An improved SABO-based grounding net corrosion fault diagnosis method and system

Abstract

The application relates to a grounding net corrosion fault diagnosis method and system based on an improved SABO, wherein the provided method comprises the following steps: acquiring all accessible nodes based on a grounding net topological structure, injecting excitation current into the grounding net in a multiple rotation excitation mode, and acquiring the measurement value of the node voltage and the initial value of the branch resistance; taking the change multiple of the branch resistance value relative to the initial value of the branch resistance as a to-be-solved variable, constructing a fitness function based on the measurement value and the calculation value, and solving the optimal value of the change multiple of each branch by using an improved SABO algorithm based on dynamic differential variation; and judging whether the branch corresponding to the optimal value exists corrosion fault based on the optimal value. Compared with the prior art, the application can more accurately and efficiently identify the fault area in the grounding net.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment maintenance, and in particular to a grounding grid corrosion fault diagnosis method and system based on an improved SABO algorithm. Background Technology

[0002] In the safe operation of power systems, the grounding grid, as a key facility for lightning protection and fault current discharge in substations, directly affects the reliability of the power grid due to its corrosion status. Because of the coupling of multiple factors such as high soil humidity, industrial pollution, and stray currents, the corrosion rate of the grounding grid is significantly higher than in conventional environments. However, traditional diagnostic techniques have significant limitations: firstly, relying on periodic excavation and sampling inspections (including visual inspection and weightlessness methods) damages the grounding electrode structure and has a coverage rate of less than 10%, making it impossible to achieve a full network status assessment; secondly, offline detection methods based on single electrical parameters struggle to distinguish between soil parameter fluctuations and actual corrosion loss, easily leading to misjudgments. To address these issues, Chinese patent application CN105044559A proposes a regional fault diagnosis method for substation grounding grids, targeting a given grounding grid design topology. The method involves a layered reduction of the grounding grid topology, calculating the nominal resistance parameters of branches based on the original design data, selecting common nodes, and generating an association matrix and branch admittance matrix. A simulation circuit model is built, and DC source excitation is applied between selected nodes in the circuit to test the voltage of selected accessible nodes and obtain the port resistance between nodes. Based on the port resistance value, it is determined whether the branch has corrosion. Although this method solves the above two problems, when solving high-dimensional nonlinear corrosion location problems, the high dimensionality of the problem and poor convergence of the solution algorithm due to the complexity of the topology result in corrosion branch location errors exceeding 30%.

[0003] Therefore, providing a grounding grid fault diagnosis method that can accurately handle high-dimensional nonlinear corrosion location problems is a technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects of the existing technology and provide a grounding grid corrosion fault diagnosis method and system based on the improved SABO algorithm. By introducing dynamic differential variation to improve the traditional SABO algorithm, the grounding grid corrosion is diagnosed based on the improved SABO algorithm, and finally, impedance imaging is performed based on the corrosion location results, thus achieving accurate grounding grid imaging.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] According to a first aspect of the present invention, a grounding grid corrosion fault diagnosis method based on improved SABO is provided, comprising:

[0007] Based on the grounding grid topology, all accessible nodes are obtained, and excitation current is injected into the grounding grid using a multiple rotation excitation method to obtain the measured values ​​of node voltages and the initial values ​​of branch resistances.

[0008] The change factor of the branch resistance value relative to the initial value of the branch resistance is taken as the variable to be solved. A fitness function is constructed based on the measured value and the calculated value. The optimal value of the change factor of each branch is obtained by using the improved SABO algorithm based on dynamic differential mutation.

[0009] Based on the aforementioned optimal value, determine whether the branch corresponding to the optimal value has a corrosion fault.

[0010] As a preferred technical solution, the method of employing multiple rotational excitations includes:

[0011] A1. Select any two accessible nodes from all the accessible nodes as the inflow and outflow ends of the excitation current.

[0012] A2. Inject the excitation current from the inflow end and out from the outflow end, and measure the node voltage of all accessible nodes under the corresponding excitation mode.

[0013] A3. Select two other reachable nodes or change the current value of the excitation current, and repeat step A2 until the termination condition is met.

[0014] As a preferred technical solution, the expression for the fitness function is:

[0015] ,

[0016] in, This represents the measured value of the node voltage of the i-th reachable node; represents the calculated value of the node voltage of the i-th reachable node; x represents the number of reachable nodes.

