Grounding grid corrosion diagnosis method and system based on electric network theory and intelligent algorithm

Abstract

The invention discloses a grounding grid corrosion diagnosis method and system based on an electric network theory and an intelligent algorithm. The method comprises the following steps: establishing a fault diagnosis equation of a grounding grid by using the electric network theory; iteratively solving the fault diagnosis equation by adopting a particle swarm simulated annealing fusion algorithm to obtain a globally optimal solution of the resistance of each branch of the grounding grid; and comparing the global optimal solution of the resistance of each branch of the grounding grid with the corresponding nominal resistance, and identifying the corrosion condition of each branch of the grounding grid. According to the method, the fault diagnosis equation is established by using the electrical network theory, the optimization model about the node voltage is constructed, and iterative solution is performed by using the particle swarm simulated annealing fusion algorithm, so that each corrosion branch of the grounding grid can be quickly and accurately identified, and technical support and technical reference are provided for grounding grid corrosion diagnosis research.

Description

Technical Field

[0001] The present application relates to the technical field of grounding grid diagnosis in power systems, and in particular to a grounding grid corrosion diagnosis method and system based on electrical network theory and intelligent algorithms. Background Art

[0002] The grounding grid of the power system plays a vital role in ensuring the safety of equipment and personnel. It achieves this goal by equalizing voltage and dissipating current. However, since the grounding grid is buried deep underground for a long time, it will inevitably be affected by electrochemical corrosion in the soil environment and leakage corrosion during the operation of power grid equipment. These corrosion effects will cause the cross-sectional area of ​​the grounding body to decrease or even break, which will lead to the problem of deterioration of grounding performance.

[0003] Severe corrosion of the substation grounding grid may not only cause power grid equipment failure, but also pose a threat to personal safety. However, since the grounding grid is buried underground, it is an extremely difficult task to renovate it. The traditional approach is usually to conduct large-scale excavation to find the corrosion location, but this method is not only labor-intensive, but also has a large degree of blindness and often fails to achieve the expected results.

[0004] In addition, there are also technologies that use intelligent algorithms, such as genetic algorithms, particle swarm algorithms, grey wolf algorithms, etc. to realize grounding grid corrosion diagnosis. However, they are prone to misdiagnosis and missed diagnosis, and their accuracy and reliability need to be improved. Summary of the invention

[0005] In response to the problems existing in the prior art, this application proposes a grounding grid corrosion diagnosis method and system based on electrical network theory and intelligent algorithms. This application uses electrical network theory to establish a fault diagnosis equation for the grounding grid corrosion problem, constructs an optimization model for node voltage, and uses a hybrid simulated annealing algorithm for optimization and solution. It can quickly and accurately identify the corroded branches in the grounding grid, providing a technical reference for the research on corrosion fault diagnosis of substation grounding grids.

[0006] This application is implemented through the following technical solutions:

[0007] A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm, the method comprising:

[0008] Establish the fault diagnosis equation of grounding grid using electrical network theory;

[0009] The fault diagnosis equation is iteratively solved by using a particle swarm simulated annealing fusion algorithm to obtain a global optimal solution for the resistance of each branch of the grounding grid;

[0010] The global optimal solution of the resistance of each branch of the grounding grid is compared with its corresponding nominal resistance to identify the corrosion condition of each branch of the grounding grid.

[0011] In some implementations, the use of electrical network theory to establish a grounding grid fault diagnosis equation specifically includes:

[0012] The grounding grid is modeled based on the electrical network method to obtain the functional relationship between voltage, current and admittance;

[0013] According to the functional relationship and in combination with the initial value of the branch resistance, the voltage of each node is calculated;

[0014] Injecting an excitation current into the grounding grid through a grounding down conductor, and measuring the voltage of an accessible node;

[0015] Taking the difference between the measured voltage and the calculated voltage as the target, the least square method is used to establish the initial fault diagnosis equation;

[0016] The initial fault diagnosis equation is corrected by Tikhonov regularization to obtain the final fault diagnosis equation.

