A grounding grid fault diagnosis method and system based on NRBO-ELT

Abstract

The application relates to a grounding net fault diagnosis method and system based on NRBO-ELT, wherein the method comprises the following steps: constructing a grounding net diagnosis model and acquiring all accessible nodes in the grounding net, calculating the calculation value of the node voltage based on the grounding net diagnosis model, and acquiring the measurement value of the accessible nodes and the initial value of the branch resistance; an adaptability function is constructed based on the measurement value and the calculation value; the change multiple of the branch resistance value relative to the initial resistance value in the diagnosis is taken as a to-be-solved parameter of the NRBO algorithm, and the change multiple is iteratively solved to obtain an optimal value; if the change multiple is greater than a preset value, the branch corresponding to the change multiple is a fault area, and the ELT technology is used for imaging the fault area; otherwise, it is indicated that the corresponding branch is not faulty, and the system mentioned herein is used for realizing the method. Compared with the prior art, the application can quickly and accurately diagnose the grounding net fault.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment maintenance, and in particular to a grounding grid fault diagnosis method and system based on NRBO-ELT. Background Technology

[0002] Because grounding grids are buried underground for extended periods, they are susceptible to soil corrosion, poor welding, and other factors, leading to a decline in grounding performance and potentially causing electrical accidents. Traditional grounding grid fault diagnosis methods, such as power outage sampling excavation, are not only labor-intensive and inefficient but can also cause prolonged power outages at substations, resulting in economic losses. Therefore, exploring an efficient and accurate grounding grid fault diagnosis method is of paramount importance. Existing technologies for fault diagnosis of substation grounding grids utilize methods such as electrical network analysis, magnetic field detection, and electrical impedance imaging. For example, Chinese patent application CN113687191A combines electrical impedance imaging and electrical network theory analysis, leveraging the sensitivity characteristics of faulty branches in the grounding grid for rapid fault detection. It only requires measuring port resistance, eliminating the need to measure branch resistance or solve equations. While electrical impedance imaging of the fault area and soil separation for imaging corroded areas avoid the cumbersome process of measuring branch resistance, the sheer volume of data detected during grounding grid fault diagnosis, coupled with complex processing, leads to poor accuracy and inherent limitations. Furthermore, it cannot provide accurate fault diagnosis and analysis for substation grounding grids.

[0003] Therefore, providing a method for quickly and accurately diagnosing grounding grid faults is a technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a grounding grid fault diagnosis method and system based on NRBO-ELT. The NRBO algorithm is introduced to solve the model. Through iterative calculation, potential fault points in the grounding grid can be located quickly and accurately. Furthermore, ELT technology is combined to obtain the voltage and current distribution information on the surface of the grounding grid using a non-invasive and non-destructive measurement method to construct an impedance image, which intuitively displays the resistivity distribution inside the grounding grid for fault analysis.

[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 fault diagnosis method based on NRBO-ELT is provided, the method comprising:

[0007] A grounding grid diagnostic model is constructed and all accessible nodes in the grounding grid are obtained. Based on the grounding grid diagnostic model, the calculated values ​​of node voltages are calculated, and the measured values ​​of accessible nodes and the initial values ​​of branch resistances are obtained.

[0008] A fitness function is constructed based on the measured and calculated values;

[0009] The change factor of the branch resistance value in the diagnosis relative to the initial resistance value is used as the parameter to be solved by the NRBO algorithm, and the optimal value of the change factor is obtained by iterative solution.

[0010] If the change factor is greater than a preset value, the branch corresponding to the change factor is a fault area, and the fault area is imaged using ELT technology; otherwise, it indicates that the corresponding branch is not faulty.

[0011] As a preferred technical solution, the method further includes injecting DC excitation current into the grounding grid using multiple alternating excitation measurements to obtain the measured values, including:

[0012] A1. Select any two accessible nodes from all the accessible nodes as the inflow and outflow ends of the DC excitation current.

[0013] A2. Inject the DC excitation current from the inflow end and out from the outflow end, and measure the node voltage of all accessible nodes under this excitation mode.

[0014] A3. Select two more reachable nodes and repeat steps A1 to A2 until the termination condition is met.

