Distribution network fault identification and positioning method, device, equipment, medium and product

Abstract

The invention relates to a distribution network fault identification and positioning method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a zero-sequence electrical sequence and an admittance parameter matrix corresponding to each collection 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 a target fault position corresponding to the distribution network line based on the admittance parameter matrix under the condition that the morphological feature data meets a preset identification condition. According to the distribution network fault identification and positioning method, the occupancy rate of computing resources can be reduced, and the efficiency of the distribution network fault identification and positioning method is improved.

Description

Technical Field

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

[0002] In the power system, due to the complex distribution network line environment, external environment such as tree barriers, external force damage, lightning strikes, etc., it is easy to cause high-resistance grounding faults in the distribution network line. Due to the large grounding impedance, the grounding fault current is weak and changes rapidly. Therefore, it is necessary to identify and locate distribution network faults to timely discover faults in distribution network lines.

[0003] The traditional distribution network fault identification and location method relies on machine learning models to extract frequency domain or time domain signal features from the collected electrical signals, such as wavelet energy spectrum, multiple harmonic features, etc., and 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 identification and positioning efficiency in response to the above technical problems.

[0006] In a first aspect, the present application provides a distribution network fault identification and location method, 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, a morphological feature extraction process is performed on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence, including:

[0011] 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;

[0012] Determine the electrical morphological data corresponding to the acquisition node according to 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, the morphological characteristic data is obtained.

[0014] In one embodiment, determining electrical morphological data corresponding to a collection node according to a ratio coefficient of highlighted electrical data and dimmed electrical data includes:

[0015] 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;

[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] Performing 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 ratio of the highlighted electrical difference to the dimmed electrical difference is processed to obtain the electrical morphology data corresponding to the target acquisition node.

[0019] In one embodiment, the electrical morphology data includes voltage morphology data and current morphology data; and the morphology feature data is obtained according to 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 a concave 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 a 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 according to the branch situation corresponding to the distribution network line and the first fault node.

[0029] In one embodiment, determining a target fault location according to a branch situation 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] Determine a second fault node according to a deviation between a voltage difference of a forward adjacent node and a voltage difference of a backward adjacent node corresponding to a fault node;

[0032] A target fault location is determined according to the first fault node and the second fault node.

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

[0034] A 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, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method of the first aspect are implemented.

[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 above-mentioned distribution network fault identification and positioning method, device, equipment, medium and product, one of which provides a distribution network fault identification and positioning method, by obtaining the zero-sequence electrical sequence and the admittance parameter matrix corresponding to each acquisition node in the distribution network line; extracting the morphological features of the zero-sequence electrical sequence to obtain the morphological feature data corresponding to the zero-sequence electrical sequence; when the morphological feature data meets the preset identification conditions, based on the admittance parameter matrix, determine the target fault position corresponding to the distribution network line. In this way, by extracting the morphological feature data from the zero-sequence electrical sequence, the fault can be identified and located quickly and effectively, and the target fault position is calculated using the admittance parameter matrix. The data acquisition complexity is low, and data can be obtained based on the existing sensors in the distribution network line without the need for new equipment, and the amount of calculation is small, which can reduce the computing resource occupancy rate, thereby improving the efficiency of the distribution network fault identification and positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

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

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

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

[0046] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] The distribution network fault identification and positioning method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the 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 the 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 the morphological feature data corresponding to the zero-sequence electrical sequence; when the morphological feature data meets the preset recognition conditions, the target fault position corresponding to the distribution network line is determined based on the admittance parameter matrix.

[0049] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent 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, and the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 202 to 206. Among them:

[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 there is a neutral point grounding device of the transformer in the distribution network, the zero-sequence voltage sequence can be directly obtained at the voltage measurement point of the neutral point; if the neutral point grounding adopts an arc suppression coil, the zero-sequence voltage can be calculated by measuring the voltage between the neutral point of the arc suppression coil and the ground. Alternatively, the zero-sequence voltage sequence can be detected by the open delta winding of the voltage transformer. Alternatively, the three-phase voltage can be directly measured, and the zero-sequence calculation processing can be performed on the three-phase voltage to obtain the zero-sequence voltage sequence.