[0017] In some implementations, the fault diagnosis equation obtained after correction is:

[0018]

[0019] Among them, β is the regularization parameter, L is the regularization coefficient matrix, ρ0 is the resistance of each branch at the beginning of the iteration, and ρ is the solution to U f The resistance of each branch, U c is the accessible node measurement voltage matrix, U f Compute the voltage matrix for accessible nodes.

[0020] In some implementations, the iterative solution of the fault diagnosis equation using a particle swarm simulated annealing fusion algorithm specifically includes:

[0021] Initialization parameters: set the number of particles in the particle swarm, each particle contains the resistance value of each branch of the entire grounding network, the number of iterations, the initial temperature, the temperature annealing coefficient and the termination temperature;

[0022] The particle swarm is initialized using the chaos initialization algorithm;

[0023] Determine the individual optimal solution and the group optimal solution, and calculate the fitness function value of the particles at the initial position;

[0024] Update the particle's velocity and position, and calculate the fitness function value of the updated particle;

[0025] The inertia factor is adjusted by using a linear decreasing inertia value weight strategy;

[0026] Calculate the difference between the fitness function value of the new particle and the fitness function value of the previous generation of particles. If the difference is less than 0, the position of the new particle is accepted. Otherwise, the Metropolis criterion is used to accept the new solution with a certain probability.

[0027] Updating the individual optimal solution and the group optimal solution;

[0028] Perform cooling process;

[0029] After each iteration, check whether the preset termination condition is met. If so, the iterative solution algorithm is terminated, otherwise, return to the step of updating the particle speed and position to perform the next iterative solution.

[0030] In some implementations, the speed update formula is:

[0031]

[0032] Among them, ω is the inertia factor, c1 and c2 represent the individual learning factor and social learning factor respectively. is the individual optimal solution of the i-th variable in the population at the k-th iteration, is the global optimal solution of the i-th variable in the population at the k-th iteration, is the particle velocity of the i-th variable in the population at the k+1th iteration, is the particle velocity of the i-th variable in the population at the k-th iteration, is the value of the i-th variable in the population at the k-th iteration;

[0033] The position update formula is:

[0034]

[0035] in, is the value of the i-th variable in the population at the k+1th iteration.

[0036] In some embodiments, the linear decreasing inertia weight strategy is specifically:

[0037] The inertia factor decreases as the number of iterations increases, and is expressed as:

[0038]

[0039] Among them, ω start is the initial inertia weight; ω end To end the inertia weight; iter max is the total number of iterations, and iter is the current number of iterations.

[0040] In some implementations, the checking whether a preset termination condition is reached is specifically:

[0041] Temperature threshold determination: If the current temperature has dropped below the preset minimum temperature, it is considered that the preset termination condition has been met;

[0042] Alternatively, fitness threshold determination: if the fitness function value of the current solution is less than or equal to a preset fitness threshold, it is considered that the preset termination condition has been met.

[0043] In the second aspect, the present application proposes a grounding grid corrosion diagnosis system based on electrical network theory and intelligent algorithm, the system comprising:

[0044] A model building module, wherein the model building module uses electrical network theory to establish a fault diagnosis equation for the grounding grid;

[0045] An iterative solution module, wherein the iterative solution module adopts a particle swarm simulated annealing fusion algorithm to iteratively solve the fault diagnosis equation to obtain a global optimal solution for the resistance of each branch of the grounding grid;

[0046] And, an identification module, which compares the global optimal solution of the resistance of each branch of the grounding grid with its corresponding nominal resistance to identify the corrosion status of each branch of the grounding grid.

