Electric energy meter fault positioning method and device based on topology perception and medium
By constructing the admittance matrix of the electricity meter node and performing orthogonal decoupling analysis, the problem of inaccurate electricity meter status assessment is solved, enabling rapid and real-time fault location and diagnosis, which is applicable to edge computing devices.
Patent Information
- Application Number
- CN202511730701.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-24
- Publication Date
- 2026-01-23
AI Technical Summary
Current status assessments of electricity meters at checkpoints are inaccurate, especially in detecting complex and subtle fault modes or anomalies. Furthermore, existing machine learning algorithms have high resource requirements on edge computing devices and are not easily interpretable, thus failing to meet the needs for rapid on-site assessments.
By constructing the admittance matrix of the energy meter node, using orthogonal operators to decouple the voltage and current loops, obtaining the terminal feature vector, constructing the fault feature matrix, and combining the reference vector to perform fault phase selection and qualitative analysis, the accurate location and nature of the fault can be determined.
It enables rapid, real-time fault location and diagnosis, improves sensitivity to anomalies, can capture minor faults, reduces computational complexity, and is suitable for edge computing devices.
Smart Images
Figure CN121385772A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of electric energy meter abnormality detection, and in particular to an electric energy meter fault positioning method, device and medium based on topology perception. BACKGROUND
[0002] As the core equipment of the gateway electric energy meter, the health state evaluation of the gateway electric energy meter usually depends on the electrical characteristics reflected by the gateway electric energy meter. The existing method usually realizes abnormality recognition by monitoring the electrical characteristics such as voltage and current. The representative method includes overcurrent identification, short-phase identification, zero sequence identification and the like. This kind of method sets threshold values and simple rules based on the measured data of the gateway electric energy meter to judge the state of the electric energy meter. Although this kind of method can effectively identify external wiring abnormalities or serious abnormalities existing in the gateway electric energy meter, it is difficult to find complex subtle fault modes or abnormal conditions due to the neglect of the internal coupling relationship of the measured data of the gateway electric energy meter.
[0003] In view of this problem, the existing technology introduces a machine learning algorithm, which to some extent improves the application of the internal correlation of the measured data of the gateway electric energy meter and greatly improves the application range of state recognition. However, the amount of data contained in the conventional electrical data such as voltage, current and phase is limited, and there is an overfitting phenomenon when using a neural network or other machine learning algorithm. The accuracy of the health degree recognition of the gateway electric energy meter is limited, the abnormal recognition effect for the internal insulation aging or malicious tampering of the internal loop which has weak electrical characteristics is poor, and the non-interpretable nature and large resource demand of the neural network make it impossible to be applied to fast edge computing and mobile terminals, which makes it difficult to meet the technical needs of the on-site evaluation of the state of the existing gateway electric energy meter. SUMMARY
[0004] The technical problem to be solved by the present application is that the existing gateway electric energy meter state evaluation is not accurate. The purpose is to provide an electric energy meter fault positioning method, device and medium based on topology perception, which constructs a gateway electric energy meter node admittance matrix by measuring the internal impedance between each electrical terminal of the electric energy meter, obtains a terminal feature vector by decoupling and analyzing the voltage loop and the current loop through an orthogonal operator, and further obtains a fault feature matrix on the basis of obtaining a fault diagnosis reference constant and a ground reference constant, so as to realize fault phase selection. According to the terminal feature vector The fault qualitative vector is obtained by optimizing the phase selection result, and the exact position and fault property of the fault are obtained by analyzing the characteristics of the elements in the fault qualitative vector.
[0005] The present application is realized by the following technical solutions:
[0006] The first aspect of the present application provides an electric energy meter fault positioning method based on topology perception, which comprises the following specific steps:
[0007] Obtaining electrical data of the electrical loop terminals of the sample electric energy meter, and constructing a node admittance matrix of the gateway electric energy meter;
[0008] Decoupling and analyzing the voltage loop and the current loop through an orthogonal operator to obtain a terminal characteristic vector;
[0009] Based on the terminal characteristic vector, a reference vector is extracted;
[0010] Based on the terminal characteristic vector, a fault feature matrix is constructed, and the reference vector is used for fault phase selection of the fault feature matrix;
[0011] Based on the fault phase selection result, fault qualification is performed to determine the fault location and the fault property.
