction module 404 is further used to, for each acquisition node, take the acquisition node as the target acquisition node and the previous acquisition node of the acquisition node as the adjacent acquisition node; perform difference processing on the highlighted electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain the highlighted electrical difference; perform difference processing on the dim electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain the dim electrical difference; perform ratio processing on the highlighted electrical difference and the dim electrical difference to obtain the electrical morphology data corresponding to the target acquisition node.

[0138] In one embodiment, the electrical morphology data includes voltage morphology data and current morphology data, and the feature extraction module 404 is further used to use the acquisition node corresponding to the voltage morphology data as a convex voltage node when the voltage morphology data is greater than or equal to a preset voltage fluctuation rate threshold; use the acquisition node corresponding to the voltage morphology data as a concave voltage node when the voltage morphology data is less than the voltage fluctuation rate threshold; use the acquisition node corresponding to the current morphology data as a convex current node when the current morphology data is greater than or equal to a preset current fluctuation rate threshold; use the acquisition node corresponding to the current morphology data as a concave current node when the current morphology data is less than the current fluctuation rate threshold; and determine the morphological feature data based on the number of convex voltage nodes, concave voltage nodes, convex current nodes, and concave current nodes.

[0139] In one embodiment, the fault location module 406 is further configured to obtain the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the distribution network line based on the admittance parameter matrix; determine the first fault node based on the degree of deviation between the forward adjacent node voltage difference and the backward adjacent node voltage difference; and determine the target fault location based on the branch situation corresponding to the distribution network line and the first fault node.

[0140] In one embodiment, the fault location module 406 is further used to obtain the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node based on the zero-sequence voltage and admittance parameters corresponding to the first fault node when there is a branch line in the distribution network line; determine the second fault node based on the degree of deviation between the forward adjacent node voltage difference and the backward adjacent node voltage difference corresponding to the first fault node; and determine the target fault location based on the first fault node and the second fault node.

[0141] Each module in the above-mentioned distribution network fault identification and location device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0142] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store zero-sequence electrical sequences and admittance parameter matrices. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a distribution network fault identification and positioning method is implemented.

[0143] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0148] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0149] The technical features of the above embodiments can 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.

[0150] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for identifying and locating distribution network faults, characterized in that: The method comprises: Obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line; For each acquisition node, performing morphological analysis on the zero-sequence electrical data corresponding to the acquisition node in the zero-sequence electrical sequence to obtain highlighted electrical data and dimmed electrical data; determining electrical morphological data corresponding to the acquisition node according to a ratio coefficient between the highlighted electrical data and the dimmed electrical data; Obtaining morphological feature data according to the electrical morphological data corresponding to each of the acquisition nodes; When the morphological feature data meets a preset identification condition, a target fault location corresponding to the distribution network line is determined based on the admittance parameter matrix.

2. The method according to claim 1, characterized in that The determining, based on a ratio coefficient between the highlighted electrical data and the dimmed electrical data, the electrical morphology data corresponding to the acquisition node includes: For each collection node, the collection node is used as a target collection node, and the previous collection node of the collection node is used as an adjacent collection node; Performing difference processing on the highlighted electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain a highlighted electrical difference; Performing difference processing on the gray electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain a gray electrical difference; Ratio processing is performed on the highlighted electrical difference and the dimmed electrical difference to obtain electrical morphology data corresponding to the target acquisition node.

3. The method according to claim 1, characterized in that The electrical morphology data includes voltage morphology data and current morphology data; the morphology feature data is obtained according to the electrical morphology data corresponding to each acquisition node, including: When the voltage shape data is greater than or equal to a preset voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as a salient voltage node; When the voltage shape data is less than the voltage fluctuation rate threshold, the acquisition node corresponding to the voltage shape data is used as a concave voltage node; When the current shape data is greater than or equal to a preset current fluctuation rate threshold, the acquisition node corresponding to the current shape data is used as a salient current node; When the current shape data is less than the current fluctuation rate threshold, the collection node corresponding to the current shape data is used as a concave current node; The morphological feature data is determined according to the bump voltage node, the recess voltage node, the bump current node, and the number of the recess current nodes.

4. The method according to claim 1, wherein The method of determining a target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets a preset identification condition includes: According to the admittance parameter matrix, a forward-pushed adjacent node voltage difference and a backward-pushed adjacent node voltage difference corresponding to the distribution network line are obtained; determining a first fault node according to a degree of deviation between the forward-pushed adjacent node voltage difference and the backward-pushed adjacent node voltage difference; The target fault location is determined according to the branch situation corresponding to the distribution network line and the first fault node.

5. The method according to claim 4, characterized in that The determining the target fault location according to the branch condition corresponding to the distribution network line and the first fault node includes: In the case where there is a branch line on the distribution network line, obtaining a forward adjacent node voltage difference and a backward adjacent node voltage difference corresponding to the first fault node according to the zero-sequence voltage and admittance parameters corresponding to the first fault node; determining a second fault node according to a degree of deviation between a voltage difference of a forward adjacent node and a voltage difference of a backward adjacent node corresponding to the first fault node; The target fault location is determined according to the first fault node and the second fault node.

6. A distribution network fault identification and positioning device, characterized in that: The device comprises: The data acquisition module is used to obtain the zero-sequence electrical sequence and admittance parameter matrix corresponding to each acquisition node in the distribution network line; a feature extraction module configured to perform morphological analysis on the zero-sequence electrical data corresponding to each acquisition node in the zero-sequence electrical sequence to obtain highlighted electrical data and dimmed electrical data; determine the electrical morphological data corresponding to the acquisition node based on a ratio coefficient between the highlighted electrical data and the dimmed electrical data; and obtain morphological feature data based on the electrical morphological data corresponding to each acquisition node; The fault location module is used to determine the target fault location corresponding to the distribution network line based on the admittance parameter matrix when the morphological feature data meets the preset identification conditions.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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