Low-voltage distribution network fault prediction method, system and device based on topology identification and medium
By calculating the Pearson correlation coefficient in the low-voltage distribution network and building a comparison matrix, judging the station area to which the user belongs, and establishing a fault prediction model, the problem of low topological relationship identification efficiency in the low-voltage distribution network is solved, real-time fault prediction and accuracy are achieved Identification improves the efficiency of emergency repair of faults.
Patent Information
- Application Number
- CN202411517825.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-01-07
AI Technical Summary
The topological relationship identification efficiency in existing low-voltage distribution networks is low, which leads to time-consuming and labor-intensive failure prediction and is prone to misjudgment, affecting power theft warning, line loss analysis and demand response.
By obtaining the meter data from the low-voltage distribution network, calculating the Pearson correlation coefficient, building a comparison matrix, determining whether the user belongs to the current station area, establishing a fault prediction model, and real-time fault prediction.
It improves the fault identification efficiency, ensures the accuracy and identification speed of household change relationships, and accelerates the active emergency repair of low-voltage distribution network faults.
Smart Images
Figure CN119269962A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power system automation control, and in particular to a method, system, equipment and medium for predicting low-voltage distribution network faults based on topology identification. Background Art
[0002] With the continuous development of intelligent distribution network construction, the morphological characteristics of low-voltage distribution networks are becoming increasingly complex. The correct transformer-branch-user topological relationship of the low-voltage distribution network is of great significance for power theft warning, line loss analysis, fault troubleshooting and demand response. At present, the identification of topological relationships is mostly focused on medium and high voltage power grids, and the topological identification of medium and high voltage power grids is mostly based on the measurement information of equipment. However, due to the large number of low-voltage distribution network areas, the rapid changes in power supply methods, and the mixed construction of new and old communities in the low-voltage distribution area, the lines are complicated, and the user topological relationship records in the distribution area are often confused or missing, which makes it easy for low-voltage distribution areas to fail.
[0003] Traditionally, the topology relationship identification of substations is to check the topology relationship of substations by checking the power outage range when manual repair is required. This fault prediction method is not only time-consuming and labor-intensive, but also has low topology identification efficiency. Summary of the invention
[0004] The present invention aims at the problem that the existing topological relationship identification of substations is to check the topological relationship of substations through the power outage range when repairing faults manually, and the fault prediction method is not only time-consuming and labor-intensive, but also has low topological identification efficiency. A low-voltage distribution network fault prediction method based on topological identification is proposed. Firstly, low-voltage distribution substation meter data and user meter data are obtained from the low-voltage distribution network, and the meter data set is obtained by pre-processing. Secondly, according to the meter data set, the Pearson correlation coefficient between the low-voltage distribution substation meter data and the user meter data is calculated to construct a comparison matrix. Then, according to the comparison matrix, it is judged whether the user belongs to the current low-voltage distribution substation to obtain the low-voltage distribution network topological structure. Finally, a fault prediction model is established according to the low-voltage distribution network topological structure to obtain the current user meter failure rate. The fault identification efficiency is improved, and real-time fault prediction is realized.
[0005] The specific implementation contents of the present invention are as follows: A low voltage distribution network fault prediction method based on topology recognition specifically comprises the following steps: Step S1: acquiring low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and preprocessing to obtain a meter data set; Step S2: Based on the meter data set, calculate the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data, and construct a comparison matrix; Step S3: judging whether the user belongs to the current low-voltage distribution network area according to the comparison matrix, and obtaining the low-voltage distribution network topology structure; Step S4: Establish a fault prediction model according to the low-voltage distribution network topology to obtain the failure rate of the front user's electric meter.
[0006] In order to better implement the present invention, further, the step S1 specifically includes the following steps: Step S11: acquiring low-voltage distribution station area meter data and user meter data from the low-voltage distribution network according to the set collection cycle and collection point; Step S12: sorting the low-voltage distribution station area meter data and the user meter data, and calculating the average value of the low-voltage distribution station area meter and the average value of the user meter; Step S13: taking the average value of the electric meters in the low-voltage distribution station area and the average value of the electric meters of the users as the time window value to obtain the electric meter data set.