[0012] Further, the obtaining of the electrical data of the electrical loop terminals of the sample electric energy meter, and the construction of the node admittance matrix of the gateway electric energy meter specifically include:
[0013] Obtaining the electrical loop terminals of the three-phase four-wire sample electric energy meter, and regarding each terminal as an electrical node;
[0014] According to the internal circuit of the electric energy meter, the admittance relationship between the nodes is determined;
[0015] Based on the admittance relationship between the nodes, the equivalent admittance between two terminals is obtained;
[0016] Based on the equivalent admittance, a node admittance matrix of the gateway electric energy meter is constructed.
[0017] Further, the terminal characteristic vector obtained by decoupling and analyzing the voltage loop and the current loop through an orthogonal operator specifically includes:
[0018] Obtaining a current direction vector and a voltage direction vector, and constructing an orthogonal operator vector;
[0019] Based on the orthogonal operator vector and the node admittance matrix of the gateway electric energy meter, the terminal characteristic vector is determined.
[0020] Further, the obtaining of the reference vector specifically includes:
[0021] Obtaining terminal characteristic vectors of n1 electric energy meters, and dividing the terminal characteristic vectors into A-phase characteristic vectors, B-phase characteristic vectors, C-phase characteristic vectors, and ground characteristic vectors;
[0022] Defining a unit reference vector: the unit reference vector is combined with the A-phase characteristic vectors, the B-phase characteristic vectors, and the C-phase characteristic vectors to obtain a phase sequence comprehensive coefficient; and the unit reference vector is combined with the ground characteristic vectors to obtain a ground coefficient;
[0023] The phase sequence synthesis coefficients of n1 samples are averaged to obtain a fault diagnosis reference constant;
[0024] The ground connection coefficients of n1 samples are averaged to obtain a ground reference constant;
[0025] The fault diagnosis reference constant and the ground reference constant are spliced to obtain a reference vector.
[0026] Further, the terminal feature vector is based on the construction of the fault feature matrix, and the reference vector is used for fault selection. Specifically, it includes:
[0027] The terminal feature vector is transposed and reconstructed to obtain a fault feature matrix;
[0028] The fault selection direction vector is constructed to perform matrix multiplication on the fault feature matrix to obtain a fault phase sequence feature vector;
[0029] Based on the reference vector, the fault phase sequence feature vector is corrected to obtain a deviation vector;
[0030] Set the fault diagnosis adjustment coefficient to make a fault judgment on the deviation vector.
[0031] Further, the fault diagnosis adjustment coefficient is set to make a fault judgment on the deviation vector, specifically including:
[0032] If , then it is judged as A phase fault;
[0033] If , then it is judged as B phase fault;
[0034] If , then it is judged as C phase fault;
[0035] If , then it is judged as ground fault.
[0036] Wherein, the elements , , , represent the A phase deviation vector, the B phase deviation vector, the C phase deviation vector, and the G ground deviation vector, respectively, represents the fault diagnosis adjustment coefficient, represents the basis of the phase sequence fault threshold, represents the basis of the ground fault threshold.
[0037] Further, based on the fault selection result, the fault is located, specifically including:
[0038] When it is determined that there is a fault, two items with the largest real and imaginary modulus in the deviation vector are selected;
[0039] If the difference between the two items and the remaining items is greater than a set threshold, the corresponding phase sequence of the two items is recorded as the fault cause point or the serious point;
[0040] If the largest item reaches three items and the difference between them is not greater than a set threshold, it is determined that there is a three-phase fault.
[0041] Further, the determination of the fault position and the fault property comprises:
[0042] n2 terminal feature vectors are obtained, and a reference feature vector is determined
[0043] According to the phase sequence fault result, the elements corresponding to the non-fault phases in the reference feature vector are removed to obtain a fault qualitative vector, wherein the remaining elements in the fault qualitative vector correspond to each terminal of the to-be-tested table one by one;
[0044] The corresponding elements in the fault qualitative vector are compared with the reference feature values of the terminals to determine the fault property:
[0045] If , the fault type of the terminal is short circuit;
[0046] If , the fault type of the terminal is open circuit;
[0047] If , the fault type of the terminal is aging or insulation damage;
[0048] Wherein, represents the fault feature value of the i-th terminal, represents a fault diagnosis adjustment coefficient, represents the reference feature value of the i-th terminal.
[0049] The second aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize a power meter fault positioning method based on topology perception.
