Fault positioning method, system and equipment based on transformer area topology identification, and medium

Abstract

The invention discloses a transformer area topology identification-based fault positioning method, system and device, and a medium, and the method comprises the steps: inputting an obtained first pulse number of a general meter and second pulse numbers of a plurality of sub-meters into a topology analysis model for solving, so as to determine a first transformer area topology structure; calculating a target correlation coefficient between the users based on each electrical quantity data, removing first user pairs of which the target correlation coefficients do not meet a preset correlation threshold to obtain a plurality of second user pairs, calculating a target DTW distance of each second user pair, taking the second user pair of which the target DTW distance is maximum as a third user pair, and taking the second user pair of which the target DTW distance is maximum as a second user pair of which the target DTW distance is maximum as a second user pair of which the target DTW distance is maximum as a third user pair; performing iterative optimization by taking the third user pair as an initial clustering center until a clustering termination condition is met, and determining a second transformer area topological structure according to an output target clustering result; and comparing the first transformer area topological structure with the second transformer area topological structure, if the first transformer area topological structure and the second transformer area topological structure are the same, determining a target transformer area topological structure, and performing fault positioning based on the target transformer area topological structure. According to the invention, the transformer area fault point can be accurately positioned.

Description

Technical Field

[0001] The present application relates to the field of power metering, and in particular to a fault location method, system, device and medium based on substation topology identification. Background Art

[0002] As a critical terminal unit of the power system, the accuracy of the low-voltage distribution substation's topology is crucial for the efficient operation and fault handling of the distribution network. Due to the complex wiring of low-voltage distribution substations, problems such as equipment outages and unauthorized connections, coupled with the widespread use of distributed energy, substation topology identification has become crucial for accurate fault location, emergency repairs, and the construction of new power systems. Accurately identifying the substation topology can clearly define the correspondence between distribution transformers and users, providing an accurate topological basis for fault location, quickly locating the fault point, narrowing the scope of investigation, improving operation and maintenance efficiency, and reducing economic losses.

[0003] Existing substation topology identification technologies primarily include the following: First, manual data entry, while simple and straightforward, is time-consuming and labor-intensive, making it difficult to adapt to grid transformation and identify faulty meters in a timely manner. Second, carrier communication methods analyze signal characteristics to identify transformer-user relationships, but are affected by clock errors, communication quality, and other factors, resulting in long identification cycles and poor real-time performance. Third, instantaneous short-circuit methods actively create short-circuit pulses to identify topology, but this poses a threat to equipment and grid safety and is costly. Fourth, big data methods use collected electrical quantity data combined with algorithmic analysis to determine transformer-user relationships. However, these methods are limited by factors such as the synchronization, accuracy, and adequacy of data collection, resulting in uncertain accuracy. These methods suffer from long data acquisition times, asynchronous data collection, and imprecise judgments, leading to inaccurate and inefficient fault location and difficulty meeting practical application requirements. Summary of the Invention

[0004] The present application provides a fault location method, system, device and medium based on substation topology identification to improve the accuracy of substation topology identification and thus accurately locate the substation fault point.

[0005] In a first aspect, the present application provides a fault location method based on substation topology identification, comprising:

[0006] Obtain the first pulse number of the main meter in the meter box, and obtain the second pulse number and electrical quantity data of several sub-meters in the meter box;

[0007] Inputting the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determining a first substation topology structure based on each of the attribution relationship coefficients, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table;

[0008] Based on the electrical quantity data, a target correlation coefficient between users is calculated, and first user pairs whose target correlation coefficients do not meet a preset correlation threshold are eliminated to obtain a plurality of second user pairs. A target DTW distance is calculated for each of the second user pairs, and the second user pair with the largest target DTW distance is used as a third user pair. The third user pair is used as an initial cluster center for iterative optimization until a clustering termination condition is met, and a second substation topology structure is determined according to the output target clustering result.

[0009] The first substation area topology structure and the second substation area topology structure are compared. If the two are the same, a target substation area topology structure is determined, and fault location is performed based on the target substation area topology structure to determine the faulty electric energy meter in the substation area.

[0010] The embodiment of the present application can collect the electric energy metering information and operating status data of each electric energy meter in the meter box by obtaining the first pulse number of the total meter and the second pulse number and electric quantity data of the sub-meter, thereby providing the necessary information support for the subsequent topological structure identification using different methods; the collected pulse number is processed and analyzed by using a preset topological analysis model, and key information reflecting the topological relationship of the substation area can be extracted from the pulse number, and the topological structure of the substation area can be preliminarily established; by calculating the target correlation coefficient between users, user pairs with high correlation can be screened out, and the user set that may be in the same substation area can be preliminarily determined; by eliminating user pairs that do not meet the correlation threshold, the interference of irrelevant data is reduced, making the analysis more targeted. , then calculate the target DTW distance and determine the initial cluster center for iterative optimization until the cluster termination condition is met. This series of operations can make full use of the time series characteristics of the electrical quantity data, and mine the similarities and differences in electrical characteristics between users, so as to more accurately divide the substation topology structure, and can identify the substation topology from another dimension, complement and verify the topology identification results based on the number of pulses, and further improve the accuracy and reliability of topology identification; by comparing the two topological structures, the credibility and accuracy of the topological identification results are greatly improved; fault location is performed based on the determined topological structure, which can quickly and accurately locate the faulty electric energy meter in the substation, thereby achieving rapid response and processing of the fault. Compared with the existing technology, the present application can improve the accuracy of substation topology identification, and thus accurately locate the substation fault point.