[0017] As a preferred technical solution, the method for improving the SABO algorithm using dynamic differential mutation is as follows: The search agent update in the SABO algorithm is optimized using a dynamic differential mutation strategy, and dynamic differential mutation is performed, the expression of which is:

[0018] ,

[0019] in, This represents the new search agent at the (t+1)th iteration; express The number that changes dynamically between them; This represents the optimal solution in the current iteration; This represents the search agent that was originally updated in the SABO algorithm at the t-th iteration; This represents a search agent dynamically selected from the solution space of the variable to be solved.

[0020] As a preferred technical solution, the method for solving the optimal value includes:

[0021] A mathematical model relating the change factor to the node voltage is constructed using the electrical network method.

[0022] Obtain the boundary of the change factor, randomly generate an initial population of the improved SABO algorithm within the boundary and initialize the parameters of the improved SABO algorithm, and each search agent in the population represents a set of candidate solutions for all branch change factors;

[0023] Iteratively execute the following steps until the iteration termination condition is met:

[0024] Based on the mathematical relationship model described above, the calculated value of the node voltage of each reachable node is obtained, and based on the measured value of the node voltage of the corresponding reachable node, the fitness value of each search agent is calculated, and the best fitness value and its corresponding optimal candidate solution are obtained.

[0025] Perform an initial update on each of the search agents, calculate the first fitness value of each search agent after the initial update, and select the first minimum value among the first fitness values. If the minimum value is less than the best fitness value, update the best fitness value to the minimum value and take the candidate solution corresponding to the minimum value as the optimal candidate solution; otherwise, do not perform any operation.

[0026] After the original update, each agent will undergo dynamic differential mutation, and the second fitness value of each search agent after dynamic differential mutation will be calculated. The minimum value among the second fitness values ​​will be selected. If the minimum value is less than the best fitness value, the best fitness value will be updated to the minimum value, and the candidate solution corresponding to the minimum value will be taken as the optimal candidate solution. Otherwise, no operation will be performed.

[0027] As a preferred technical solution, the method for original update includes:

[0028] Calculate the v-subtraction value between each search agent and the remaining search agents, and calculate the average of the v-subtraction values;

[0029] The search agent is updated based on the aforementioned average value.

[0030] As a preferred technical solution, the expression for calculating the v-subtraction value is:

[0031] ,

[0032] in, This represents the v-subtraction value between search agent A and search agent B; This represents the fitness value corresponding to search agent A; This represents the fitness value corresponding to agent B in the search. This represents a randomly generated number whose dimension is the same as the number of variables to be solved and whose value range is [1,2].

[0033] As a preferred technical solution, the method for updating the search agent based on the aforementioned average value is as follows:

[0034] ,

[0035] in, Indicates the i-th search agent; The number of randomly generated numbers whose dimension is the same as the number of variables to be solved; Indicates the total number of search agents; Indicates search agent and search agent The v-subtraction value; , indicating a search proxy index.

[0036] According to a second aspect of the present invention, a grounding grid corrosion fault diagnosis system based on an improved SABO is provided, the system being used to implement the method of any one of claims 1 to 8.

[0037] As a preferred technical solution, a grounding grid corrosion fault diagnosis system based on improved SABO is characterized in that the system comprises:

[0038] Data input block: Used to receive measurements of node voltages and initial branch resistances, including the grounding grid topology, the node voltages of accessible nodes, and the branch resistances.

[0039] Parameter setting block: Used to initialize the parameters of the improved SABO algorithm;

[0040] Algorithm block: Embedded improved SABO algorithm, based on the data collected by the data input block and using the initialization parameters provided by the parameter setting block to solve the change factor of the branch resistance relative to the initial value of the branch resistance;

[0041] Visualization block: used to display the convergence curve during the solution process of the algorithm block and the resistance change heat map generated based on the change factor;

[0042] Report generation block: Generates a report based on the solution results of the algorithm block. The report includes abnormal branch location, resistance offset, and maintenance recommendations.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1) This invention provides a grounding grid fault diagnosis method based on an optimized SABO algorithm. It introduces a dynamic differential mutation strategy to improve the individual update method in the traditional SABO algorithm, so that it can still solve the problem of grounding grid with complex topology and high-dimensional nonlinear corrosion location problem efficiently and accurately. Moreover, the introduction of the dynamic differential mutation strategy can avoid the SABO algorithm from falling into premature convergence when solving high-dimensional nonlinear corrosion location problem, and can jump out of the local optimum and find the global optimum.