[0050] The third aspect of the present application provides a computer readable storage medium, which stores a computer program, wherein the program is executed by a processor to realize a power meter fault positioning method based on topology perception.
[0051] Compared with the prior art, the present application has the following advantages and beneficial effects:
[0052] A decoupling theory is proposed for fault feature extraction mechanism, by constructing complex orthogonal operator with explicit physical meaning, the multi-dimensional impedance parameter space is projected to the orthogonal feature subspace, realizing the decoupling analysis of voltage loop and current loop, revealing the potential fault coupling relationship between different loops, laying a mathematical foundation for pattern separation of complex fault;
[0053] A multi-level decision system based on dynamic benchmark learning is established, by introducing the statistical characteristics of normal equipment group to establish the reference benchmark, and using the relative comparison principle to construct the fault discrimination criterion, the misjudgment risk caused by individual differences of equipment is effectively overcome;
[0054] Through constructing the multi-dimensional analysis framework of fault qualitative vector, the hierarchical decision of fault phase positioning, fault property determination and fault type identification is realized, forming a complete diagnosis chain from regional positioning to accurate diagnosis. BRIEF DESCRIPTION OF DRAWINGS
[0055] In order to more clearly illustrate the technical scheme of the exemplary embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, it should be understood that the following drawings only show some embodiments of the present application, therefore should not be regarded as a limitation on the scope, for those skilled in the art, without paying creative labor, other related drawings can also be obtained according to these drawings. In the drawings:
[0056] Figure 1 The power meter abnormality identification method in the embodiment of the present application. DETAILED DESCRIPTION
[0057] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the following will combine with the embodiments and drawings to make further detailed description on the present application, the exemplary embodiments of the present application and the description are only used to explain the present application, and should not be regarded as a limitation on the present application.
[0058] As a possible implementation manner, as Figure 1As shown, the embodiment provides a topology-aware electric energy meter fault positioning method. The internal impedance between each electrical terminal of the electric energy meter is measured to construct a node admittance matrix of the gateway electric energy meter. The terminal characteristic vector is obtained by decoupling and analyzing the voltage loop and the current loop through the orthogonal operator. The fault characteristic matrix is further obtained on the basis of the obtained fault diagnosis reference constant and the ground reference constant to realize fault phase selection. The fault qualitative vector is obtained by optimizing the terminal characteristic vector on the basis of the phase selection result. The exact position and the fault property are obtained by analyzing the characteristics of the elements in the fault qualitative vector. The node admittance matrix is constructed to establish a mathematical model corresponding to the internal physical topology structure of the electric energy meter. The diagnosis process is based on the basic principles of circuit network, realizes the white-box mapping from external observation to internal structure parameters, solves the problem that the machine learning diagnosis result is difficult to explain the causal relationship between the fault and the data characteristics, and is extremely sensitive to structural defects such as connection point loosening, insulation performance degradation, and line corrosion through monitoring the internal equivalent admittance representing the connection state. Even if no obvious electrical quantity anomaly is caused, the structural defects can also be reflected in the admittance matrix, which can improve the sensitivity of abnormal detection and prevent soft faults such as insulation aging and slight tampering in the initial stage of the internal loop from being captured, which is easy to cause misjudgment,
[0059] Through matrix multiplication, characteristic vector calculation, and reference comparison, the calculation degree is reduced, the state can be evaluated quickly and in real time, and the work requirements of the staff are guaranteed.
[0060] The embodiment is implemented through the following technical means:
[0061] Step 1: Construct the admittance matrix of the gateway electric energy meter .
[0062] The embodiment provides an electric energy meter evaluation method based on a gateway electric energy meter which is a three-phase four-wire electric energy meter. The gateway electric energy meter has 3 voltage terminals, 6 current terminals, and 1 ground terminal, which are labeled as A-phase current in , A-phase voltage , A-phase current out , B-phase current in , B-phase voltage , B-phase current out , C-phase current in , C-phase voltage , C-phase current out , and ground terminal . The above 10 terminals are electric circuit terminals of the gateway electric energy meter. Each terminal is regarded as an electrical node. The admittance relationship between the nodes is determined according to the internal circuit of the electric energy meter. The equivalent admittance between two terminals is obtained based on the admittance relationship between the nodes. The node admittance matrix of the gateway electric energy meter is constructed based on the equivalent admittance.