[0011] Furthermore, the calculation formula of the topology analysis model is specifically:

[0012]

[0013] Where, e j represents the attribution relationship coefficient between the jth sub-table and the current area master table, where j = 1, 2, ..., p; The measurement values ​​of different time series of the same sub-table; is the measurement value of different time series of the total table; n is the number of data sets to be collected; ΔP j is the second pulse number of all sub-tables whose topological relationships are to be determined, where j = 1, 2, ..., p; c p is the pulse constant of all sub-meters whose topological relationships are to be determined; ΔN k is the first pulse number in the total table of the area, c k is the pulse constant of the total table of the substations, where k is the number of corresponding different substations.

[0014] Furthermore, the electrical quantity data includes voltage data, current data, and power data, and the target DTW distance of each second user pair is calculated as follows:

[0015] Constructing a corresponding first distance matrix, a second distance matrix, and a third distance matrix for the voltage data, the current data, and the power data respectively;

[0016] Calculating a first cumulative matrix based on the first distance matrix, calculating a second cumulative matrix based on the second distance matrix, and calculating a third cumulative matrix based on the third distance matrix;

[0017] For each accumulation matrix, find the optimal path with the smallest cumulative distance, and calculate the cumulative distance of the optimal path to obtain a first DTW distance, a second DTW distance, and a third DTW distance;

[0018] The first DTW distance, the second DTW distance, and the third DTW distance are weightedly fused according to preset weights to obtain a target DTW distance for each second user pair.

[0019] By calculating the target DTW distance, we can more accurately measure the similarity of their electrical characteristics, making it easier to subsequently build a substation topology that is more in line with actual conditions.

[0020] Furthermore, based on the electrical quantity data, the target correlation coefficient between users is calculated, specifically:

[0021] Calculating a first correlation coefficient for the voltage data between users;

[0022] calculating a second correlation coefficient for the current data between users;

[0023] Calculating a third correlation coefficient for the power data between users, wherein the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all Pearson correlation coefficients;

[0024] A target correlation coefficient between users is determined based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

[0025] In this way, by calculating the target correlation coefficient between users, we can screen out user pairs with high correlation and preliminarily determine the set of users that may be in the same area. By eliminating user pairs that do not meet the correlation threshold, we can reduce the interference of irrelevant data and make the analysis more targeted.

[0026] Furthermore, the second user pair with the largest target DTW distance is used as the third user pair, and the third user pair is used as the initial cluster center for iterative optimization until the clustering termination condition is met, and the second station area topology structure is determined according to the output target clustering result, specifically:

[0027] The second user pair with the largest target DTW distance is used as the third user pair, and the third user pair is used as the initial cluster center. The fourth DTW distance between each user and the initial cluster center is calculated, and each user is assigned to the initial cluster center with the closest fourth DTW distance.

[0028] For each user assigned to each of the initial cluster centers, a mean sequence of the electrical quantity data is calculated, and a second cluster center is iteratively updated based on the mean sequence until the updated second cluster center meets a preset clustering termination condition, an updated target clustering result is output, and a second substation topology structure is determined based on the target clustering result.

[0029] In this way, by determining the initial cluster center and performing iterative optimization until the cluster termination conditions are met, this series of operations can make full use of the time series characteristics of electrical quantity data, explore the similarities and differences in electrical characteristics between users, and thus more accurately divide the substation topology structure. The substation topology can be identified from another dimension, complementing and verifying the topology identification results based on the number of pulses, further improving the accuracy and reliability of topology identification.

[0030] Furthermore, the first area topology structure and the second area topology structure are compared, and if the two are the same, a target area topology structure is determined, specifically:

[0031] Determine a difference matrix by comparing the first station area topology structure and the second station area topology structure element by element;

[0032] If the difference values ​​of all elements in the difference matrix are 0, it is determined that the two are the same, and the target area topology is determined.

[0033] In this way, by comparing the two topological structures, the credibility and accuracy of the topological recognition results are greatly improved.

[0034] Furthermore, the fault location is performed based on the target area topology structure to determine the faulty electric energy meter in the area, specifically:

[0035] Determining the third pulse number of each electric meter in the target area topology, wherein the electric meters include a main meter and sub-meters;

[0036] Based on the third pulse number, the Pearson correlation coefficient is used to calculate the electric energy pulse correlation coefficient between each meter, and it is determined whether the electric energy pulse correlation coefficient is greater than a preset threshold, and the faulty electric energy meter in the substation is determined based on the judgment result.

[0037] In this way, fault location is performed based on the determined topological structure, which can quickly and accurately locate the faulty electric energy meter in the platform area, thereby achieving rapid response and processing of the fault.

[0038] In a second aspect, the present application provides a fault location system based on substation topology identification, comprising: an acquisition module, a first determination module, a second determination module, and a fault location module;

[0039] The acquisition module is used to acquire the first pulse number of the main meter in the meter box, and acquire the second pulse number and electrical quantity data of several sub-meters in the meter box;

[0040] The first determination module is configured to input the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determine the first substation topology structure based on each of the attribution relationship coefficients, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table;

[0041] The second determination module is configured to calculate a target correlation coefficient between users based on each of the electrical quantity data, and eliminate first user pairs whose target correlation coefficients do not meet a preset correlation threshold to obtain a plurality of second user pairs, calculate a target DTW distance for each of the second user pairs, and use the second user pair with the largest target DTW distance as a third user pair, and use the third user pair as an initial cluster center for iterative optimization until a clustering termination condition is met, and determine a second substation topology structure according to the output target clustering result;

[0042] The fault location module is used to compare the first substation topology structure and the second substation topology structure. If the two are the same, the target substation topology structure is determined, and the fault is located based on the target substation topology structure to determine the faulty electric energy meter in the substation.