[0015] As a preferred technical solution, the expression for the fitness function is:

[0016]

[0017] Among them, U pi U represents the measured voltage at node i; qi η represents the calculated voltage at node i; η represents the number of reachable nodes.

[0018] As a preferred technical solution, the method for obtaining the branch resistance value change using the NRBO algorithm includes:

[0019] Obtain the boundary of the change factor of the branch resistance value, and randomly generate an initial population within the boundary. The position of each individual in the initial population represents a candidate solution of the change factor of the branch resistance value.

[0020] Based on the initial population, perform the following steps iteratively until the iteration termination condition is met:

[0021] Calculate the fitness value of each individual in the population, and obtain the best fitness value and the minimum fitness value, as well as their corresponding optimal and worst solutions;

[0022] Calculate the adaptive coefficients, search step size, and direction guidance parameters of the NRBO algorithm;

[0023] The root position is calculated based on the search step size, and the first intermediate position and the second intermediate position are calculated based on the root position and the direction guidance parameters. The position of the individual is initially updated based on the first intermediate position and the second intermediate position to obtain the candidate position.

[0024] Based on the aforementioned adaptive coefficients, the candidate positions are optimized using TAO to obtain the final position of the individual, the fitness function corresponding to the final position is calculated, and the optimal or worst solution is updated.

[0025] As a preferred technical solution, the method for calculating the adaptive coefficients, search step size, and direction guidance parameters of the NRBO algorithm is as follows:

[0026] Adaptive coefficients: Where IT represents the current iteration number; Max_IT represents the maximum iteration number; δ represents the adaptive coefficient, and δ∈[-1, 1];

[0027] Search step size: Where rand represents generating random numbers; (1, dim) represents one row; X b Indicates the optimal solution; This indicates the position of individual n at the current iteration number IT;

[0028] Directional guidance parameters: Where a and b represent random numbers between (0, 1); This indicates the position of an individual r1 randomly selected from the population; This represents the position of individual r2 randomly selected from the population; X b This represents the optimal solution.

[0029] As a preferred technical solution, the method for obtaining the candidate positions is as follows:

[0030] The root position is derived using Newton's method based on the updated individual positions, and its expression is:

[0031]

[0032] Where, x n+1 This represents the root position of the nth individual in the current iteration; The position of the nth individual in the current iteration is represented by Δx; Δx represents the search step size.

[0033] The first intermediate value is calculated based on the root position, and its expression is: Where ρ represents the direction guidance parameter;

[0034] The second intermediate value is calculated based on the optimal solution, and its expression is as follows: Among them, X b Represents the optimal solution; NRSR represents the NRSR operator;

[0035] The candidate positions are calculated using the following expression:

[0036]

[0037] Where c represents the weighting coefficient; δ represents the adaptive coefficient; This indicates the position of individual n in the current iteration.

[0038] As a preferred technical solution, the method for obtaining the final position is as follows:

[0039]

[0040] in, θ1 represents the final position of the individual in the (IT+1)th iteration after optimization; θ2 represents a uniformly random number in (-1, 1); μ1 and μ2 represent random numbers; δ represents the adaptive parameter. Indicates the candidate position; X b Indicates the optimal solution; This indicates the position of individual n in the current iteration.

[0041] As a preferred technical solution, the method for imaging the fault area includes:

[0042] A fault region is constructed using the impedance method for diagnosis. A multi-frequency alternating current is applied to the excitation terminal of the impedance method diagnostic model, and the boundary voltage of the fault region is measured. A forward problem mathematical model is then constructed, with the expression: V = U(σ) + ε, where V represents the boundary voltage; U(σ) represents the nonlinear forward operator; and ε represents the measurement noise.

[0043] The conductivity distribution, current magnitude, and boundary voltage of the fault region under the multi-frequency AC current excitation are obtained. Based on the conductivity distribution, current magnitude, and boundary voltage, the mathematical model of the forward problem is solved to obtain the potential distribution.

[0044] Inject a low-frequency current into the fault region, reconstruct the resistivity distribution under the excitation of the low-frequency current, and construct the objective function of the inverse problem.

[0045] The magnitude of the low-frequency current and the node location where it is injected into the fault region are obtained, and the resistivity distribution is iteratively solved based on the objective function of the inverse problem in combination with the potential distribution.