[0063] Terminal admittance matrix of gateway electric energy meter For Matrix, where element is the equivalent admittance between the ith and jth terminal . The equivalent admittance :
[0064] ;
[0065] where, is the measured admittance between two terminals; , is the equivalent adjustment coefficient, where is an integer, with a value range of [2~4]; is a real number, with a value range of [10~100]; the value principle is to ensure that the modulus length ratio of the maximum element to the minimum element in is less than 100.
[0066] The impedance between each terminal of the gateway electric energy meter is measured by a measuring device, and the terminal admittance matrix of the gateway electric energy meter is constructed according to the above method .
[0067] Step2: Orthogonal decomposition is performed on the terminal admittance matrix of the gateway electric energy meter to obtain the current direction vector and the voltage direction vector, and an orthogonal operator vector is constructed, and the orthogonal operator vector :
[0068] ;
[0069] where is the current direction vector, which is a one-dimensional vector of , where the element value corresponding to the current terminal is 1, the element value corresponding to the voltage terminal is 0, and the element value corresponding to the ground terminal is 0. is the voltage direction vector, which is a one-dimensional vector of , where the element value corresponding to the current terminal is 0, the element value corresponding to the voltage terminal is , and the element value corresponding to the ground terminal is , is the imaginary unit. Multiply the orthogonal operator vector and the terminal admittance matrix of the gateway electric energy meter to obtain the terminal feature vector :
[0070] ;
[0071] The terminal feature vector is a one-dimensional vector, where the elements , , , , , are current features, the elements , , are voltage features, and the elements are ground features.
[0072] Step 3: Fault phase sequence positioning;
[0073] S31: Calculate fault diagnosis reference constant and ground reference constant
[0074] Select n1 normal electric energy meters of the same type as the to-be-tested electric energy meter, and calculate terminal feature vectors in turn according to the method described in Step 2, each terminal feature vector can be divided into A, B, C, G (ground) four phase sequences, where: A-phase feature vector , B-phase feature vector , C-phase feature vector , and ground feature vector .
[0075] Combine the unit reference vector with the A-phase feature vector, the B-phase feature vector, and the C-phase feature vector to obtain phase sequence comprehensive coefficients; combine the unit reference vector with the ground feature vector to obtain a ground coefficient;
[0076] Average the phase sequence comprehensive coefficients of the n1 samples to obtain the fault diagnosis reference constant :
[0077] ;
[0078] wherein is the reference vector, , , denotes the three-phase phase sequence comprehensive coefficients of the 1st, 2nd, …, n1st normal meters.
[0079] Average the ground coefficients of the n1 samples to obtain the ground reference constant :
[0080] ;
[0081] wherein .
[0082] S32: Fault phase determination
[0083] Transposed reconstructed terminal feature vector , reconstructed into a fault feature matrix :
[0084] ;
[0085] Fault phase selection vector :
[0086] ;
[0087] Fault phase sequence feature vector :
[0088] ;
[0089] is a reference vector, ;
[0090] i.e. Terminal feature vector Fault feature matrix Fault phase sequence feature vector Reference vector , we get Thus . Wherein, is a vector of 1, where the elements , , , , represent A, B, C, G (ground) in turn, |, |, |If greater than , the corresponding phase sequence has a fault, |If greater than , the ground terminal has a fault. is the fault diagnosis adjustment coefficient, which is the adjustment coefficient set to deal with the quality control error of the same specified electric energy meter when the reference value is obtained. The more stringent the quality requirements of the electric energy meter, the closer this value should be to 0. Usually, the value is 0~0.1. Specifically:
[0091] If | , it is judged as A phase fault;
[0092] If | , it is judged as B phase fault;
[0093] If | , it is judged as C phase fault;
[0094] If | , it is judged as ground fault.
[0095] wherein, element , , , denote A-phase deviation vector, B-phase deviation vector, C-phase deviation vector, G-ground deviation vector, respectively, denote fault diagnosis adjustment coefficient, denote base quantity of phase sequence fault threshold, denote base quantity of ground fault threshold.
[0096] Based on the fault phase selection result, fault positioning is performed, specifically including:
[0097] respectively select , , , the two items with the largest real and imaginary parts, and the difference between the lengths of the two items and other items is greater than 0.1 , the corresponding phase sequence of the two items is the fault cause point or the fault severity point. If the real and imaginary parts corresponding to the largest three items are three, or the difference between the three items is less than 0.1 , it is a three-phase fault.