[0043] The embodiment of the present application can collect the electric energy metering information and operating status data of each electric energy meter in the meter box by obtaining the first pulse number of the total meter and the second pulse number and electric quantity data of the sub-meter, thereby providing the necessary information support for the subsequent topological structure identification using different methods; the collected pulse number is processed and analyzed by using a preset topological analysis model, and key information reflecting the topological relationship of the substation area can be extracted from the pulse number, and the topological structure of the substation area can be preliminarily established; by calculating the target correlation coefficient between users, user pairs with high correlation can be screened out, and the user set that may be in the same substation area can be preliminarily determined; by eliminating user pairs that do not meet the correlation threshold, the interference of irrelevant data is reduced, making the analysis more targeted. , then calculate the target DTW distance and determine the initial cluster center for iterative optimization until the cluster termination condition is met. This series of operations can make full use of the time series characteristics of the electrical quantity data, and mine the similarities and differences in electrical characteristics between users, so as to more accurately divide the substation topology structure, and can identify the substation topology from another dimension, complement and verify the topology identification results based on the number of pulses, and further improve the accuracy and reliability of topology identification; by comparing the two topological structures, the credibility and accuracy of the topological identification results are greatly improved; fault location is performed based on the determined topological structure, which can quickly and accurately locate the faulty electric energy meter in the substation, thereby achieving rapid response and processing of the fault. Compared with the existing technology, the present application can improve the accuracy of substation topology identification, and thus accurately locate the substation fault point.

[0044] In the third aspect, the present application also provides a terminal device, including: one or more processors; a memory coupled to the processor, for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the fault location method based on substation topology identification as described in the present application.

[0045] In a fourth aspect, the present application further provides a terminal device, a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the fault location method based on substation topology identification as described in the present application is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of an embodiment of a fault location method based on substation topology identification provided by the present application;

[0047] Figure 2 This is a schematic diagram of the interaction between the smart measurement switch and the electric energy meter provided by this application;

[0048] Figure 3 It is a flowchart of steps S501 to S504 provided by this application;

[0049] Figure 4 It is a schematic diagram of the dynamic time bending path of the electrical quantity time series provided by this application;

[0050] Figure 5 It is a schematic diagram of the first area topology structure and the second area topology structure provided in this application;

[0051] Figure 6 This is a structural diagram of an embodiment of a fault location system based on substation topology identification provided by the present application;

[0052] Figure 7 This is a schematic diagram of the hardware structure of the terminal device provided in this application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0055] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0057] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0058] Next, the nouns involved in this application are analyzed:

[0059] The "transformer-user" relationship refers to the correspondence between distribution transformers (transformers) and the electricity users (users) within their power supply range in the operation and management of distribution networks. It clearly defines which users a distribution transformer directly provides power to and is fundamental information about the distribution network topology.

[0060] Electricity meter Bluetooth: A Bluetooth communication module is added to the traditional smart electricity meter, which is mainly used for short-range wireless transmission, enabling mutual communication between several nodes with high communication efficiency.

[0061] Based on this, the embodiments of the present application provide a fault location method, system, device and medium based on substation topology identification, which can improve the accuracy of substation topology identification and thus accurately locate the substation fault point.

[0062] The embodiments of the present application provide a fault location method, system, device and medium based on substation topology identification, which are specifically illustrated through the following embodiments. First, the fault location method based on substation topology identification in the embodiments of the present application is described.

[0063] The fault location method based on substation topology identification provided in the embodiment of the present application relates to the field of electricity metering. The fault location method based on substation topology identification provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a fault location method based on substation topology identification, etc., but is not limited to the above forms.

[0064] The present application can also be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0065] Example 1

[0066] Please refer to Figure 1, Figure 1 This is a flow chart of an embodiment of a fault location method based on substation topology identification provided by the present application, including steps S101 to S104;

[0067] Step S101, obtaining the first pulse number of the master meter in the meter box, and obtaining the second pulse number and electrical quantity data of several sub-meters in the meter box;

[0068] In some embodiments, the second pulse number and electrical quantity data of each sub-meter and the first pulse number of the total meter are synchronously obtained through wireless connection through the Bluetooth module (or rail meter) of the intelligent measuring switch, wherein the electrical quantity data includes voltage data, current data and power data.

[0069] In some embodiments, the schematic diagram of the interaction between the smart measurement switch and the electric energy meter is as follows: Figure 2 As shown, specifically, first, an intelligent measuring switch is installed in the meter box, with a built-in Bluetooth module as the communication master device, and the main meter and each sub-meter (user electricity meter) are Bluetooth slave devices. The intelligent measuring switch establishes a wireless communication connection with the main meter and each sub-meter through the Bluetooth module; then, the intelligent measuring switch synchronously sends data collection instructions to the main meter and each sub-meter. After receiving the instructions, the main meter and each sub-meter simultaneously send the number of electric energy pulses and instantaneous voltage, current and other electrical quantity data; finally, after receiving the data from the main meter, the intelligent measuring switch uses it as the total reference value of the meter box, that is, it obtains the first pulse number of the main meter. For each sub-meter, the intelligent measuring switch receives the number of electric energy pulses sent by it through the Bluetooth module, that is, it obtains the second pulse number of the sub-meter. At the same time, it also receives the voltage and current sent by the sub-meter, and synchronizes according to the obtained voltage and current data, and calculates the power at the same time, thereby obtaining the electrical quantity data.

[0070] It should be noted that the smart measuring switch is generally installed at the incoming line of the low-voltage meter box, replacing the existing ordinary incoming line switch. It is a highly integrated intelligent measuring device that, while maintaining the original protection functions of the molded case circuit breaker of the meter box, integrates the functions of a three-phase smart energy meter, an electricity consumption information collector, and an intelligent monitoring terminal for the meter box. It can monitor voltage, current, power, power factor, energy consumption and other data in real time, and remotely control the switch on and off via an app, cloud platform, or local network (such as Wi-Fi, Bluetooth, Zigbee). It supports cross-metering, file management, topology recognition and other operations.