[0046] Imaging is performed based on the aforementioned resistivity distribution.

[0047] As a preferred technical solution, the objective function of the inverse problem is:

[0048]

[0049] Where U(ρ) represents the calculated electrode voltage; V represents the measured electrode voltage; α represents the regularization parameter; L represents the regularization identity matrix; ρ represents the resistivity distribution; and ρ0 represents the initial resistivity distribution.

[0050] According to a second aspect of the present invention, a grounding grid fault diagnosis system based on NRBO-ELT is provided for implementing the above-described method.

[0051] Compared with existing technologies, this invention provides a grounding grid fault diagnosis method based on NRBO-ELT. Compared with existing methods that rely solely on ELT technology for fault imaging, this application first uses the NRBO algorithm to locate corrosion in the face of complex and ever-changing grounding grid environments. By optimizing the algorithm to solve for the branch resistance change factor, the complex mathematical model solution process is avoided, and the corrosion point is located quickly and efficiently. Then, ELT technology is used to perform impedance imaging on the grounding grid section with corrosion to facilitate visual observation. This method is more accurate and comprehensive than existing grounding grid fault monitoring methods. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method of the present invention;

[0053] Figure 2 This is a schematic diagram of the EIT principle in this invention;

[0054] Figure 3 This is the grounding grid model in Embodiment 2 of the present invention;

[0055] Figure 4 This is a topology diagram of the grounding grid in Embodiment 2 of the present invention;

[0056] Figure 5 This is a diagram showing the diagnostic results in Embodiment 2 of the present invention. Detailed Implementation

[0057] 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.

[0058] 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.

[0059] Example 1

[0060] To address the problems existing in current technologies, this invention proposes a grounding grid fault diagnosis method based on NRBO-ELT, cleverly integrating the electric network method based on the Newton-Raphson optimization algorithm (NRBO) with electrical impedance imaging (EIT) technology. The NRBO algorithm is introduced to solve the model, and through iterative calculations, potential fault points in the grounding grid are quickly and accurately located. Furthermore, this invention combines EIT technology, using a non-invasive and non-destructive measurement method to visually display the resistivity distribution within the grounding grid. This not only improves the accuracy and reliability of the diagnosis but also reduces the cost and risk in the diagnostic process, providing strong support for the safe and stable operation of the power system. It also significantly improves maintenance efficiency.

[0061] Its process is as follows Figure 1 As shown, it includes:

[0062] S1. Construct a grounding grid diagnostic model and obtain all accessible nodes in the grounding grid. Calculate the node voltage based on the grounding grid diagnostic model. Inject DC excitation current into the accessible nodes using a multiple rotation excitation measurement method to obtain the measured values ​​of the accessible nodes and obtain the initial value of the branch resistance.

[0063] 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.

[0064] The following equations can be constructed:

[0065] Y n =AY b A -1 U n =A -1 I n ,

[0066] Among them, Y n Represents the node impedance matrix; Y b U represents the branch impedance matrix; A represents the node-branch correlation matrix, which is an n×b dimensional matrix. If a node is positively correlated with a branch, the corresponding element in the matrix is ​​+1; if a node is negatively correlated with a branch, the corresponding element in the matrix is ​​-1; if a node is uncorrelated with a branch, the corresponding element in the matrix is ​​0; U n Represents the node voltage matrix; I n The node current matrix is ​​represented as an (n-1)×k dimensional matrix if there are k excitation modes during the test. The injected current from the current source is represented as I. The elements of the matrix are: +1 for injected nodes, -1 for outflowing nodes, and 0 for all other nodes.

[0067] Under the first 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) excitation mode, the following formula exists:

[0068]

[0069] in, Let represent the column vector of node currents under the e-th type of excitation.

[0070] The detailed process includes:

[0071] A1. Select any two accessible nodes from all accessible nodes as the inflow and outflow ends of the DC excitation current.

[0072] A2. Inject the DC excitation current from the inflow end and let it flow out from the outflow end, and measure the node voltage of all accessible nodes under this excitation mode.

[0073] A3. Select two more reachable nodes and repeat steps A1 to A2 until the termination condition is met.

[0074] S2. The change factor of the branch resistance value relative to the initial resistance value is used as the parameter to be solved. A fitness function is constructed based on the measured and calculated values, and the optimal value of the change factor is obtained by using the NRBO algorithm.