[0007] In order to better implement the present invention, further, the step S2 specifically includes the following steps: Step S21: Calculate the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data according to the meter data set, and construct a Pearson correlation matrix; Step S22: Obtain a comparison matrix according to the constructed Pearson correlation matrix and the set comparison parameters.
[0008] In order to better implement the present invention, further, the specific operation of step S22 is: according to the set comparison parameter, if the correlation coefficient between the current user and any selected user is greater than or equal to the set correlation threshold, the value of the comparison parameter is increased by 1, and the current user is compared with the remaining users to obtain a comparison matrix.
[0009] In order to better implement the present invention, further, the step S3 specifically includes the following steps: Step S31: judging whether the current user belongs to the current low-voltage distribution network area according to the comparison matrix, the set classification threshold, and the number of user tables; Step S32: verifying and obtaining the household-to-household relationship according to the similarity of the user voltage curves; Step S33: according to the user change relationship, a clustering method is called to obtain user characteristics; Step S34: Classify users according to their characteristics and construct a low-voltage distribution network topology.
[0010] In order to better implement the present invention, further, the specific operation of step S31 is: according to the comparison matrix D, the set classification threshold a, and the number of user tables b, determine whether the current user belongs to the current low-voltage distribution network area; if the comparison matrix D≥a×b, then determine that the current user belongs to the current low-voltage distribution network area; if the comparison matrix D<a×b, then determine that the current user belongs to the current low-voltage distribution network area.
[0011] In order to better implement the present invention, further, the step S4 specifically includes the following steps: Step S41: obtaining low-voltage distribution network operation data and user meter operation data, and calculating the user meter operation status value; Step S42: constructing a fault prediction model according to the constructed low-voltage distribution network topology and the operating status value of the user's electric meter; Step S43: Obtain the current user meter failure rate according to the set operating status standard value and the constructed fault model.
[0012] Based on the above-mentioned low-voltage distribution network fault prediction method based on topology identification, in order to better realize the present invention, further, a low-voltage distribution network fault prediction system based on topology identification is proposed, which is used to execute the above-mentioned low-voltage distribution network fault prediction method based on topology identification; including a preprocessing unit, a comparison unit, a topology unit, and a prediction unit; The preprocessing unit is used to obtain low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and preprocess to obtain meter data sets; The comparison unit is used to calculate the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data according to the meter data set, and construct a comparison matrix; The topology unit is used to determine whether the user belongs to the current low-voltage distribution network area according to the comparison matrix, and obtain the low-voltage distribution network topology structure; The prediction unit is used to establish a fault prediction model according to the low-voltage distribution network topology structure to obtain the current user meter failure rate.
[0013] Based on the above-mentioned low-voltage distribution network fault prediction method based on topology identification, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned low-voltage distribution network fault prediction method based on topology identification is implemented.
[0014] Based on the above-mentioned low-voltage distribution network fault prediction method based on topology identification, in order to better realize the present invention, further, a computer-readable storage medium is proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned low-voltage distribution network fault prediction method based on topology identification is implemented.
[0015] The present invention has the following beneficial effects: (1) The present invention calculates the Pearson correlation coefficient of the user's electricity meter and combines the phase to judge the topological relationship of the low-voltage distribution network, thereby ensuring the accuracy and speed of household-to-transformer relationship identification.
[0016] (2) The present invention calculates the state value through the constructed prediction model, realizes the prediction of the failure rate, and speeds up the efficiency of active repair of low-voltage distribution network failures. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 A schematic flow chart of the low-voltage distribution network fault prediction method based on topology identification provided by the present invention. DETAILED DESCRIPTION
[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.
[0019] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0020] Embodiment 1: This embodiment proposes a low-voltage distribution network fault prediction method based on topology identification, which specifically includes the following steps: Step S1: Obtain low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and pre-process to obtain meter data sets.