[0071] Step S102: Inputting the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determining a first substation topology structure based on each attribution relationship coefficient, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table;

[0072] In some embodiments, before inputting the first pulse number and each second pulse number into a preset topological analysis model for solution, it also includes: performing data synchronization, data preprocessing and smoothing on the first pulse number and each second pulse number, specifically: first, using the synchronization mechanism of Bluetooth communication, the collected first pulse number of the total table and each second pulse number of the sub-table are time synchronized to ensure the periodicity and consistency of the data; then, the synchronized first pulse number and each second pulse number of the sub-table are data preprocessed by filtering, denoising and calibration to obtain the processed first pulse number and each second pulse number.

[0073] In some embodiments, since the meter box topology structure and the power supply and consumption relationship are the physical basis of the topological analysis model, based on the relationship that the total meter electric energy increment in the same sub-meter area is approximately equal to the sum of the increments of each sub-meter, combined with the pulse constants of the total meter and the sub-meter, an equation between the total meter and the sub-meter electric energy pulse numbers is established, and by accumulating data from multiple cycles, a group of equations, namely the topological analysis model, is formed, and the topological analysis model is then solved to determine the ownership relationship between the sub-meter and the total meter.

[0074] It should be noted that the specific construction process of the topology analysis model is as follows: First, since the user energy pulses and the total meter energy pulses in the same substation are strongly synchronized, the user pulses and the total meter pulses in different substations have no significant correlation. In other words, within the same substation, the total meter energy increment should be approximately equal to the sum of the increments of each sub-meter. Therefore, it can be determined that:

[0075] ΔP 总 ≈∑ i∈台区 ΔP 分,i #(1), where ΔP 总 Indicates the energy increment of the total meter within the time window, ΔP 分,i represents the energy increment of the ith sub-meter in the same substation within the time window; then, combined with the pulse constant of the total meter and each user's sub-meter (number of energy pulses = pulse constant × consumed energy), formula (1) can be rewritten as:

[0076]

[0077] Where, ΔN 总 Indicates the first pulse number of the total meter in the time window, ΔP 分,i Indicates the number of second pulses of the ith sub-meter in the same area within the time window, c 总 Indicates the pulse constant of the total table, c 分,i Represents the pulse constant of the ith sub-meter in the same substation. Then, for the main meter and sub-meters with undetermined topological relationships, after the meter box accumulates data for n cycles, a system of n equations can be obtained, which is the determined topological analysis model.

[0078] In some embodiments, the calculation formula of the topology analysis model is specifically:

[0079]

[0080] Where, e j represents the attribution relationship coefficient between the jth sub-table and the current area master table, where j = 1, 2, ..., p; The measurement values ​​of different time series of the same sub-table; is the measurement value of different time series of the total table; n is the number of data sets to be collected; ΔP j is the second pulse number of all sub-tables whose topological relationships are to be determined, where j = 1, 2, ..., p; c p is the pulse constant of all sub-meters whose topological relationships are to be determined; ΔN k is the first pulse number in the total table of the area, c k is the pulse constant of the total table of the substations, where k is the number of corresponding different substations.

[0081] It should be noted that the numbers in the brackets of the topological analysis model represent different time data collected in time series under the same electricity meter, that is, a total of n groups of data must be collected before the equation group can be solved, where the number of equations to be solved is the same as the number of substations.

[0082] In some embodiments, the first pulse number and each second pulse number are input into a preset topological analysis model for solution, and the attribution coefficient e can be obtained. j , where j = 1, 2, ..., p, that is, solving e1 to e p The attribution relationship coefficient between each sub-table and the current master table, wherein the system of equations can be solved by using the least squares method or linear regression, which is not limited in this application.

[0083] In some embodiments, the first area topology structure is determined based on each of the ownership relationship coefficients, specifically: when the ownership relationship coefficient e is obtained j Then, the threshold is set to 0.5, if the attribution coefficient e j If the ownership coefficient e is greater than 0.5, the user (i.e., the sub-table) is considered to be under the current master table. j If it is less than 0.5, the user (that is, the sub-table) is considered not to belong to the current master table. The relevant formula is: In the formula, 1 represents the relationship, and 0 represents the relationship. After determining whether each sub-table belongs to the substation, the topological structure of the first substation can be determined.

[0084] It should be noted that in order to more accurately determine the topological structure of the first substation, within the same substation, the topological analysis model can also be solved based on the number of second pulses in different phases of the main meter and the number of first pulses of different single-phase users to achieve the distinction between different phases. For example, assuming that the pulse constants of the main meter and the sub-meter are the same, within the time window, there are 100 pulses in the main meter phase A, 80 pulses in the sub-meter A, 20 pulses in the sub-meter B, and 30 pulses in the sub-meter C. It can be judged that the sub-meters A and B are in the main meter phase A.

[0085] Step S103: Based on the electrical quantity data, a target correlation coefficient between users is calculated, and first user pairs whose target correlation coefficients do not meet a preset correlation threshold are eliminated to obtain a plurality of second user pairs. A target DTW distance is calculated for each of the second user pairs, and the second user pair with the largest target DTW distance is used as a third user pair. The third user pair is used as the initial cluster center for iterative optimization until a clustering termination condition is met, and a second substation topology is determined based on the output target clustering results.

[0086] In some embodiments, before calculating the target correlation coefficient between users based on the electrical quantity data, the electrical quantity data is normalized to eliminate dimensional differences.

[0087] It should be noted that since users in the same substation have a short electrical distance, their fluctuation patterns are very similar, while users in different substations have a long electrical distance and poor fluctuation similarity. Therefore, the clustering algorithm is used as an example to illustrate how to determine the topological structure of the second substation.