[0047] In a third aspect, the present application proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0048] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0049] This application proposes a grounding grid corrosion diagnosis method and system based on electrical network theory and intelligent algorithm. The fault diagnosis equation is established using electrical network theory, an optimization model for node voltage is constructed, and a particle swarm simulated annealing fusion algorithm is used for iterative solution. The method can quickly and accurately identify the corroded branches of the grounding grid, providing technical support and technical reference for the research on grounding grid corrosion diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0051] Figure 1 A flow chart of a grounding grid corrosion diagnosis method according to an embodiment of the present application;

[0052] Figure 2 This is an equivalent schematic diagram of the grounding grid of an embodiment of the present application;

[0053] Figure 3 A schematic diagram of measuring voltages of accessible nodes on a grounding grid according to an embodiment of the present application;

[0054] Figure 4 This is a flow chart of the iterative solution algorithm of the embodiment of the present application;

[0055] Figure 5 This is a principle block diagram of the grounding grid corrosion diagnosis system according to an embodiment of the present application;

[0056] Figure 6 A grounding grid simulation model diagram of an embodiment of the present application;

[0057] Figure 7 The resistance of each branch of the grounding grid is obtained by using the PSO-SA algorithm;

[0058] Figure 8 The resistance of each branch of the grounding grid is obtained by using the PSO algorithm;

[0059] Fig. 9 The resistance of each branch of the grounding grid obtained by using the GWO algorithm;

[0060] Fig.10 The resistance of each branch of the grounding grid obtained by using the GA algorithm;

[0061] Fig.11 It is the fitness curve of the iteration of GA algorithm, PSO algorithm, GWO algorithm and PSO-SA algorithm. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0063] Example 1

[0064] In response to the problem of grounding grid corrosion diagnosis, this application proposes a grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm. This application uses electrical network theory to establish a fault diagnosis equation, constructs an optimization model for node voltage, and uses a hybrid simulated annealing algorithm for optimization and solution, which can quickly and accurately identify the corrosion condition of the grounding grid.

[0065] like Figure 1 As shown, the method proposed in this embodiment specifically includes the following steps:

[0066] Step 100, establishing a fault diagnosis equation for the grounding grid using electrical network theory.

[0067] like Figure 2 As shown, step 100 converts the buried grounding grid into a pure resistance network, and the length, cross-sectional area, resistivity and topological structure of each branch of the grounding grid are fixed, so the resistance of each branch can be calculated. If a branch of the grounding grid is corroded, the resistance of the corresponding branch will increase. The grounding down conductor usually falls at the intersection of the grounding grid. This node is an accessible node for measurement, and the remaining nodes without down conductors are inaccessible nodes. The resistance of each branch of the grounding grid is calculated by injecting an excitation source into two accessible nodes and measuring the port response of the remaining accessible nodes, such as Figure 3 shown.

[0068] Based on the above principle, step 100 specifically includes the following process:

[0069] Step 101 : Modeling the actual grounding grid based on the electrical network method.

[0070] Assume that the parameters of the established grounding network are: the total number of network nodes is n, the total number of branches is b, the location where the excitation source is connected is between accessible nodes i and j, A represents the network association matrix, Y n represents the node admittance matrix, Y b represents the branch admittance matrix, Z is the impedance matrix, U b is the branch voltage matrix, U n is the node voltage matrix, I b is the current matrix of the branch, I n is the column vector of the injected node current. According to the principle of electrical network, the impedance group is the inverse of the admittance matrix, so we can get:

[0071] Y b =Z -1 (1)

[0072] Y n =AY b A T (2)

[0073] I b =Y b A T U n (3)

[0074] U b =A T U n (4)

[0075]

[0076] Step 102, based on a set of initial values ​​of branch resistance, the voltage data of each node can be calculated, and at the same time, the excitation current is injected into the grounding grid of the substation through the grounding down conductor, and the voltage data of the accessible nodes can be measured. Assuming that there are m accessible nodes on the surface, correspondingly, the m voltage data that can be measured are:

[0077] U c = [U c1 ,U c2 ,…, U cm ] (6)

[0078] A measuring device, such as a multimeter, may be used to measure the voltage of the accessible node.