[0021] Furthermore, the step S1 specifically includes the following steps: Step S11: acquiring low-voltage distribution station area meter data and user meter data from the low-voltage distribution network according to the set collection cycle and collection point; Step S12: sorting the low-voltage distribution station area meter data and the user meter data, and calculating the average value of the low-voltage distribution station area meter and the average value of the user meter; Step S13: taking the average value of the electric meters in the low-voltage distribution station area and the average value of the electric meters of the users as the time window value to obtain the electric meter data set.
[0022] Step S2: Based on the meter data set, the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data is calculated to construct a comparison matrix.
[0023] Furthermore, the step S2 specifically includes the following steps: Step S21: Calculate the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data according to the meter data set, and construct a Pearson correlation matrix; Step S22: Obtain a comparison matrix according to the constructed Pearson correlation matrix and the set comparison parameters.
[0024] The specific operation of step S22 is: according to the set comparison parameter, if the correlation coefficient between the current user and any selected user is greater than or equal to the set correlation threshold, the value of the comparison parameter is increased by 1, and the current user is compared with the remaining users to obtain a comparison matrix.
[0025] Step S3: According to the comparison matrix, determine whether the user belongs to the current low-voltage distribution station area, and obtain the low-voltage distribution network topology structure.
[0026] Furthermore, the step S3 specifically includes the following steps: Step S31: judging whether the current user belongs to the current low-voltage distribution network area according to the comparison matrix, the set classification threshold, and the number of user tables; The specific operation of step S31 is: according to the comparison matrix D, the set classification threshold a, and the number of user tables b, determine whether the current user belongs to the current low-voltage distribution network area. If the comparison matrix D≥a×b, then determine that the current user belongs to the current low-voltage distribution network area; if the comparison matrix D<a×b, then determine that the current user belongs to the current low-voltage distribution network area.
[0027] Step S32: verifying and obtaining the household-to-household relationship according to the similarity of the user voltage curves; Step S33: according to the user change relationship, a clustering method is called to obtain user characteristics; Step S34: Classify users according to their characteristics and construct a low-voltage distribution network topology.
[0028] Step S4: Establish a fault prediction model according to the low-voltage distribution network topology to obtain the failure rate of the front user's electric meter.
[0029] Furthermore, the step S4 specifically includes the following steps: Step S41: obtaining low-voltage distribution network operation data and user meter operation data, and calculating the user meter operation status value; Step S42: constructing a fault prediction model according to the constructed low-voltage distribution network topology and the operating status value of the user's electric meter; Step S43: Obtain the current user meter failure rate according to the set operating status standard value and the constructed fault model.
[0030] Working principle: This embodiment first obtains the low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and pre-processes the meter data set; secondly, based on the meter data set, the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data is calculated to construct a comparison matrix; then, based on the comparison matrix, it is determined whether the user belongs to the current low-voltage distribution station area to obtain the low-voltage distribution network topology structure; finally, a fault prediction model is established based on the low-voltage distribution network topology structure to obtain the current user meter failure rate; the fault identification efficiency is improved, and real-time fault prediction is achieved.
[0031] Embodiment 2: This embodiment is based on the above embodiment 1. Figure 1 As shown, a specific embodiment is described in detail, which specifically includes the following steps.
[0032] Step S1: Obtain low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and pre-process to obtain meter data sets.
[0033] First, according to the set collection period T and the number of collection points n, the low-voltage distribution station area meter data and user meter data are obtained from the low-voltage distribution network; Then sort the low-voltage distribution station area meter data and user meter data, and calculate the low-voltage distribution station area meter average value and user meter average value; Finally, the average values of the electric meters in the low-voltage distribution station area and the average values of the electric meters of the users are taken as the time window values to obtain the electric meter data set.
[0034] In this embodiment, data is acquired once every acquisition period T, and n acquisition points are used as a group of data. The n data are arranged, and the average of the middle m numbers is taken as the time window value.
[0035] Step S2: Based on the meter data set, the Pearson correlation coefficient γ between the low-voltage distribution station area meter data and the user meter data is calculated to construct a comparison matrix.