[0098] Step4: Fault qualification and positioning
[0099] Select the same type of normal electric energy meter as the to-be-tested electric energy meter , and calculate terminal feature vectors in turn according to the method described in Step2, and then obtain the reference feature vector :
[0100] ;
[0101] Eliminate the elements corresponding to the non-fault items determined in Step3 to obtain the fault qualification vector , and the elements in the fault qualification vector correspond to each terminal of the key electric energy meter , wherein element :
[0102] If , the fault type of the terminal is short circuit;
[0103] If , the fault type of the terminal is open circuit;
[0104] If If the terminal is in the state of
[0105] wherein, represents the fault feature value of the i-th terminal, represents the fault diagnosis adjustment coefficient, represents the reference feature value of the i-th terminal.
[0106] As a possible implementation, the embodiment takes DTZY395C-Z three-phase four-wire controlled intelligent electric energy meter as an example, selects 12 normal and passed electric energy meters in the same batch as dynamic reference measurement data set. Four electric energy meters of this model are selected as diagnostic objects, and the fault types of the four electric energy meters are respectively same-phase voltage and current terminal short circuit, different-phase voltage terminal short circuit, current terminal to ground short circuit, and phase-to-phase voltage terminal open circuit.
[0107] S1: Calculate the fault diagnosis reference constant based on 12 normal and passed electric energy meters as dynamic reference measurement data set and the ground reference constant .
[0108] ;
[0109] ;
[0110] ;
[0111] ;
[0112] S2: Obtain the admittance matrix of four fault electric energy meters, and the faults of the four fault electric energy meters are respectively: 1, same-phase voltage and current terminal short circuit (terminal 2, 3 short circuit), 2, different-phase voltage terminal short circuit (terminal 2, 5 short circuit), 3, current terminal to ground short circuit (terminal 1 and ground terminal short circuit), 4, phase-to-phase voltage terminal open circuit (terminal 1, 3 open circuit), and the admittance matrix of the fault electric energy meter is , , , . The admittance matrix is normalized, and , = 2, the normalized admittance matrix is , , , .
[0113] S3: Further solve the terminal feature vector , , , , , according to the fault diagnosis reference constant and the ground reference constant solving the fault phase sequence characteristic vector :
[0114] , , , .
[0115] S4: fault phase sequence judgment, take 0.1, =3.831, =0.801; according to the results of the diagnosis method in step 3:
[0116] ;
[0117] Under normal circumstances, The corresponding voltage and current terminals are short-circuited (terminals 2 and 3 are short-circuited), terminal 2 is the A-phase voltage phase sequence, the voltage terminal is connected to the ground terminal, and the current terminal is not connected to the ground terminal. When the voltage terminal and the current terminal are short-circuited, the ground terminal will also reflect the fault characteristics.
[0118] S5: according to the reference characteristic vector solved in step 4 , and then solving ,
[0119] , , , ;
[0120] 3.22, The diagnosis result is as follows:
[0121] ;
[0122] Under normal circumstances, terminals 1 and 3 are the input and output terminals of the same phase current loop, and 1 and 3 are the passageway. When one of 1 and 3 is short-circuited with other terminals, the other will also be short-circuited. In summary, the feasibility of the method proposed in the present application in this embodiment is verified.
[0123] As a possible implementation, the present embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it realizes a power meter fault positioning method based on topology awareness.
[0124] As a possible implementation, the embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement a topology-aware power meter fault locating method.
[0125] The above detailed description further describes the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific implementation of the present application and is not intended to limit the scope of protection of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
Claims
1. A method for locating faults in electricity meters based on topology sensing, characterized in that, The specific steps include the following: Obtain electrical data from the electrical circuit terminals of the sample energy meters and construct the node admittance matrix of the energy meters at the gateway. Terminal feature vectors are obtained by decoupling the voltage and current loops using orthogonal operators; Based on the terminal feature vector, the reference vector is extracted; A fault feature matrix is constructed based on terminal feature vectors, and a reference vector is used to select the fault phase from the fault feature matrix. Based on the fault phase selection results, the fault is characterized, and the fault location and nature are determined.