[0079] Thus, the difference between the measured voltage of the accessible node and its corresponding calculated voltage is calculated as the target equation:

[0080]

[0081] Step 103, using the least square method to establish the initial fault diagnosis equation:

[0082]

[0083] Among them, U f It represents the calculated voltage data column, in which the number of voltage data in the data column is greater than the number of accessible nodes. The initial fault diagnosis equation is the minimum cumulative sum of the squares of the difference between the measured voltage and the calculated voltage.

[0084] Step 104: Since the number of accessible nodes of the grounding grid is less than the number of conductor branches of the grounding grid in practice, the initial fault diagnosis equation is seriously ill-conditioned and underdetermined, and there is no unique solution. Therefore, in order to effectively improve the initial fault diagnosis equation, Tikhonov regularization is used to improve the initial fault diagnosis equation, and the final fault diagnosis equation is:

[0085]

[0086] Where β is the regularization parameter (preferably 0.01), which can be obtained by L curve method or empirical method, L is the regularization coefficient matrix (here the unit matrix is ​​taken), ρ0 is the resistance value of each branch at the beginning of the iteration, and ρ is the solution to U f The resistance of each branch.

[0087] Step 200, using a particle swarm simulated annealing fusion algorithm to iteratively solve the fault diagnosis equation to obtain a global optimal solution for the resistance of each branch of the grounding grid.

[0088] Based on the above principle, a constant current can be input into the grounding grid, the voltage of the node can be measured, and then the resistance of each branch can be solved. However, due to the limited number of nodes, the resistance of each branch cannot be directly solved, so it is solved by iterative optimization. This step 200 uses a particle swarm simulated annealing fusion algorithm to solve, that is, in the process of particle swarm iteration, the idea of ​​a simulated annealing algorithm is introduced. According to the Metropolis criterion, a worse solution is accepted with a certain probability to avoid falling into a local optimal solution. At the same time, by continuously reducing the temperature parameter, the algorithm gradually converges to the global optimal solution. Figure 4 As shown, the specific solution process is as follows:

[0089] Step 201, initialize various parameters, set the number of particles in the particle swarm sizepop, each particle contains the resistance value of each branch of the entire network, and the number of iterations k max , initial temperature T0, temperature annealing coefficient α and termination temperature T1;

[0090] Step 202, using a chaotic initialization algorithm to initialize the particle swarm. The traditional particle swarm algorithm uses random initialization to obtain the initial position of the particle swarm. Although this method is simple and easy to use. However, due to the strong randomness, the distribution of the initial particle swarm may not be uniform, that is, some places are sparse and some places are dense, which can easily cause the algorithm to fall into a local optimal solution. Therefore, this embodiment uses a chaotic initialization algorithm, such as a Logistic chaotic model to initialize the particle swarm, and assigns the initial position formula of the particle swarm as follows:

[0091] x k+1 =λ·x k (1-x k )(9)

[0092] Among them, λ is the model control parameter, x k is the initial position of the particle, and λ is taken as 4 to make the distribution of the particles completely chaotic.

[0093] Step 203, determine the individual optimal solution and the group optimal solution according to formula (9), and calculate the fitness function value of the particle at the initial position. The fitness function is:

[0094]

[0095] Among them, U gi is the voltage of each node calculated by the current global optimal solution, U ci To measure the voltage of each node.

[0096] Step 204, updating the velocity and position of the particle, and calculating the fitness function value of the updated particle.

[0097] The speed update formula is:

[0098]

[0099] Among them, ω is the inertia factor, c1 and c2 represent the individual learning factor and social learning factor respectively. is the individual optimal solution of the i-th variable in the population at the k-th iteration, is the global optimal solution of the i-th variable in the population at the k-th iteration, is the particle velocity of the i-th variable in the population at the k+1th iteration, is the particle velocity of the i-th variable in the population at the k-th iteration, is the value of the i-th variable in the population at the k-th iteration.