[0075] S21. Construct the fitness function.

[0076]

[0077] Among them, U pi U represents the measured voltage at node i; qi η represents the calculated voltage at node i; η represents the number of reachable nodes.

[0078] S22. Solve for the optimal value.

[0079] S221. Obtain the boundary of the change factor of the branch resistance value, and randomly generate an initial population within the boundary. The position of each individual in the initial population represents a candidate solution of the change factor of the branch resistance value.

[0080] The detailed method for generating a random population is as follows:

[0081]

[0082] in, Let represent the j-th dimension position of the nth individual; lb represents the upper bound of the parameters to be solved; ub represents the lower bound of the parameters to be solved; N p Indicates the total population size.

[0083] Then for a population, the following exists:

[0084]

[0085] Among them, X n The matrix representing the individuals of the nth population; This represents the vector x, where each element is processed by N. p Exponentiation.

[0086] Based on the initial population, iteratively execute the following steps until the iteration termination condition is met:

[0087] S222. Calculate the fitness value of each individual in the population, and obtain the best fitness value and the minimum fitness value, as well as the corresponding optimal solution and worst solution.

[0088] S223. Calculate the adaptive coefficients, search step size, and direction guidance parameters of the NRBO algorithm.

[0089] Adaptive coefficients: Where IT represents the current iteration number; Max_IT represents the maximum iteration number; δ represents the adaptive coefficient, and δ∈[-1, 1];

[0090] Search step size: Where rand represents generating random numbers; (1, dim) represents one row; X b Indicates the optimal solution; This indicates the position of individual n at the current iteration number IT;

[0091] Directional guidance parameters: Where a and b represent random numbers between (0, 1); This indicates the position of an individual r1 randomly selected from the population; This represents the position of an individual r2 randomly selected from the population; X b This represents the optimal solution.

[0092] S224. Calculate the root position based on the search step size, and calculate the first intermediate position and the second intermediate position based on the root position and the direction guidance parameters. Perform an initial update on the individual's position based on the first intermediate position and the second intermediate position to obtain the candidate position.

[0093] i. The root position is derived by using Newton's method based on the updated individual position.

[0094] Based on Newton's method, the solution process includes:

[0095]

[0096] Where Δx represents the search step size.

[0097] Adding and subtracting the two equations above, we obtain the first and second derivatives, which are expressed as follows:

[0098]

[0099] Based on the above solution, the root position is updated, and the expression is as follows:

[0100]

[0101] Where, r n+1 This represents the root position of the nth individual in the current iteration; Δx represents the position of the nth individual in the current iteration; Δx represents the search step size.

[0102] ii. Calculate the first intermediate value based on the root position, its expression is: Where ρ represents the directional guidance parameter.

[0103] iii. Calculate the second intermediate value based on the optimal solution, and its expression is: Among them, X b represents the optimal solution; NRSR represents the NRSR operator.

[0104] iiii. Calculate the candidate positions, the expression is:

[0105]

[0106] Where c represents the weighting coefficient; δ represents the adaptive coefficient; This indicates the position of individual n in the current iteration.

[0107] S225. Based on the adaptive coefficient, the candidate positions are optimized using TAO to obtain the final position of the individual, the fitness function corresponding to the final position is calculated, and the optimal or worst solution is updated.

[0108] Specifically,

[0109]

[0110] in, θ1 represents the final position of the individual in the (IT+1)th iteration after optimization; θ2 represents a uniformly random number in (-1, 1); μ1 and μ2 represent random numbers; δ represents the adaptive parameter. Indicates the candidate position; X b Indicates the optimal solution; This indicates the position of individual n in the current iteration.

[0111] S3. Based on the optimal value of the change factor, the fault area is divided, and ELT technology is used to image the fault area to obtain the corrosion status of each branch in the fault area.