2. The method for fault location of electricity meters based on topology sensing according to claim 1, characterized in that, The process of acquiring electrical data from the electrical circuit terminals of the sample energy meter and constructing the admittance matrix of the energy meter node specifically includes: Obtain the electrical circuit terminals of the three-phase four-wire sample energy meter, and treat each terminal as an electrical node; Determine the admittance relationship between each node based on the internal circuit of the electricity meter; Based on the admittance relationship between each node, the equivalent admittance between the two terminals is obtained; Based on equivalent admittance, the admittance moment of the energy meter node is constructed.
3. The method for fault location of electricity meters based on topology sensing according to claim 1, characterized in that, The method of decoupling the voltage and current loops using orthogonal operators to obtain terminal feature vectors specifically includes: Obtain the current direction vector and voltage direction vector, and construct orthogonal operator vectors; The terminal characteristic vector is determined based on the orthogonal operator vector and the admittance moment of the energy meter node.
4. The method for fault location of electricity meters based on topology sensing according to claim 1, characterized in that, The steps for obtaining the reference vector specifically include: Obtain the terminal feature vectors of n1 energy meters, and divide the terminal feature vectors into phase A feature vector, phase B feature vector, phase C feature vector and grounding feature vector; Define a unit reference vector: The unit reference vector is combined with the characteristic vectors of phase A, phase B, and phase C to obtain the phase sequence comprehensive coefficient; the unit reference vector is combined with the grounding characteristic vector to obtain the grounding coefficient. The fault diagnosis baseline constant is obtained by averaging the phase sequence comprehensive coefficients of n1 samples. The grounding reference constant is obtained by averaging the grounding coefficients of n1 samples. The fault diagnosis reference constant and the grounding reference constant are concatenated to obtain the reference vector.
5. The method for fault location of electricity meters based on topology sensing according to claim 1, characterized in that, The process of constructing a fault feature matrix based on terminal feature vectors and using a reference vector to select the fault phase from the fault feature matrix specifically includes: The fault feature matrix is obtained by transposing and reconstructing the terminal feature vectors; Construct the fault direction selection vector and perform matrix multiplication on the fault feature matrix to obtain the fault phase sequence feature vector; The fault phase sequence characteristic vector is corrected based on the reference vector to obtain the deviation vector; Set fault diagnosis adjustment coefficients and perform fault judgment based on deviation vector.
6. The method for fault location of electricity meters based on topology sensing according to claim 5, characterized in that, The process of setting a fault diagnosis adjustment coefficient and determining the fault based on the deviation vector specifically includes: If | |> If so, it is determined that phase A is faulty; If | |> If so, it is determined to be a phase B fault; If | |> If so, it is determined to be a phase C fault; If | |> If so, it is determined to be a grounding fault; Among them, elements , , , The vectors representing phase A, phase B, phase C, and grounding deviation, respectively, are: phase A deviation vector, phase B deviation vector, phase C deviation vector, and grounding deviation vector, G. This represents the fault diagnosis adjustment coefficient. The fundamental quantity representing the phase sequence fault threshold. The basic quantity representing the ground fault threshold.
7. The method for fault location of electricity meters based on topology sensing according to claim 5, characterized in that, Based on the fault phase selection results, fault location is performed, specifically including: Once a fault is identified, the two terms with the largest real and imaginary moduli in the deviation vector are selected. If the difference between these two items and the remaining items is greater than the set threshold, then the phase sequence corresponding to these two items is recorded as the fault cause point or the critical point. If the largest item reaches three items and the difference between them is not greater than the set threshold, it is determined to be a three-phase fault.
8. The method for fault location of electricity meters based on topology sensing according to claim 1, characterized in that, Determining the location and nature of the fault includes: Obtain n² terminal feature vectors and determine the baseline feature vector. Based on the phase sequence fault results, the non-fault corresponding elements in the reference feature vector are removed to obtain the fault qualitative vector. The elements retained in the fault qualitative vector correspond one-to-one with each terminal of the meter under test. The nature of the fault is determined by comparing the corresponding element in the fault characterization vector with the reference characteristic value of the terminal. like If so, the fault type of the terminal is short circuit; like If so, the fault type of the terminal is open circuit; like If so, the fault type of the terminal is aging or insulation damage; in, This represents the fault characteristic value of the i-th terminal. This represents the fault diagnosis adjustment coefficient. This represents the reference characteristic value of the i-th terminal.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the topology-aware energy meter fault location method as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the topology-aware method for locating electricity meter faults as described in any one of claims 1 to 8.