[0088] In some embodiments, the electrical quantity data includes voltage data, current data, and power data, and the target correlation coefficient between users is calculated based on each of the electrical quantity data, specifically: a first correlation coefficient is calculated for the voltage data between users; a second correlation coefficient is calculated for the current data between users; a third correlation coefficient is calculated for the power data between users, wherein the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all Pearson correlation coefficients; based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient, the target correlation coefficient between users is determined. Specifically, first, the correlation between the voltage, current, and power time series between each two users (sub-meters) is calculated by the Pearson coefficient, wherein the calculation formula of the Pearson coefficient is: In the formula, x and y are the electrical quantity data (including voltage data, current data and power data) of the two users (sub-meters) to be calculated, and n is the number of collection groups of electricity consumption data. The first correlation coefficient r between users in the platform area can be calculated by the above formula V , the second correlation coefficient r Iand the third correlation coefficient r P (where V represents voltage, I represents current, and P represents power); then, the first correlation coefficient r between users is calculated. V , the second correlation coefficient r I and the third correlation coefficient r P Take the average value to get the target correlation coefficient between users The relevant formula is:

[0089] It should be noted that the correlation coefficient between user electricity consumption data can be calculated from the data of two groups of users. The correlation coefficient is distributed between -1 and 1. The closer the correlation coefficient is to 0, the lower the correlation between the two groups of data. The closer the correlation coefficient is to -1, it means that the two groups of data are negatively correlated. The closer the correlation coefficient is to 1, the two groups of data are positively correlated (that is, the higher the correlation of user data, the larger the correlation coefficient).

[0090] In some embodiments, the first user pair whose target correlation coefficient does not meet the preset correlation threshold is eliminated, specifically: when determining the target correlation coefficient After that, by setting a preset correlation threshold (for example, it can be set to 0.8), when the target correlation coefficient between two users (between sub-tables) is If the correlation is less than the preset threshold, it means that the correlation is low and can be eliminated. This method can quickly screen out highly correlated user pairs, narrow the scope of subsequent calculations, eliminate obviously irrelevant users, reduce the amount of DTW calculations, and obtain several second user pairs with high correlation.

[0091] In this way, by calculating the target correlation coefficient between users, we can screen out user pairs with high correlation and preliminarily determine the set of users that may be in the same area. By eliminating user pairs that do not meet the correlation threshold, we can reduce the interference of irrelevant data and make the analysis more targeted.

[0092] In some embodiments, the electrical quantity data includes voltage data, current data, and power data. The step of calculating the target DTW distance of each second user pair may include, but is not limited to, steps S501 to S504. Figure 3 As shown:

[0093] Step S501: constructing a corresponding first distance matrix, a second distance matrix, and a third distance matrix for the voltage data, the current data, and the power data respectively;

[0094] In some embodiments, the absolute distance between each point in the time series of two users is calculated for the voltage, current, and power data, and a first distance matrix (voltage), a second distance matrix (current), and a third distance matrix (power) are constructed. The relevant calculation formula is: d(ws )=d(i,j)=|x i -y j |, where element w s is the coordinate of the sth point on the path, that is, w s =(i,j), representing x in the smart meter electrical quantity sequence X i and y in another smart meter Y i Correspondingly, where i is 1, 2, ..., n, and j is 1, 2, ..., m. n and m represent the number of data in the time series of two different users' electrical quantities.

[0095] It should be noted that in order to facilitate the subsequent calculation of the optimal path, it is necessary to construct an nxm matrix D, where n and m represent the number of data in the two electrical quantity time series. The dynamic time bending paths of the electrical quantity time series X and Y are as follows Figure 4 As shown in the figure, the path composed of gray diagonal squares represents the corresponding relationship between the time series X and Y of the electrical quantity of the smart meter. It is only one of many paths, that is, each element D[i][j] in the matrix D represents x i and y j absolute distance.

[0096] Step S503: Calculate a first cumulative matrix based on the first distance matrix, calculate a second cumulative matrix based on the second distance matrix, and calculate a third cumulative matrix based on the third distance matrix;

[0097] In some embodiments, for each distance matrix (including the first distance matrix, the second distance matrix, and the third distance matrix), the boundary value D[0][0]=d(0,0) is first initialized. (first column), (first row), and generate the cumulative matrix through the dynamic programming recursive formula

[0098] It should be noted that in order to find the best path in the cumulative matrix, since there are multiple paths w, all possibilities of w are combined into a path space W, and there is an optimal path in W that makes The minimum DTW distance between time series X and Y is: From (x1,y1) to (x n ,y m )'s shortest path needs to satisfy: when the calculation reaches the (x i ,y j ), we need to find the shortest path of (x i ,y j-1 )、(x i-1 ,y j-1 )、(x i-1 ,yj )The third point to (x i ,y j ) to determine the DTW distance.

[0099] Step S503: for each accumulation matrix, find the optimal path with the smallest accumulated distance, and calculate the accumulated distance of the optimal path to obtain a first DTW distance, a second DTW distance, and a third DTW distance;

[0100] In some embodiments, starting from the starting point (1,1) of each accumulation matrix and ending at the end point (n,m), and under the premise of ensuring monotonicity (the path can only move right, upward, or right-upward (to ensure time order)) and continuity (the path cannot skip any point), find the path with the smallest cumulative distance, that is, the optimal path. The end point value of the path is the DTW distance of the single electrical quantity, that is, the cumulative distance of the optimal path, DTW(X i ,Y j )=D[i][j], and according to the situation of the station area, the DTW distance is calculated according to the voltage, current and power between users respectively, thereby obtaining the first DTW distance (voltage), the second DTW distance (current) and the third DTW distance (power).

[0101] It should be noted that the DTW distance is the goal of dynamic programming. There are multiple paths w. All possibilities of w are combined into a path space W to find an optimal path that makes the DTW distance between time series X and Y the shortest. This is the optimal path.

[0102] Step S504 : performing weighted fusion on the first DTW distance, the second DTW distance, and the third DTW distance according to preset weights to obtain a target DTW distance for each second user pair.