[0054] Exemplarily, the zero-sequence current sequence can be a sequence of current values ​​flowing through the neutral point of each acquisition node; or, a current transformer is installed at the grounding resistor or arc suppression coil to measure the current flowing through it, the zero-sequence current sequence; or, the zero-sequence current sequence is calculated by measuring the three-phase current; or, through the zero-sequence current transformer, the three-phase conductor and the neutral line are passed through the transformer together to directly measure the zero-sequence current sequence. If a protection device is installed at the end of the line or at the load, the built-in current sensor of the device 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 calculation on the zero-sequence electrical sequence, using the selected structural element to perform morphological processing such as corrosion, expansion, opening operation or closing operation on each electrical data in the zero-sequence electrical sequence to obtain electrical morphological data, and then performing feature extraction on the electrical morphological data to obtain morphological feature data. Exemplarily, the feature extraction process can be to extract the envelope curve of the electrical morphological data, or mark the mutation point of the electrical morphological data, or extract the peak feature as the morphological feature data.

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

[0059] Among them, the admittance parameter matrix is ​​a mathematical model that describes the electrical relationship between each acquisition node in the distribution network line. Each admittance parameter in the admittance parameter matrix can reflect the impedance, distributed capacitance and other electrical parameters of the line between each acquisition node. Using the zero-sequence electrical data and admittance parameters corresponding to the acquisition node, the distance between the fault location and the acquisition node can be accurately calculated, thereby determining the target fault location.

[0060] In the above distribution network fault identification and location method, the zero-sequence electrical sequence and the admittance parameter matrix corresponding to each acquisition node in the distribution network line are obtained; the morphological feature extraction and processing of the zero-sequence electrical sequence are performed to obtain the morphological feature data corresponding to the zero-sequence electrical sequence; when the morphological feature data meets the preset identification conditions, the target fault position corresponding to the distribution network line is determined based on the admittance parameter matrix. In this way, by extracting the morphological feature data from the zero-sequence electrical sequence, the fault can be identified and located quickly and effectively, and the target fault position is calculated using the admittance parameter matrix. The data acquisition complexity is low, and data can be obtained based on the existing sensors in the distribution network line without the need for new equipment. In addition, the amount of calculation is small, which can reduce the occupancy rate of computing resources, 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 in the present invention further comprises: performing morphological feature extraction processing 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 according to the proportional coefficient of the highlighted electrical data and the dimmed electrical data; and the morphological feature data is obtained according to 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 the opening operation voltage; convert the zero sequence voltage The difference between the open operation voltage and the high brightness voltage corresponding to the acquisition node i is obtained. 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] As another example, for the zero-sequence current corresponding to the acquisition node i , for zero sequence current Perform opening operation to obtain the opening operation current; convert the zero sequence current The difference between the open operation current and the current corresponding to the acquisition node i is obtained. 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 The difference between the closed operation current and the gray current corresponding to the acquisition node i is obtained. The gray current sequence corresponding to each acquisition node can be expressed as { }.

[0066] In a possible implementation, in the provided method, the process of obtaining a highlighted electrical sequence and a dimmed electrical sequence also 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 { }The zero sequence voltages in 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 analyze the zero-sequence current sequence { }The zero sequence currents in Processing to obtain sharpening current In this way, the edge of the signal curve 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 a possible implementation, the process of determining the electrical morphology data corresponding to the acquisition node according to 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 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 dimmed electrical data corresponding to the target acquisition node and the adjacent acquisition node to obtain the dimmed electrical difference; 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] As another 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 a possible implementation, the process of obtaining morphological feature data according to 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 according to the number of convex voltage nodes, concave voltage nodes, convex current nodes and concave current nodes.

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

[0075] ,

[0076] The collection 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] If the acquisition node i is a convex current node, then it is considered that the acquisition node i is a concave current node, otherwise it is considered that the acquisition node i is 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 position 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; determining the target fault position 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 Represents the voltage difference corresponding to the collection node i on the main line L of the distribution network line.

[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. 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, there is no branch line in the distribution network line, and the distance between the target fault position and the first fault node is calculated based on the voltage difference of 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 position and the first fault node to the total line length of the distribution network line is It can be expressed as:

[0095] .