[0045] 2) The method provided by this invention has good versatility and scalability, and can be applied to grounding grid corrosion fault diagnosis problems of different scales and complexities. In the solution process, there is no need to perform complex mathematical modeling of the grounding grid. It is only necessary to obtain the voltage measurement value and the initial resistance value based on its topology, and then use the optimized SABO algorithm to solve it. In particular, when dealing with complex grounding grids, there is no need to solve high-dimensional nonlinear equations, and the solution efficiency is high. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention;

[0047] Figure 2 This is a schematic diagram of the measurement process of the present invention;

[0048] Figure 3 This is an equivalent model diagram of the grounding grid electrical network in Embodiment 2 of the present invention;

[0049] Figure 4 This is a bar graph illustrating the experimental structure of Embodiment 2 of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0051] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0052] Example 1

[0053] To address the technical problems existing in the prior art, this invention provides a grounding grid corrosion fault diagnosis method based on an improved SABO algorithm, the process of which is as follows: Figure 1 As shown, it includes:

[0054] S1. Based on the grounding grid topology, obtain all accessible nodes and inject excitation current into the grounding grid using a multiple rotation excitation method to obtain the measured values ​​of node voltages and the initial values ​​of branch resistances.

[0055] Since the number of reachable nodes is much smaller than the total number of nodes, the solution process is ill-conditioned, and the results are difficult to converge to the true value or even fail to converge. Therefore, it is necessary to expand the data volume by measuring voltage values ​​under various excitation methods to improve the ill-conditioned problem of the electric network method.

[0056] In this process, the grounding grid corrosion fault diagnosis equation constructed based on the electrical network theory method is as follows:

[0057] , ,

[0058] in, Node impedance matrix; Branch impedance matrix; A represents the nodal branch correlation matrix, which is a... A 3D matrix, where if a node and a branch are positively correlated, the corresponding element in the matrix is ​​+1; if a node and a branch are negatively correlated, the corresponding element in the matrix is ​​-1; and if a node and a branch are uncorrelated, the corresponding element in the matrix is ​​0. Node voltage matrix; The node current matrix is ​​an (n-1)×k dimensional matrix, assuming there are k excitation modes during the diagnosis process. The injected current from the current source is represented by I. The corresponding elements in the matrix are: +1 for injected nodes, -1 for outflowing nodes, and 0 for all other nodes.

[0059] In this process, since the number of accessible nodes m is much smaller than the total number of nodes n, a multi-stage rotation of excitation measurements is used to improve the underdetermined problem of the grounding grid fault diagnosis solution model. Under the I-th excitation mode, given the known node branch correlation matrix and initial branch resistance values, the node potentials under this excitation mode can be calculated using electrical network theory. For the e-th (e=1,2,3,…I)-th excitation mode, the following formula exists: , Let represent the column vector of node currents under the e-th type of excitation.

[0060] In detail, according to such Figure 2 The rotation incentives shown include:

[0061] A1. Select any two accessible nodes from all accessible nodes as the inflow and outflow ends of the excitation current.

[0062] A2. Inject the excitation current from the inflow end and let it flow out from the outflow end. Measure the node voltage of all accessible nodes under the corresponding excitation mode to obtain a set of node current column vectors and corresponding voltage column vectors.

[0063] A3. Select two other reachable nodes or change the current value of the excitation current, and repeat step A2 until the termination condition is met.

[0064] S2. The change factor of the branch resistance value relative to the initial value of the branch resistance is taken as the variable to be solved. A fitness function is constructed based on the measured value and the calculated value. The optimal value of the change factor of each branch is obtained by using the improved SABO algorithm based on dynamic differential mutation.

[0065] S21. Construction of fitness function.

[0066] ,

[0067] in, This represents the measured value of the node voltage of the i-th reachable node; represents the calculated value of the node voltage of the i-th reachable node; x represents the number of reachable nodes.

[0068] S22. Solve for the branch resistance transformation factor.

[0069] S221. Construct a mathematical model of the relationship between the change factor and the node voltage using the electric network method.