[0100] The position update formula is:

[0101]

[0102] in, is the value of the i-th variable in the population at the k+1th iteration.

[0103] The updated particle fitness value is calculated using the fitness function shown in formula (10).

[0104] Step 205, using a linear decreasing inertia value weight strategy to adjust the inertia factor. The inertia factor ω has an important impact on the performance of the particle swarm algorithm. When the value of the inertia factor is large, it is beneficial to the global range search. When it is small, it is beneficial to the algorithm convergence speed and local convergence ability. Usually, the inertia factor of the particle swarm algorithm is a fixed value and cannot be adjusted in real time. In order to prevent the algorithm from premature convergence, this embodiment uses a linear decreasing inertia weight strategy to adjust the inertia factor. The inertia factor decreases as the number of iterations increases. At the beginning of the iteration, it mainly enhances the global search ability of the algorithm. As the number of iterations increases, the inertia factor weight decreases, which is beneficial to local search. The adjustment is as follows:

[0105]

[0106] Among them, ω start is the initial inertia weight, which is usually a larger value to facilitate global search; ω end To end the inertia weight, it is usually set to a small value to facilitate local search; iter max is the total number of iterations, and iter is the current number of iterations.

[0107] Step 206, calculate the difference between the fitness function value of the new particle and the fitness function value of the previous generation of particles. If the difference is less than 0, the position of the new particle is accepted. Otherwise, the Metropolis criterion is used to accept the new solution with a certain probability. The probability of accepting the new solution is:

[0108] p=exp(-Δf / T i )(14)

[0109] Δf=f(x i +1)-f(x i )

[0110] Among them, T i Indicates the current temperature, f(x i +1) represents the fitness function value of the new particle, f(x i ) represents the fitness function value of the previous generation of particles.

[0111] Step 207: Update the individual optimal solution and the group optimal solution.

[0112] Step 208, a cooling process, i.e., annealing operation, is performed, and the temperature is expressed as:

[0113] T i =α×T i (15)

[0114] Step 209, after each iteration, it is necessary to check whether the preset termination condition is met. If so, the iterative solution algorithm is terminated, otherwise, it returns to the step of updating the particle speed and position to perform the next iterative solution.

[0115] The preset termination conditions include:

[0116] (1) Temperature threshold determination: If the current temperature has dropped below the preset minimum temperature, it means that the algorithm has performed enough searches and stops iterating to output the current solution.

[0117] (2) Fitness threshold determination: If the fitness function value (i.e., the objective function value) of the current solution is less than or equal to the preset fitness threshold, the algorithm is considered to have found a solution that meets the requirements and no further iterative search is required. The algorithm is stopped and the current solution is output.

[0118] It should be noted that if any of the above two termination conditions is met, the iterative process of the algorithm is stopped. If the above termination conditions are not met, the algorithm returns to the step of updating the particle speed and position and performs the next iterative solution.

[0119] Step 300 , comparing the global optimal solution of each branch resistance obtained by solving the problem with its corresponding nominal resistance, and identifying the corrosion condition of each branch of the grounding grid.

[0120] In step 300, the ratio of the difference between the global optimal solution of each branch resistance and the nominal resistance of each branch is calculated to the nominal resistance, that is:

[0121]

[0122] Then, the corrosion condition of the corresponding branch is judged according to the ratio. Specifically, the judgment can be made according to the following judgment basis and the corresponding result can be output:

[0123] (1) If the calculated ratio is in the range of 0 to 1, it is mild corrosion;

[0124] (2) If the calculated ratio is in the range of 1 to 9, it is moderate corrosion;

[0125] (3) If the calculated ratio is greater than 9, it indicates severe corrosion or fracture.