[0112] The establishment of the electrical impedance method (ELT) diagnostic model is a systematic project that integrates electromagnetic field theory, inverse problem solving, and optimization algorithms. Its principle is as follows: Figure 2As shown, the core lies in constructing a complete mapping relationship from the internal conductivity distribution of the measured object to the measurable potential response at the boundary, and developing a stable and efficient parameter inversion method. The model construction is first based on Maxwell's equations, simplified to a boundary value problem of the Laplace equation under a quasi-static approximation. The continuous medium is discretized into a three-dimensional mesh system containing N elements using the finite element method (FEM), with each element assigned an initial conductivity, forming a parameter vector σ∈R^N. A multi-frequency alternating current (typically 1kHz-1MHz) is applied to the excitation end, and the boundary voltage V∈R^M is measured using a surface electrode array. A forward problem mathematical model is established: V=U(σ)+ε, where U(·) is the nonlinear forward operator and ε is the measurement noise. To overcome the ill-conditioned inverse problem during the solution process, a two-layer optimization framework is constructed. The first layer uses an improved Tikhonov regularization method to transform the inversion problem into minimizing the objective function. The second layer introduces a genetic algorithm-particle swarm optimization strategy to globally search for the optimal solution in the parameter space: maintaining population diversity through adaptive mutation probability, combined with the sensitivity matrix... The singular value decomposition (SVD) achieves local fine-grained search. During the iteration process, the Morozov bias principle is used to dynamically shrink the search radius to ensure convergence to the physically feasible solution space.

[0113] Details include:

[0114] S31. Construct a fault region electrical impedance method diagnostic model, apply a multi-frequency AC current to the excitation terminal of the electrical impedance method diagnostic model, measure the boundary voltage of the fault region, and construct a forward problem mathematical model, which is expressed as: V=U(σ)+ε, where V represents the boundary voltage; U(σ) represents the nonlinear forward operator; and ε represents the measurement noise.

[0115] Specifically, in conducting forward impedance analysis, a certain number of electrodes are installed on the object under test, and a low-frequency current is applied to it to create a current field. The forward impedance imaging problem calculates the potential distribution within the field given the conductivity distribution, current injection location and magnitude, and boundary potential. Based on the differential form of Ohm's law:

[0116] J = σE,

[0117] Where J represents the current density applied to the surface of the grounding grid, σ represents the conductivity, and E represents the electric field strength.

[0118] Since the electric field strength E does not change with time during the instantaneous measurement, and there is no current source inside the volume, based on the continuity of current, we have:

[0119]

[0120] in, This represents the vector differentiation operator.

[0121] The relationship between electric field strength and electric potential is as follows:

[0122]

[0123] in, This indicates the field potential distribution in the grounding grid.

[0124] Therefore, the mathematical description of the positive problem is: Current injection and voltage measurement are performed by applying appropriate boundary conditions, where the boundary conditions are: denoted by Ω, j represents the current density injected into the imaging region Ω through the electrode, and ψ(x,y) represents the boundary potential for any point (x,y).

[0125] Based on the above conditions, the solution model for the positive problem can be constructed as: V=U(σ)+ε.

[0126] S32. Obtain the conductivity distribution, current magnitude, and boundary voltage of the fault area under multi-frequency AC current excitation. Solve the mathematical model of the forward problem based on the conductivity distribution, current magnitude, and boundary voltage to obtain the potential distribution.

[0127] S33. Inject low-frequency current into the fault region, reconstruct the resistivity distribution under the excitation of low-frequency current, and construct the objective function of the inverse problem.

[0128] The inverse problem computational model involves injecting a low-frequency current I into the field and reconstructing the resistivity distribution ρ or its variation within the field based on the measured electrode potentials. Given the location and magnitude of the injected excitation current and the electrode potentials, the resistivity distribution within the field can be calculated in reverse.

[0129] A certain current excitation is applied to the i-th pair of electrodes, and the voltage value V of the j-th pair of electrodes is measured. ij Where i,j=1,2,…,N, and N represents the number of electrodes, the voltage V is measured. ij To calculate the voltage at each electrode, we can use the resistivity of the field to determine the voltage U at each electrode when the resistivity is ρ. ij (ρ),i,j=1,2,…,N and: U ij =V ij V ij U represents the measured voltage of the j-th electrode when a current excitation is applied to the i-th electrode pair; ij This represents the calculated voltage corresponding to the j-th electrode when a current excitation is applied to the i-th electrode pair.