[0103] In some embodiments, weights are assigned according to the importance of electrical quantities (e.g., voltage 0.4, current 0.3, power 0.3), and the comprehensive DTW distance is calculated to obtain the target DTW distance of each second user pair. The relevant calculation formula is: Where, X i ,Y j Represents two user data of the same electrical quantity, with weight w k It can be allocated according to the importance of electrical quantity characteristics such as voltage, current, and power.

[0104] It's important to note that the DTW (Dynamic Time Warping) algorithm is used to measure the similarity between two time series. Its main principle is to map one time series onto another through a nonlinear mapping relationship, thereby measuring the similarity between the two time series. This mapping relationship can be solved using a dynamic programming algorithm to find the optimal path that minimizes the distance between the two time series.

[0105] By calculating the target DTW distance, we can more accurately measure the similarity of their electrical characteristics, making it easier to subsequently build a substation topology that is more in line with actual conditions.

[0106] Furthermore, the second user pair with the largest target DTW distance is used as the third user pair, and the third user pair is used as the initial cluster center for iterative optimization until the clustering termination condition is met, and the second substation topology structure is determined according to the output target clustering result, including: using the second user pair with the largest target DTW distance as the third user pair, and the third user pair as the initial cluster center, and calculating the fourth DTW distance between each user and the initial cluster center, and assigning each user to the initial cluster center closest to the fourth DTW distance; for each user assigned to each initial cluster center, calculating the mean sequence of the electrical quantity data, and iteratively updating the second cluster center based on the mean sequence, until the updated second cluster center meets the preset clustering termination condition, outputting the updated target clustering result, and determining the second substation topology structure based on the target clustering result. Specifically, since the core of cluster analysis is to determine the number of clusters K and clustering points, the N low-voltage distribution area users are divided into K sets, where the number of clusters k is determined by the number of distribution transformers M, and the process of determining the clustering points is as follows: first, the two users with the largest DTW distance (that is, the third user pair) are used as the two initial clustering points. Then, the comprehensive DTW distance of each user to the two initial clustering centers is calculated, and the user is assigned to the initial clustering center closest to the user; then, for each user in the initial clustering center, the mean sequence of its electrical quantity time series (voltage data, current data, and power data) is calculated as the new clustering center, that is, the second clustering center is updated until any termination condition is met (the clustering center no longer changes or the preset maximum number of iterations is reached). At this time, the users can be divided into K sets (K = the number of distribution transformers M), and the second area topology structure is constructed based on the target clustering results (wherein the matrix rows represent the areas (clustering centers), the columns represent users, if the user belongs to a certain area, the corresponding position is marked as 1, otherwise it is 0).

[0107] It should be noted that to further accurately determine the second substation topology, the electrical quantity data of single-phase users has a high similarity with the electrical quantity data of the corresponding phase of the transformer that supplies them, resulting in a small DTW distance. However, the similarity with the transformer phase voltage curves of different phases is low, resulting in a large DTW distance. Based on this principle, the DTW distance between single-phase users and the three-phase voltage of the transformer is calculated separately. The phase with the smallest DTW distance is the phase to which the single-phase user belongs, thus more accurately determining the second substation topology.

[0108] In this way, by determining the initial cluster center and performing iterative optimization until the cluster termination conditions are met, this series of operations can make full use of the time series characteristics of electrical quantity data, explore the similarities and differences in electrical characteristics between users, and thus more accurately divide the substation topology structure. The substation topology can be identified from another dimension, complementing and verifying the topology identification results based on the number of pulses, further improving the accuracy and reliability of topology identification.

[0109] It should be noted that the second substation topology structure can also be obtained through a series of heuristic optimization algorithms, least squares, regression optimization and other algorithms.

[0110] Step S104: compare the first substation area topology structure and the second substation area topology structure. If the two are the same, determine the target substation area topology structure, and locate the fault based on the target substation area topology structure to determine the faulty electric energy meter in the substation area.

[0111] It should be noted that the first and the second do not represent the order of precedence, but can be understood as names. The topology structure of the first substation is the pulse topology adjacency matrix determined by the electric energy pulse analysis, while the topology structure of the second substation is the electrical quantity topology adjacency matrix determined by big data analysis.

[0112] Furthermore, the first station area topology structure and the second station area topology structure are compared. If the two are the same, the target station area topology structure is determined. Specifically, the first station area topology structure and the second station area topology structure are compared element by element to determine the difference matrix; if the difference of all elements in the difference matrix is ​​0, it is determined that the two are the same, and the target station area topology structure is determined. Specifically, first, the values ​​of the corresponding positions in the two matrices (the first station area topology structure and the second station area topology structure) are subtracted by the element-level comparison method. If the difference of all positions is 0 (A ij -B ij≡0, i is the number of substations, j is the number of users), then the topological structures identified by the two methods are judged to be completely consistent. If there is a non-zero difference, it indicates that there is a disagreement in the topological identification of the corresponding branch, and more data needs to be further collected for analysis to obtain the corresponding matrix until the pulse topology adjacency matrix and the electrical quantity topology adjacency matrix reach consistency.

[0113] In some embodiments, if the topologies of the two substations are different, a multi-cycle weighted average algorithm is triggered to smooth the electrical quantity data, the first pulse number, and the second pulse number, wherein the relevant formula is: Where, is the electrical quantity data after multi-cycle smoothing in the i-th cycle, the first pulse number and the second pulse number, w k is the weight of the kth period, x i+k is the historical data of the i+kth cycle, M is the cumulative number of cycles, and pulse analysis and big data analysis are re-performed until the obtained substation topology results are consistent.