[0070] S222. Obtain the boundary of the change factor, randomly generate an initial population of the improved SABO algorithm in the boundary and initialize the parameters of the improved SABO algorithm, and each search agent in the population represents a set of candidate solutions for the change factors of all branches.

[0071] In detail, in the traditional SABO algorithm, the position update method for individuals in the population is to compare the current individual with the current best individual. New individuals emerge nearby, and as the iterative search continues, the population clusters around a local optimum. The surrounding environment leads to a loss of population diversity, causing premature convergence and low convergence accuracy in the algorithm. Therefore, this invention uses a dynamic differential mutation strategy to optimize the search surrogate update in the SABO algorithm, performing dynamic differential mutation, the expression of which is:

[0072] ,

[0073] in, This represents the new search agent at the (t+1)th iteration; express The number that changes dynamically between them; This represents the optimal solution in the current iteration; This represents the search agent that was originally updated in the SABO algorithm at the t-th iteration; This represents a search agent dynamically selected from the solution space of the variable to be solved.

[0074] In detail, there are:

[0075] ,

[0076] ,

[0077] in, This represents the search proxy matrix of the improved SABO algorithm; Indicates the i-th search agent; Let represent the d-th dimension of the ith search agent in the search space; N represents the number of search agents; M represents the number of decision variables, i.e., the number of parameters to be solved. This represents the upper limit of the d-th decision variable; This represents the lower bound of the d-th decision variable.

[0078] Since each search agent represents a set of candidate solutions with varying factors across all branches, the objective function of the problem can be evaluated based on each search agent. The evaluation of the objective function can be represented using a vector called , where the objective function is evaluated and stored in the vector based on the placement of specified values ​​for the decision variables of the problem by each population member. Therefore, the number of elements in the vector is equal to the total number of members N.

[0079] The evaluation value of the objective function serves as a suitable criterion for analyzing the quality of the solutions proposed by the search agent. Therefore, the optimal value calculated for the objective function corresponds to the optimal search agent. Similarly, the worst value calculated for the objective function corresponds to the worst search agent. Considering that the search agent's position in the search space is updated in each iteration, the process of identifying and saving the optimal search agent continues until the last iteration of the algorithm.

[0080] Iteratively execute steps S223~S225 until the iteration termination condition is met:

[0081] S223. Based on the mathematical relationship model, obtain the calculated value of the node voltage of each reachable node, and based on the measured value of the node voltage of the corresponding reachable node, calculate the fitness value of each search agent, and obtain the best fitness value and its corresponding optimal candidate solution.

[0082] At this point, we have:

[0083] ,

[0084] in, This represents the fitness value of the i-th search agent.

[0085] S224. Perform an initial update on each search agent and calculate the first fitness value of each search agent after the initial update. Select the first minimum value among the first fitness values. If the minimum value is less than the best fitness value, update the best fitness value to the minimum value and take the candidate solution corresponding to the minimum value as the optimal candidate solution. Otherwise, do not perform any operation.

[0086] i. Calculate the v-subtraction value between each search agent and the other search agents, and calculate the average of the v-subtraction values.

[0087] The SABO algorithm introduces a new computational concept, "-v", which is called the v-subtraction between search agent B and search agent A, defined as follows:

[0088] ,

[0089] in, This represents the v-subtraction value between search agent A and search agent B; This represents the fitness value corresponding to search agent A; This represents the fitness value corresponding to agent B in the search. This represents a randomly generated number whose dimension is the same as the number of variables to be solved and whose value range is [1,2].

[0090] ii. Update the search agent based on the average value.

[0091] Its expression is:

[0092] ,

[0093] in, Indicates the i-th search agent; This represents a randomly generated number whose dimension is the same as the number of variables to be solved. Indicates the total number of search agents; Indicates search agent and search agent The v-subtraction value; , indicating a search proxy index.

[0094] At this point, we have:

[0095] ,

[0096] in, Indicates the original position; Indicates the search proxy location after the original update; This represents the fitness value after updating from the original location; This represents the fitness value before the update.

[0097] S225. Perform dynamic differential mutation on each agent after the original update, calculate the second fitness value of each search agent after dynamic differential mutation, and select the minimum value among the second fitness values. If the minimum value is less than the best fitness value, update the best fitness value to the minimum value, and take the candidate solution corresponding to the minimum value as the optimal candidate solution. Otherwise, do not operate.