[0130] However, the calculated voltage and the measured voltage are often not exactly equal, and there are various errors between them, making the above equation difficult to achieve. Generally, the error function is constructed using the least squares method:

[0131]

[0132] Finding the resistivity distribution ρ that minimizes E(ρ) involves ill-conditioned problems in the solution process. Therefore, a regularization penalty factor is introduced to correct this, leading to the derivation of the inverse problem model based on the modified Newton-Raphson (MNR) method. The objective function can be expressed as:

[0133]

[0134] Where U(ρ) represents the calculated electrode voltage; V represents the measured electrode voltage; α represents the regularization parameter; L represents the regularization identity matrix; ρ represents the resistivity distribution; and ρ0 represents the initial resistivity distribution.

[0135] S34. Obtain the magnitude of the low-frequency current and the node location of its injection into the fault region, and combine the potential distribution to iteratively solve the resistivity distribution based on the objective function of the inverse problem.

[0136] In detail, by performing a Taylor expansion of the objective function at ρ(k), taking the extreme values ​​of the Taylor series at the extreme points, and then substituting the first and second derivatives of the function f, we can obtain the iterative solution format as follows:

[0137] ρ (k+1) =ρ (k) -(J T J+α1L T L) -1 [J T (U(ρ)-V)+α1L T L(ρ (k) -ρ 0 )],

[0138] Where, ρ (k+1) ρ represents the result of the (k+1)th resistivity iteration; (k) denoted by ; J represents the partial derivative matrix of the calculated voltage with respect to resistivity; L represents the regularization identity matrix.

[0139] The resistivity distribution is obtained by solving the inverse problem based on the above iterative solution scheme.

[0140] S35, Imaging based on resistivity distribution.

[0141] Example 2

[0142] To verify the feasibility of the method provided in the above embodiments, this embodiment employs the following... Figure 3 The grounding grid shown is verified, including:

[0143] S1, Construction Figure 3 The grounding grid diagnostic model shown is as follows: Figure 4 As shown, all accessible nodes in the grounding grid are obtained. DC excitation current is injected into the accessible nodes by means of multiple rotation excitation measurements. The calculated value of the node voltage is calculated based on the grounding grid diagnostic model, and the measured value of the accessible node and the initial value of the branch resistance are obtained.

[0144] Furthermore, in this embodiment, it is assumed that branch resistors R3, R29, R36, and R40 are subject to corrosion, such as Figure 4 The branch resistance from the middle dashed coil.

[0145] S2. The change factor of the branch resistance value relative to the initial resistance value during diagnosis is used as the parameter to be solved. A fitness function is constructed based on the measured and calculated values, and the optimal value of the change factor is obtained by using the NRBO algorithm. The result is as follows: Figure 5 As shown.

[0146] S3. Based on the optimal value of the change factor, the fault area is divided, and ELT technology is used to image the fault area to obtain the corrosion status of each branch in the fault area.

[0147] from Figure 5 As can be seen, the branch resistors R3, R29, R36, and R40 have changed compared to their initial values, meaning that R3, R29, R36, and R40 have been corroded, which is the same as the initial condition. Therefore, the method provided by this invention is feasible.