[0114] For example, please refer to Figure 5 , Figure 5 The diagrams of the first area topology (a) and the second area topology (b) are shown. Both the first and second area topologies include table 1 (T1), table 2 (T2), user 1 (U1), user 2 (U2), user 3 (U3), user 4 (U4), and user 5 (U6). A value of 1 indicates that the element belongs to the table, and a value of 0 indicates that the element does not belong to the table. Comparing the difference positions of the two matrices reveals two differences: Difference 1: (T1, U2) = 0 in the first area topology and (T1, U2) = 1 in the second area topology. Difference 2: (T2, U2) = 1 in the first area topology and (T2, U2) = 0 in the second area topology. Therefore, the topologies obtained by the two methods are different, so further data collection and analysis are needed to determine the corresponding matrices until the first and second area topologies are consistent.

[0115] In some embodiments, if the first area topology structure and the second area topology structure are exactly the same, it means that both area topologies are accurate, and any one of them can be selected as the target area topology structure.

[0116] In this way, by comparing the two topological structures, the credibility and accuracy of the topological recognition results are greatly improved.

[0117] In some embodiments, the fault location is performed based on the target area topology structure to determine the faulty electric energy meter in the area, including: determining the third pulse number of each electric meter in the target area topology structure, wherein the electric meters include a main meter and a sub-meter; based on the third pulse number, calculating the electric energy pulse correlation coefficient between each electric meter using the Pearson correlation coefficient, determining whether the electric energy pulse correlation coefficient is greater than a preset threshold, and determining the faulty electric energy meter in the area based on the judgment result. Specifically, after determining the target area topology structure, it is necessary to calculate the Pearson correlation coefficient of the electric energy pulses of each sub-meter within the target area topology structure using the third pulse number corresponding to each sub-meter. Subsequently, two electric energy meters may be connected to different households due to wiring errors, and their electric energy pulses will accumulate or increase synchronously, thus showing a high correlation. Therefore, it is necessary to determine whether the electric energy pulse correlation coefficient is greater than a preset threshold (e.g., greater than 0.95). It can be determined that the electric meters have an abnormal correlation, and it can be determined that one or both of the two electric energy meters are faulty.

[0118] It should be noted that the preset threshold can be set based on historical data or experimental verification, and this application does not impose any restrictions.

[0119] In this way, fault location is performed based on the determined topological structure, which can quickly and accurately locate the faulty electric energy meter in the platform area, thereby achieving rapid response and processing of the fault.

[0120] The embodiment of the present application can collect the electric energy metering information and operating status data of each electric energy meter in the meter box by obtaining the first pulse number of the total meter and the second pulse number and electric quantity data of the sub-meter, thereby providing the necessary information support for the subsequent topological structure identification using different methods; the collected pulse number is processed and analyzed by using a preset topological analysis model, and key information reflecting the topological relationship of the substation area can be extracted from the pulse number, and the topological structure of the substation area can be preliminarily established; by calculating the target correlation coefficient between users, user pairs with high correlation can be screened out, and the user set that may be in the same substation area can be preliminarily determined; by eliminating user pairs that do not meet the correlation threshold, the interference of irrelevant data is reduced, making the analysis more targeted. , then calculate the target DTW distance and determine the initial cluster center for iterative optimization until the cluster termination condition is met. This series of operations can make full use of the time series characteristics of the electrical quantity data, and mine the similarities and differences in electrical characteristics between users, so as to more accurately divide the substation topology structure, and can identify the substation topology from another dimension, complement and verify the topology identification results based on the number of pulses, and further improve the accuracy and reliability of topology identification; by comparing the two topological structures, the credibility and accuracy of the topological identification results are greatly improved; fault location is performed based on the determined topological structure, which can quickly and accurately locate the faulty electric energy meter in the substation, thereby achieving rapid response and processing of the fault. Compared with the existing technology, the present application can improve the accuracy of substation topology identification, and thus accurately locate the substation fault point.

[0121] Example 2

[0122] Please refer to Figure 6 , Figure 6 This is a structural diagram of an embodiment of a fault location system based on substation topology identification provided by the present application, comprising: an acquisition module 100, a first determination module 200, a second determination module 300 and a fault location module 400;

[0123] The acquisition module 100 is used to acquire the first pulse number of the main meter in the meter box, and acquire the second pulse number and electrical quantity data of several sub-meters in the meter box;

[0124] The first determination module 200 is configured to input the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determine the first substation topology structure based on each attribution relationship coefficient, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table;

[0125] The second determination module 300 is configured to calculate a target correlation coefficient between users based on the electrical quantity data, eliminate first user pairs whose target correlation coefficients do not meet a preset correlation threshold, obtain a plurality of second user pairs, calculate a target DTW distance for each of the second user pairs, and use the second user pair with the largest target DTW distance as a third user pair, and use the third user pair as an initial cluster center for iterative optimization until a clustering termination condition is met, and determine a second substation topology structure based on the output target clustering result;

[0126] The fault location module 400 is used to compare the first substation topology structure and the second substation topology structure. If the two are the same, the target substation topology structure is determined, and the fault is located based on the target substation topology structure to determine the faulty electric energy meter in the substation.

[0127] The information interaction, execution process, etc. between the modules in the above-mentioned fault location system based on substation topology identification are based on the same concept as the embodiment of the fault location method based on substation topology identification in the first aspect of the present invention, and the technical effects achieved are basically the same. For specific contents, please refer to the description in the first embodiment of the method of the present invention, and will not be repeated here.

[0128] The apparatus embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these elements may be selected based on actual needs to achieve the objectives of the methods of this embodiment.

[0129] See also Figure 7 , Figure 7 The hardware structure of a terminal device according to another embodiment is shown. The terminal device includes:

[0130] The processor 701 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0131] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called by the processor 701 to execute the large model-based dialogue risk assessment method of the embodiments of this application.

[0132] Input / output interface 703, used to implement information input and output;

[0133] Communication interface 704, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0134] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0135] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0136] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the fault location method based on substation topology identification as described in the first embodiment above is implemented.