[0096] In a possible implementation, the process of determining the target fault location according to the branching 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] 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 adjacent node voltage difference and the backward adjacent node voltage difference corresponding to each acquisition node on the .

[0098] For example, branch line The forward adjacent node voltage difference corresponding to the acquisition node j on can be expressed as , the voltage difference between adjacent nodes can be expressed as , the branch line The collection node with the largest deviation degree is taken as the second fault node. The deviation degree corresponding to collection node j 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 manner, the method provided determines the target fault location according to 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 line length of the distribution network line is 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 indicates that 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 second fault node, the third fault node is determined, and 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, and the method is applied to Figure 1The server 104 in the example is used as an example to illustrate, including the following steps 301 to 206. Among them:

[0105] Step 301, obtaining 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 the electrical morphology data corresponding to the acquisition node according to the ratio coefficient of 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, determining 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, determining whether the morphological feature data meets the preset recognition conditions.

[0118] Step 313, when the morphological feature data meets the preset identification conditions, the forward adjacent node voltage difference and the backward 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 according to the degree of deviation between the voltage difference of the forward adjacent nodes and the voltage difference of the backward adjacent nodes;

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

[0121] Step 316, when there is a branch line in the distribution network line, obtain 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.

[0122] Step 317, determining a second 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 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 faulty node is greater than the deviation degree of the second faulty node, the first faulty 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 steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same node, but can be executed at different nodes, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0131] Based on the same inventive concept, the embodiment of the present application also provides a distribution network fault identification and positioning device for implementing the distribution network fault identification and positioning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network fault identification and positioning device embodiments provided below can refer to the limitations of the distribution network fault identification and positioning method 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 the 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 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.

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

[0137] In one of the embodiments, the feature extraction module 404 is also 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 of the embodiments, the electrical morphology data includes voltage morphology data and current morphology data, and the feature extraction module 404 is also used to use the collection 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 collection 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 collection 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 collection 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; 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 of the embodiments, the fault location module 406 is also used to obtain 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; determine the first fault node according to the degree of deviation between the forward adjacent node voltage difference and the backward adjacent node voltage difference; determine the target fault location according to the branch situation corresponding to the distribution network line and the first fault node.

[0140] In one of the embodiments, the fault location module 406 is also 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 are branch lines 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 positioning device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[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, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the 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 through 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0144] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, 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 can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0149] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network fault identification and location method, 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; Performing 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 position 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 step of performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence includes: For each acquisition node, a morphological analysis process is performed 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; The morphological characteristic data is obtained according to the electrical morphological data corresponding to each of the acquisition nodes.

3. The method according to claim 2, characterized in that The determining, according to the ratio coefficient of the highlighted electrical data and the dimmed electrical data, the electrical form data corresponding to the acquisition node comprises: 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; The highlighted electrical difference and the dimmed electrical difference are ratio-processed to obtain electrical morphology data corresponding to the target acquisition node.

4. The method according to claim 2, 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 of the acquisition nodes, including: In the case where the voltage shape data is greater than or equal to a preset voltage fluctuation rate threshold, taking the acquisition node corresponding to the voltage shape data as a convex point voltage node; When the voltage shape data is less than the voltage fluctuation rate threshold, taking the acquisition node corresponding to the voltage shape data as a concave point voltage node; In the case where the current shape data is greater than or equal to a preset current fluctuation rate threshold, taking a collection node corresponding to the current shape data as a salient current node; When the current shape data is less than the current fluctuation rate threshold, taking the acquisition node corresponding to the current shape data 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.

5. The method according to claim 1, characterized in that The method of determining a target fault position 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 adjacent node voltage difference and a backward 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 adjacent node voltage difference and the backward 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.

6. The method according to claim 5, characterized in that The determining the target fault location according to the branch situation 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, a forward adjacent node voltage difference and a 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; Determine 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.

7. A distribution network fault identification and positioning device, characterized in that: The device comprises: A 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, used for performing morphological feature extraction processing on the zero-sequence electrical sequence to obtain morphological feature data corresponding to the zero-sequence electrical sequence; A 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.

8. 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 6 are implemented.

9. 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 6 are implemented.

10. 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 6 are implemented.

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