[0148] Example 3

[0149] This invention also provides a grounding grid fault diagnosis system based on NRBO-ELT, comprising: a central processing unit (CPU), which can execute various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 fault diagnosis method based on NRBO-ELT, characterized in that, The method includes: A grounding grid diagnostic model is constructed and all accessible nodes in the grounding grid are obtained. Based on the grounding grid diagnostic model, the calculated values ​​of node voltages are calculated, and the measured values ​​of accessible nodes and initial values ​​of branch resistances are obtained. A DC excitation current is injected into the grounding grid using multiple alternating excitation measurements to obtain the measured values, including: A1. Select any two accessible nodes from all the accessible nodes as the inflow and outflow ends of the DC excitation current. A2. Inject the DC excitation current from the inflow end and out from the outflow end, and measure the node voltage of all accessible nodes under this excitation mode. A3. Select two more reachable nodes and repeat steps A1 to A2 until the termination condition is met; A fitness function is constructed based on the measured and calculated values; the expression for the fitness function is: ， in, This represents the measured voltage value at node i; This represents the calculated value of the voltage at node i; Indicates the number of reachable nodes; The factor by which the branch resistance value changes relative to the initial resistance value during diagnosis is used as the parameter to be solved by the NRBO algorithm, and the optimal value of the factor by change is obtained through iterative solution. Methods for obtaining the change in branch resistance value using the NRBO algorithm include: Obtain the boundary of the change factor of the branch resistance value, and randomly generate an initial population within the boundary. The position of each individual in the initial population represents a candidate solution of the change factor of the branch resistance value. Based on the initial population, perform the following steps iteratively until the iteration termination condition is met: Calculate the fitness value of each individual in the population, and obtain the best fitness value and the minimum fitness value, as well as their corresponding optimal and worst solutions; The method for calculating the adaptive coefficients, search step size, and direction guidance parameters of the NRBO algorithm is as follows: Adaptive coefficients: ,in, Indicates the current iteration number; Indicates the maximum number of iterations; Represents the adaptive coefficient, and ; Search step size: ,in, This indicates generating a random number; (1, () represents a vector with 1 row and dim columns; Indicates the optimal solution; This indicates that individual n is in the current iteration number. The position at that time; Directional guidance parameters: ,in, and express Random numbers between; This refers to an individual randomly selected from the population. Location; Indicates an individual randomly selected from the population. Location; Indicates the optimal solution; The root position is calculated based on the search step size, and the first intermediate position and the second intermediate position are calculated based on the root position and the direction guidance parameters. The individual's position is initially updated based on the first intermediate position and the second intermediate position to obtain candidate positions. The method for obtaining the candidate positions is as follows: The root position is derived using Newton's method based on the updated individual positions, and its expression is: ， in, This represents the root position of the nth individual in the current iteration; This indicates the position of the nth individual in the current iteration; Indicates the search step size; The first intermediate value is calculated based on the aforementioned root position, and its expression is as follows: ,in, Indicates the direction guidance parameters; The second intermediate value is calculated based on the aforementioned optimal solution, and its expression is as follows: ,in, Indicates the optimal solution; express Operator; The candidate positions are calculated using the following expression: ， in, Indicates the weighting coefficient; Indicates the adaptive coefficient; This indicates the position of individual n in the current iteration. Based on the aforementioned adaptive coefficients, the candidate positions are optimized using TAO to obtain the final position of the individual. The fitness function corresponding to the final position is calculated, and the optimal or worst solution is updated. The method for obtaining the final position is as follows: in, This represents the final position of the individual in the (IT+1)th iteration after optimization; express Uniform random numbers; express Uniform random numbers; and Represents a random number; Indicates adaptive parameters; Indicates the candidate position; Indicates the optimal solution; This indicates the position of individual n in the current iteration; If the change factor is greater than a preset value, the branch corresponding to the change factor is a fault area, and the fault area is imaged using ELT technology; otherwise, it indicates that the corresponding branch is not faulty.

2. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 1, characterized in that, The method for imaging the fault area includes: A fault region diagnostic model using the impedance method is constructed. A multi-frequency AC current is applied to the excitation terminal of the impedance method diagnostic model, the boundary voltage of the fault region is measured, and a forward problem mathematical model is constructed, with the expression: ,in, Indicates the boundary voltage; Represents a nonlinear forward operator; Indicates measurement noise; The conductivity distribution, current magnitude, and boundary voltage of the fault region under the multi-frequency AC current excitation are obtained. Based on the conductivity distribution, current magnitude, and boundary voltage, the mathematical model of the forward problem is solved to obtain the potential distribution. Inject a low-frequency current into the fault region, reconstruct the resistivity distribution under the excitation of the low-frequency current, and construct the objective function of the inverse problem. The magnitude of the low-frequency current and the node location where it is injected into the fault region are obtained, and the resistivity distribution is iteratively solved based on the objective function of the inverse problem in combination with the potential distribution. Imaging is performed based on the aforementioned resistivity distribution.

3. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 2, characterized in that, The objective function of the inverse problem is: ， in, This represents the calculated value of the electrode voltage; Indicates the measured value of electrode voltage; Represents the regularization parameter; Represents the regularized identity matrix; Represents the resistivity distribution; This represents the initial resistivity distribution.

4. A grounding grid fault diagnosis system based on NRBO-ELT, characterized in that, The system is used to implement the method as described in any one of claims 1 to 3.

Images