[0126] This embodiment also proposes a grounding grid corrosion diagnosis system based on electrical network theory and intelligent algorithm. Figure 5 As shown, the system specifically includes:

[0127] Model building module, which uses electrical network theory to establish fault diagnosis equations for grounding grids.

[0128] Iterative solution module, which uses particle swarm simulated annealing fusion algorithm to iteratively solve the fault diagnosis equation and obtain the global optimal solution of the resistance of each branch of the grounding grid.

[0129] and, an identification module, which compares the global optimal solution of each branch resistance obtained with its corresponding nominal resistance to identify the corrosion condition of each branch of the grounding grid.

[0130] It should be noted that the specific implementation methods of the various functional modules of the above system are as described in the steps of the above method, and will not be elaborated here.

[0131] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0132] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] Example 2

[0136] This embodiment uses Multisim simulation model grounding grid (40 branches and 25 nodes), as shown in Figure 6 As shown, the branch resistances numbered 5, 8, 11, 16, 18, 21, 25, 30, and 35 are enlarged four times to simulate corrosion. It is assumed that nodes 1, 5, 6, 10, 12, 15, 18, and 20 are accessible nodes, and voltage data is obtained by simulation. In order to reflect the superiority of the particle swarm simulated annealing fusion algorithm PSO-SA proposed in the above embodiment, the genetic algorithm GA, the particle swarm algorithm PSO, and the gray wolf algorithm GWO are used as comparative examples. The specific results are shown in Figure 7-Figure 11 As shown, Figure 7 The results obtained by the PSO-SA algorithm are shown in the figure. The resistance of the branches numbered 5, 8, 11, 16, 18, 21, 25, 30, and 35 are all enlarged four times. It can be seen that there is no misdiagnosis or missed diagnosis, and the diagnosis results are consistent with the simulation setting results. The number of iterations is 1000 and the population size is 100.

[0137] Figure 8 , Fig. 9 and Fig.10 The results obtained by PSO, GWO and GA algorithms are respectively. Figure 8 It can be seen that the PSO algorithm has many misdiagnoses and is basically unable to determine the corroded branch; Fig. 9 It can be seen that the GWO algorithm can basically identify which branches are corroded, but the result is inaccurate; Fig.10 It can be seen that the GA algorithm has many misdiagnoses. For example, corrosion is not set in the simulation of 15, 20, 32, and 34, but the solution results show that these branches are corroded.

[0138] Fig.11 The iterative fitness curves of the GA algorithm, PSO algorithm, GWO algorithm and PSO-SA algorithm are shown. It can be seen from the figure that the GA algorithm and the PSO algorithm are trapped in the local optimal solution during the iteration process. The GWO algorithm and the PSO-SA algorithm have better effects, but the GWO algorithm converges too slowly, and the PSO-SA algorithm converges much faster than the GWO algorithm.

[0139] Based on this, the grounding grid corrosion diagnosis technology proposed in this embodiment uses electrical network theory to establish a fault diagnosis equation, constructs an optimization model for node voltage, and uses a hybrid simulated annealing algorithm for optimization and solution. It can quickly and accurately identify the corroded branches of the grounding grid, providing technical support and reference for the diagnosis of substation grounding grid corrosion faults.

[0140] The specific implementation methods 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 the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm, characterized in that: The method comprises: Establish the fault diagnosis equation of grounding grid using electrical network theory; The fault diagnosis equation is iteratively solved by using a particle swarm simulated annealing fusion algorithm to obtain a global optimal solution for the resistance of each branch of the grounding grid; The global optimal solution of the resistance of each branch of the grounding grid is compared with its corresponding nominal resistance to identify the corrosion condition of each branch of the grounding grid.

2. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to claim 1, characterized in that: The fault diagnosis equation of the grounding grid established by using the electrical network theory specifically includes: The grounding grid is modeled based on the electrical network method to obtain the functional relationship between voltage, current and admittance; According to the functional relationship and in combination with the initial value of the branch resistance, the voltage of each node is calculated; Injecting an excitation current into the grounding grid through a grounding down conductor, and measuring the voltage of an accessible node; Taking the difference between the measured voltage and the calculated voltage as the target, the least square method is used to establish the initial fault diagnosis equation; The initial fault diagnosis equation is corrected by Tikhonov regularization to obtain the final fault diagnosis equation.

3. The grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to claim 1 is characterized in that: The fault diagnosis equation obtained after correction is: Among them, β is the regularization parameter, L is the regularization coefficient matrix, ρ0 is the resistance of each branch at the beginning of the iteration, and ρ is the solution to U f The resistance of each branch, U c is the accessible node measurement voltage matrix, U f Compute the voltage matrix for accessible nodes.

4. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to any one of claims 1 to 3, characterized in that: The iterative solution of the fault diagnosis equation using the particle swarm simulated annealing fusion algorithm specifically includes: Initialization parameters: set the number of particles in the particle swarm, each particle contains the resistance value of each branch of the entire grounding network, the number of iterations, the initial temperature, the temperature annealing coefficient and the termination temperature; The particle swarm is initialized using the chaos initialization algorithm; Determine the individual optimal solution and the group optimal solution, and calculate the fitness function value of the particles at the initial position; Update the particle's velocity and position, and calculate the fitness function value of the updated particle; The inertia factor is adjusted by using a linear decreasing inertia value weight strategy; Calculate the difference between the fitness function value of the new particle and the fitness function value of the previous generation of particles. If the difference is less than 0, the position of the new particle is accepted. Otherwise, the Metropolis criterion is used to accept the new solution with a certain probability. Updating the individual optimal solution and the group optimal solution; Perform cooling process; After each iteration, check whether the preset termination condition is met. If so, the iterative solution algorithm is terminated, otherwise, return to the step of updating the particle speed and position to perform the next iterative solution.

5. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to claim 4, characterized in that: The speed update formula is: Among them, ω is the inertia factor, c1 and c2 represent the individual learning factor and social learning factor respectively. is the individual optimal solution of the i-th variable in the population at the k-th iteration, is the global optimal solution of the i-th variable in the population at the k-th iteration, is the particle velocity of the i-th variable in the population at the k+1th iteration, is the particle velocity of the i-th variable in the population at the k-th iteration, is the value of the i-th variable in the population at the k-th iteration; The position update formula is: in, is the value of the i-th variable in the population at the k+1th iteration.

6. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to claim 5, characterized in that: The linear decreasing inertia weight strategy is specifically: The inertia factor decreases as the number of iterations increases, and is expressed as: Among them, ω start is the initial inertia weight; ω end To end the inertia weight; iter max is the total number of iterations, and iter is the current number of iterations.

7. A grounding grid corrosion diagnosis method based on electrical network theory and intelligent algorithm according to claim 4, characterized in that: The checking whether the preset termination condition is reached is specifically: Temperature threshold determination: If the current temperature has dropped below the preset minimum temperature, it is considered that the preset termination condition has been met; Alternatively, fitness threshold determination: if the fitness function value of the current solution is less than or equal to a preset fitness threshold, it is considered that the preset termination condition has been met.

8. A grounding grid corrosion diagnosis system based on electrical network theory and intelligent algorithm, characterized in that: The system comprises: A model building module, wherein the model building module uses electrical network theory to establish a fault diagnosis equation for the grounding grid; An iterative solution module, wherein the iterative solution module adopts a particle swarm simulated annealing fusion algorithm to iteratively solve the fault diagnosis equation to obtain a global optimal solution for the resistance of each branch of the grounding grid; And, an identification module, which compares the global optimal solution of the resistance of each branch of the grounding grid with its corresponding nominal resistance to identify the corrosion status of each branch of the grounding grid.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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 steps of the method according to any one of claims 1 to 7 are implemented.

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