[0098] S3. Determine whether there is a corrosion fault in the branch corresponding to the optimal value based on the optimal value.

[0099] If the optimal value exceeds the preset value, the branch corresponding to the optimal value is considered to be corroded, and a report is generated according to the judgment result. The report includes the location of the abnormal branch, the resistance offset, and maintenance suggestions.

[0100] Example 2

[0101] To verify the feasibility of the method provided in Example 1, this example demonstrates... Figure 3 The model shown uses the above method for corrosion diagnosis to verify the method, including:

[0102] S1. Based on the grounding grid topology, obtain all accessible nodes and inject excitation current into the grounding grid using a multiple rotation excitation method to obtain the measured values ​​of node voltages and the initial values ​​of branch resistances.

[0103] S2. The change factor of the branch resistance value relative to the initial value of the branch resistance is taken as the variable to be solved. A fitness function is constructed based on the measured value and the calculated value. The optimal value of the change factor of each branch is obtained by using the improved SABO algorithm based on dynamic differential mutation.

[0104] S3. Determine whether there is a corrosion fault in the branch corresponding to the optimal value based on the optimal value.

[0105] After the above steps, the result is as follows: Figure 4 The branch resistance diagram shown in the figure has the branch number on the horizontal axis and the change factor of the branch resistance relative to the initial value on the vertical axis, indicating that the method provided in Example 1 is feasible.

[0106] Example 3

[0107] This embodiment provides a grounding grid corrosion fault diagnosis system based on an improved SABO algorithm to implement the above-mentioned method, including a software part and a hardware part, wherein the software part includes:

[0108] Data input block: Used to receive the grounding grid topology diagram, measured values ​​of node voltages of accessible nodes, and initial values ​​of branch resistances. It supports uploading topology files in Excel format, measured node voltage data, and nominal branch resistance values.

[0109] Parameter setting block: Used to initialize the parameters of the improved SABO algorithm, providing population size, number of iterations, and regularization coefficient. Improve the SABO algorithm parameters;

[0110] Algorithm block: Embedded improved SABO algorithm, based on data collected by the data input block and using initialization parameters provided by the parameter setting block to solve for the change factor of branch resistance relative to the initial value of branch resistance;

[0111] Visualization block: Used to display the convergence curve during the algorithm block solution process and the resistance change heat map generated based on the change factor;

[0112] Report generation block: Generates a report based on the solution results of the algorithm block. The report includes the location of abnormal branches, resistance offset, and maintenance recommendations.

[0113] In the software section, the user-uploaded Excel / CSV files are first structured using a data parsing function, converting them into algorithm input formats such as admittance matrices and excitation current vectors. An anomaly detection mechanism is also integrated—when data is missing or the format is abnormal, a callback function is triggered to display a warning dialog box and interrupt the process. Subsequently, the improved SABO optimization algorithm, encapsulated as an independent module, is linked to the front-end interaction. Using measured voltage, network topology, and parameters as input, it iterative calculations output the optimal solution for the resistance change factor of each branch, and dynamic curves provide real-time feedback on the convergence process of the objective function. Finally, the optimization results are generated into a report. The detailed report includes key data such as the abnormal branch number, resistance change factor, and voltage fitting error, which are cached in a structure variable, providing a standardized data interface for the report generation module and achieving seamless integration from algorithm calculation to diagnostic report output.

[0114] The hardware component provided by this invention includes a central processing unit (CPU), which can execute various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0115] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] The processing unit executes the various methods and processes described above, such as methods S1-S3 and A1-A3. For example, in some embodiments, methods S1-S3 and A1-A3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1-S3 and A1-A3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1-S3 and A1-A3 by any other suitable means (e.g., by means of firmware).

[0117] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0118] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0120] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A grounding grid corrosion fault diagnosis method based on improved SABO, characterized in that, include: Based on the grounding grid topology, all accessible nodes are obtained, and excitation current is injected into the grounding grid using a multiple rotation excitation method to obtain the measured values ​​of node voltages and the initial values ​​of branch resistances. The change factor of the branch resistance value relative to the initial value of the branch resistance is taken as the variable to be solved. A fitness function is constructed based on the measured value and the calculated value. The optimal value of the change factor of each branch is obtained by using the improved SABO algorithm based on dynamic differential mutation. The method for improving the SABO algorithm using dynamic differential mutation is as follows: The search agent update in the SABO algorithm is optimized using a dynamic differential mutation strategy, and the expression is: ， in, This represents the new search agent at the (t+1)th iteration; express The number that changes dynamically between them; This represents the optimal solution in the current iteration; This represents the search agent that was originally updated in the SABO algorithm at the t-th iteration; This represents a search agent dynamically selected in the solution space of the variable to be solved; Based on the aforementioned optimal value, determine whether the branch corresponding to the optimal value has a corrosion fault.

2. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 1, characterized in that, The method of employing multiple rotational excitations includes: A1. Select any two accessible nodes from all the accessible nodes as the inflow and outflow ends of the excitation current. A2. Inject the excitation current from the inflow end and out from the outflow end, and measure the node voltage of all accessible nodes under the corresponding excitation mode. A3. Select two other reachable nodes or change the current value of the excitation current, and repeat step A2 until the termination condition is met.

3. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 1, characterized in that, The expression for the fitness function is: ， in, This represents the measured value of the node voltage of the i-th reachable node; represents the calculated value of the node voltage of the i-th reachable node; x represents the number of reachable nodes.

4. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 1, characterized in that, The methods for solving the optimal value include: A mathematical model relating the change factor to the node voltage is constructed using the electrical network method. Obtain the boundary of the change factor, randomly generate an initial population of the improved SABO algorithm within the boundary and initialize the parameters of the improved SABO algorithm, and each search agent in the population represents a set of candidate solutions for all branch change factors; Iteratively execute the following steps until the iteration termination condition is met: Based on the mathematical relationship model described above, the calculated value of the node voltage of each reachable node is obtained, and based on the measured value of the node voltage of the corresponding reachable node, the fitness value of each search agent is calculated, and the best fitness value and its corresponding optimal candidate solution are obtained. Perform an initial update on each of the search agents, calculate the first fitness value of each search agent after the initial update, and select the first minimum value among the first fitness values. If the minimum value is less than the best fitness value, update the best fitness value to the minimum value and take the candidate solution corresponding to the minimum value as the optimal candidate solution; otherwise, do not perform any operation. After the original update, each agent will undergo dynamic differential mutation, and the second fitness value of each search agent after dynamic differential mutation will be calculated. The minimum value among the second fitness values ​​will be selected. If the minimum value is less than the best fitness value, the best fitness value will be updated to the minimum value, and the candidate solution corresponding to the minimum value will be taken as the optimal candidate solution. Otherwise, no operation will be performed.

5. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 4, characterized in that, The original update method includes: Calculate the v-subtraction value between each search agent and the remaining search agents, and calculate the average of the v-subtraction values; The search agent is updated based on the aforementioned average value.

6. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 5, characterized in that, The expression for calculating the v-subtraction value is: ， in, This represents the v-subtraction value between search agent A and search agent B; This represents the fitness value corresponding to search agent A; This represents the fitness value corresponding to agent B in the search. This represents a randomly generated number whose dimension is the same as the number of variables to be solved and whose value range is [1,2].

7. The grounding grid corrosion fault diagnosis method based on improved SABO according to claim 5, characterized in that, The method for updating the search agent based on the aforementioned average value is as follows: ， in, Indicates the i-th search agent; The number of randomly generated numbers whose dimension is the same as the number of variables to be solved; Indicates the total number of search agents; Indicates search agent and search agent The v-subtraction value; , indicating a search proxy index.

8. A grounding grid corrosion fault diagnosis system based on an improved SABO algorithm, characterized in that, The system described herein is used to implement the method of any one of claims 1 to 7.

9. A grounding grid corrosion fault diagnosis system based on an improved SABO as described in claim 8, characterized in that, The system includes: Data input block: Used to receive measurements of node voltages and initial branch resistances, including the grounding grid topology, the node voltages of accessible nodes, and the branch resistances. Parameter setting block: Used to initialize the parameters of the improved SABO algorithm; Algorithm block: Embedded improved SABO algorithm, based on the data collected by the data input block and using the initialization parameters provided by the parameter setting block to solve the change factor of the branch resistance relative to the initial value of the branch resistance; Visualization block: used to display the convergence curve during the solution process of the algorithm block and the resistance change heat map generated based on the change factor; Report generation block: Generates a report based on the solution results of the algorithm block. The report includes abnormal branch location, resistance offset, and maintenance recommendations.

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