[0036] The Pearson correlation coefficient calculated in this embodiment refers to the Pearson correlation coefficient between the current user meter and the remaining user meters, with the current user meter as a reference. Taking the current meter A1 as a reference object, before switching phases, the user meters A2, A3, and A4 with the strongest correlation with the current meter A1 are sorted according to the calculated Pearson correlation coefficient. After the current user meter A1 switches phase to phase B, the user meters B2, B3, and B4 with the strongest correlation with the current meter B1 are obtained according to the Pearson correlation coefficient. Combined with the change in correlation strength caused by the change in phase, the change in the topology of the low-voltage distribution network is judged.
[0037] Firstly, the Pearson correlation coefficient between the current user's meter data and the other user's meter data is calculated based on the meter data set, and the Pearson correlation matrix is constructed; Then, according to the set comparison parameters, it is determined whether the current user's meter is within the current low-voltage distribution network area. If the correlation coefficient between the current user and any selected user is greater than or equal to the set correlation threshold, the value of the comparison parameter is increased by 1, and then the current user is compared with the remaining users to obtain the comparison matrix D.
[0038] Step S3: According to the comparison matrix D, determine whether the user belongs to the current low-voltage distribution station area, and obtain the low-voltage distribution network topology structure.
[0039] First, according to the comparison matrix, the set classification threshold, and the number of user tables, it is determined whether the current user belongs to the current low-voltage distribution network area; Secondly, according to the comparison matrix D, the set classification threshold a, and the number of user tables b, it is determined whether the current user belongs to the current low-voltage distribution network area. If the comparison matrix D≥a×b, it is determined that the current user belongs to the current low-voltage distribution network area. If the comparison matrix D<a×b, it is determined that the current user belongs to the current low-voltage distribution network area.
[0040] Then, based on the similarity of the user voltage curves, the user-to-user relationship is verified; Finally, according to the household-to-transformer relationship, the clustering method is called to obtain user characteristics; users are classified according to user characteristics, and the low-voltage distribution network topology is constructed.
[0041] Step S4: Establish a fault prediction model according to the low-voltage distribution network topology to obtain the failure rate of the front user's electric meter.
[0042] First, obtain the low-voltage distribution network operation data X1 and the user's meter operation data X2, and calculate the user's meter operation status value Z; Then, a fault prediction model is constructed based on the constructed low-voltage distribution network topology and the user meter operation status value Z; Finally, according to the set operating state standard value Z b , construct the fault model and obtain the current user meter failure rate.
[0043] The other parts of this embodiment are the same as those of the above-mentioned embodiment 1, and thus will not be described in detail.
[0044] Embodiment 3: Based on any one of the above-mentioned embodiments 1-2, this embodiment proposes a low-voltage distribution network fault prediction system based on topology identification, which is used to execute the above-mentioned low-voltage distribution network fault prediction method based on topology identification; it includes a preprocessing unit, a comparison unit, a topology unit, and a prediction unit; The preprocessing unit is used to obtain low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and preprocess to obtain meter data sets; The comparison unit is used to calculate the Pearson correlation coefficient between the low-voltage distribution station area meter data and the user meter data according to the meter data set, and construct a comparison matrix; The topology unit is used to determine whether the user belongs to the current low-voltage distribution network area according to the comparison matrix, and obtain the low-voltage distribution network topology structure; The prediction unit is used to establish a fault prediction model according to the low-voltage distribution network topology structure to obtain the current user meter failure rate.
[0045] This embodiment also proposes an electronic device, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned low-voltage distribution network fault prediction method based on topology identification is implemented.
[0046] This embodiment further proposes a computer-readable storage medium, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned low-voltage distribution network fault prediction method based on topology identification is implemented.
[0047] The other parts of this embodiment are the same as any one of the above-mentioned embodiments 1-2, so they will not be repeated here.
[0048] The processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.
[0049] The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.
[0050] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0051] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0052] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0053] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.
[0054] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one device or distributed on multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.
[0055] In addition, each functional module in each embodiment of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.
[0056] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or may include one or more servers, data centers and other data storage devices that can be integrated with the medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).