[0137] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-monitorable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0138] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0139] It is particularly pointed out that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. A fault location method based on area topology identification, characterized in that: include: Obtain the first pulse number of the main meter in the meter box, and obtain the second pulse number and electrical quantity data of several sub-meters in the meter box; Inputting the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determining a first substation topology structure based on each of the attribution relationship coefficients, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table; Based on the electrical quantity data, a target correlation coefficient between users is calculated, and first user pairs whose target correlation coefficients do not meet a preset correlation threshold are eliminated to obtain a plurality of second user pairs. A target DTW distance is calculated for each of the second user pairs, and the second user pair with the largest target DTW distance is used as a third user pair. The third user pair is used as an initial cluster center for iterative optimization until a clustering termination condition is met, and a second substation topology structure is determined according to the output target clustering result. The first substation area topology structure and the second substation area topology structure are compared. If the two are the same, a target substation area topology structure is determined, and fault location is performed based on the target substation area topology structure to determine the faulty electric energy meter in the substation area.

2. The fault location method based on substation topology identification according to claim 1, characterized in that: The calculation formula of the topology analysis model is specifically: Where, e j represents the attribution relationship coefficient between the jth sub-table and the current area master table, where j = 1, 2, ..., p; The measurement values ​​of different time series of the same sub-table; is the measurement value of different time series of the total table; n is the number of data sets to be collected; ΔP j is the second pulse number of all sub-tables whose topological relationships are to be determined, where j = 1, 2, ..., p; c p is the pulse constant of all sub-meters whose topological relationships are to be determined; ΔN k is the first pulse number in the total table of the area, c k is the pulse constant of the total table of the substations, where k is the number of corresponding different substations.

3. The fault location method based on substation topology identification according to claim 1, characterized in that: in, The electrical quantity data includes voltage data, current data, and power data. The target DTW distance of each second user pair is calculated as follows: Constructing a corresponding first distance matrix, a second distance matrix, and a third distance matrix for the voltage data, the current data, and the power data respectively; Calculating a first cumulative matrix based on the first distance matrix, calculating a second cumulative matrix based on the second distance matrix, and calculating a third cumulative matrix based on the third distance matrix; For each accumulation matrix, find the optimal path with the smallest cumulative distance, and calculate the cumulative distance of the optimal path to obtain a first DTW distance, a second DTW distance, and a third DTW distance; The first DTW distance, the second DTW distance, and the third DTW distance are weightedly fused according to preset weights to obtain a target DTW distance for each second user pair.

4. The fault location method based on substation topology identification according to claim 3, characterized in that: The target correlation coefficient between users is calculated based on the electrical quantity data, specifically: calculating a first correlation coefficient for the voltage data between users; calculating a second correlation coefficient for the current data between users; Calculating a third correlation coefficient for the power data between users, wherein the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all Pearson correlation coefficients; A target correlation coefficient between users is determined based on the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

5. The fault location method based on substation topology identification according to claim 1, characterized in that: The second user pair with the largest target DTW distance is used as the third user pair, and the third user pair is used as the initial cluster center for iterative optimization until the clustering termination condition is met, and the second station area topology structure is determined according to the output target clustering result, specifically: The second user pair with the largest target DTW distance is used as the third user pair, and the third user pair is used as the initial cluster center. The fourth DTW distance between each user and the initial cluster center is calculated, and each user is assigned to the initial cluster center with the closest fourth DTW distance. For each user assigned to each of the initial cluster centers, a mean sequence of the electrical quantity data is calculated, and a second cluster center is iteratively updated based on the mean sequence until the updated second cluster center meets a preset clustering termination condition, an updated target clustering result is output, and a second substation topology structure is determined based on the target clustering result.

6. The fault location method based on substation topology identification according to claim 1, characterized in that: The first area topology structure and the second area topology structure are compared, and if they are the same, a target area topology structure is determined, specifically: Determine a difference matrix by comparing the first station area topology structure and the second station area topology structure element by element; If the difference values ​​of all elements in the difference matrix are 0, it is determined that the two are the same, and the target area topology is determined.

7. The fault location method based on substation topology identification according to claim 1, characterized in that: The fault location is performed based on the target area topology structure to determine the faulty electric energy meter in the area, specifically: Determining the third pulse number of each electric meter in the target area topology, wherein the electric meters include a main meter and sub-meters; Based on the third pulse number, the Pearson correlation coefficient is used to calculate the electric energy pulse correlation coefficient between each meter, and it is determined whether the electric energy pulse correlation coefficient is greater than a preset threshold, and the faulty electric energy meter in the substation is determined based on the judgment result.

8. A fault location system based on substation topology identification, characterized in that: include: an acquisition module, a first determination module, a second determination module, and a fault location module; The acquisition module is used to acquire the first pulse number of the main meter in the meter box, and acquire the second pulse number and electrical quantity data of several sub-meters in the meter box; The first determination module is configured to input the first pulse number and each second pulse number into a preset topology analysis model for solution to obtain a plurality of attribution relationship coefficients, and determine the first substation topology structure based on each of the attribution relationship coefficients, wherein the attribution relationship coefficient is a coefficient of each sub-table belonging to the master table; The second determination module is configured to calculate a target correlation coefficient between users based on each of the electrical quantity data, and eliminate first user pairs whose target correlation coefficients do not meet a preset correlation threshold to obtain a plurality of second user pairs, calculate a target DTW distance for each of the second user pairs, and use the second user pair with the largest target DTW distance as a third user pair, and use the third user pair as an initial cluster center for iterative optimization until a clustering termination condition is met, and determine a second substation topology structure according to the output target clustering result; The fault location module is used to compare the first substation topology structure and the second substation topology structure. If the two are the same, the target substation topology structure is determined, and the fault is located based on the target substation topology structure to determine the faulty electric energy meter in the substation.

9. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fault location method based on substation topology identification according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault location method based on substation topology identification according to any one of claims 1 to 7 is implemented.

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