[0057] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. A low voltage distribution network fault prediction method based on topology recognition, characterized in that: The specific steps include: Step S1: acquiring low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and preprocessing to obtain a meter data set; Step S2: According to the electric meter data set, the Pearson correlation coefficient between the electric meter data of the current user and the electric meter data of other users is calculated to construct a comparison matrix; Step S3: judging whether the current user belongs to the current low-voltage distribution network area according to the comparison matrix, and obtaining the low-voltage distribution network topology structure; Step S4: Establish a fault prediction model according to the low-voltage distribution network topology to obtain the current user meter failure rate.
2. A low voltage distribution network fault prediction method based on topology identification according to claim 1, characterized in that: The step S1 specifically includes the following steps: Step S11: acquiring low-voltage distribution station area meter data and user meter data from the low-voltage distribution network according to the set collection cycle and collection point; Step S12: sorting the low-voltage distribution station area meter data and the user meter data, and calculating the average value of the low-voltage distribution station area meter and the average value of the user meter; Step S13: taking the average value of the electric meters in the low-voltage distribution station area and the average value of the electric meters of the users as the time window value to obtain the electric meter data set.
3. A low voltage distribution network fault prediction method based on topology identification according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S21: Calculate the Pearson correlation coefficient between the current user's meter data and the remaining user's meter data according to the meter data set, and construct a Pearson correlation matrix; Step S22: Obtain a comparison matrix according to the constructed Pearson correlation matrix and the set comparison parameters.
4. A low voltage distribution network fault prediction method based on topology identification according to claim 3, characterized in that: The specific operation of step S22 is: according to the set comparison parameter, if the correlation coefficient between the current user and any selected user is greater than or equal to the set correlation threshold, the value of the comparison parameter is increased by 1, and the current user is compared with the remaining users to obtain a comparison matrix.
5. A low voltage distribution network fault prediction method based on topology identification according to claim 1, characterized in that: The step S3 specifically comprises the following steps: Step S31: judging whether the current user belongs to the current low-voltage distribution network area according to the comparison matrix, the set classification threshold, and the number of user tables; Step S32: verifying and obtaining the household-to-household relationship according to the similarity of the user voltage curves; Step S33: according to the user change relationship, a clustering method is called to obtain user characteristics; Step S34: Classify users according to their characteristics and construct a low-voltage distribution network topology.
6. A low voltage distribution network fault prediction method based on topology identification according to claim 5, characterized in that: The specific operation of step S31 is: according to the comparison matrix D, the set classification threshold a, and the number of user tables b, determine whether the current user belongs to the current low-voltage distribution network area. If the comparison matrix D≥a×b, then determine that the current user belongs to the current low-voltage distribution network area; if the comparison matrix D<a×b, then determine that the current user belongs to the current low-voltage distribution network area.
7. A low voltage distribution network fault prediction method based on topology identification according to claim 1, characterized in that: The step S4 specifically comprises the following steps: Step S41: obtaining low-voltage distribution network operation data and user meter operation data, and calculating the user meter operation status value; Step S42: constructing a fault prediction model according to the constructed low-voltage distribution network topology and the operating status value of the user's electric meter; Step S43: Obtain the current user meter failure rate according to the set operating status standard value and the constructed fault model.
8. A low-voltage distribution network fault prediction system based on topology identification, used to execute the low-voltage distribution network fault prediction method based on topology identification as claimed in claim 1; characterized in that: It includes a preprocessing unit, a comparison unit, a topology unit, and a prediction unit; The preprocessing unit is used to obtain low-voltage distribution station area meter data and user meter data from the low-voltage distribution network, and preprocess to obtain meter data sets; The comparison unit is used to calculate the Pearson correlation coefficient between the current user's meter data and the other users' meter data according to the meter data set, and construct a comparison matrix; The topology unit is used to determine whether the user belongs to the current low-voltage distribution network area according to the comparison matrix, and obtain the low-voltage distribution network topology structure; The prediction unit is used to establish a fault prediction model according to the low-voltage distribution network topology structure to obtain the current user meter failure rate.
9. An electronic device, characterized in that: It comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the low-voltage distribution network fault prediction method based on topology identification as described in any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as described in claim 9, the low-voltage distribution network fault prediction method based on topology identification as described in any one of claims 1-7 is implemented.