Grounding grid fault diagnosis method and system based on NRBO-ELT

Abstract

The invention relates to a grounding grid fault diagnosis method and system based on NRBO-ELT, and the method comprises the steps: constructing a grounding grid diagnosis model, obtaining all accessible nodes in a grounding grid, calculating the calculation value of node voltage based on the grounding grid diagnosis model, and obtaining the measurement value of the accessible nodes and the initial value of branch resistance; constructing a fitness function based on the measured value and the calculated value; taking the change multiple of the branch resistance value relative to the initial resistance value in diagnosis as a to-be-solved parameter of the NRBO algorithm, and performing iterative solution to obtain an optimal value of the change multiple; if the change multiple is greater than a preset value, determining that the branch corresponding to the change multiple is a fault area, and imaging the fault area by adopting an ELT technology; otherwise, it is indicated that the corresponding branch does not break down, and the system is used for achieving the method. Compared with the prior art, the grounding grid fault can be quickly and accurately diagnosed.

Description

Technical Field

[0001] The present 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 Art

[0002] Because grounding grids are buried underground for long periods of time, they are susceptible to factors such as soil corrosion and poor welding, which can degrade grounding performance and, in severe cases, cause electrical accidents. Traditional methods for diagnosing grounding grid faults, such as sampling and excavation during power outages, are not only labor-intensive and inefficient, but can also cause prolonged power outages at substations, resulting in economic losses. Therefore, developing an efficient and accurate method for diagnosing grounding grid faults is crucial. Existing technologies use electrical network methods, magnetic field detection methods, and electrical impedance imaging methods to diagnose faults in substation grounding networks. For example, China's patent application "CN113687191A" combines electrical impedance imaging and electrical network theoretical analysis methods, and utilizes the sensitivity characteristics of the faulty branches of the grounding network to quickly detect faults in the grounding network. Only the port resistance needs to be measured, without the need to measure the branch resistance and solve a set of equations. Electrical impedance imaging is performed on the fault area, and soil separation is used to image the corrosion site. Although this avoids the complicated process of measuring branch resistance, measuring only the port resistance simplifies the workload when diagnosing grounding network faults, but the detected data is complex, and therefore the processing process is complicated, resulting in poor accuracy. This has its own limitations and cannot accurately diagnose and analyze the substation grounding network.

[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 the present invention is to overcome the defects of the above-mentioned prior art and provide a grounding grid fault diagnosis method and system based on NRBO-ELT. The NRBO algorithm is introduced to solve the model, and through iterative calculation, the potential fault points in the grounding grid are quickly and accurately located. Further combined with ELT technology, a non-invasive and non-destructive measurement method is used to obtain the voltage and current distribution information on the surface of the grounding grid for constructing an electrical impedance image, which intuitively displays the resistivity distribution inside the grounding grid for fault analysis.

[0005] The purpose of the present invention can be achieved by 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] Constructing a grounding network diagnostic model and obtaining all accessible nodes in the grounding network, calculating node voltage values ​​based on the grounding network diagnostic model, and obtaining measured values ​​of accessible nodes and initial branch resistance values;

[0008] constructing a fitness function based on the measured values ​​and the calculated values;

[0009] The change multiple 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 multiple is obtained by iterative solution;

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

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

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

[0013] A2. injecting the DC excitation current from the inflow end and allowing it to flow out from the outflow end, and measuring the node voltages of all accessible nodes under this excitation mode;

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

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

[0016]

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

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

[0019] Obtaining a boundary of the variation multiple of the branch resistance value, and randomly generating an initial population within the boundary, wherein the position of each individual in the initial population represents a candidate solution for the variation multiple of the branch resistance value;

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

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

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

[0023] Calculating a root position based on the search step, calculating a first intermediate position and a second intermediate position based on the root position and the direction guidance parameter, and performing an initial update on the position of the individual based on the first intermediate position and the second intermediate position to obtain a candidate position;

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

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

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

[0027] Search step size: Among them, rand means taking a random number; (1, dim means 1 row; X b represents the optimal solution; Indicates the position of individual n at the current iteration number IT;

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

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

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

[0031]

[0032] Among them, x n+1 Indicates the root position of the nth individual at the current iteration; Indicates the position of the nth individual in the current iteration; Δx represents the search step length;

[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: Among them, X b represents the optimal solution; NRSR represents the NRSR operator;

[0035] Calculate the candidate position, the expression is:

[0036]

[0037] Where c represents the weight coefficient; δ represents the adaptive coefficient; Indicates the position of individual n at the current iteration.

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

[0039]

[0040] in, represents the final position of the individual at the IT+1th iteration after optimization; θ1 represents a uniform random number of (-1, 1); θ2 represents a uniform random number of (-0.5, 0.5); μ1 and μ2 represent random numbers; δ represents an adaptive parameter; represents the candidate position; X b represents the optimal solution; 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] An electrical impedance method diagnostic model of the fault area is constructed, a multi-frequency AC current is applied to the excitation end of the electrical impedance method diagnostic model, the boundary voltage of the fault area is measured, and a forward problem mathematical model is constructed, 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;

[0043] Obtaining conductivity distribution, current magnitude, and boundary voltage of the fault area under the multi-frequency AC current excitation, and solving a forward problem mathematical model based on the conductivity distribution, current magnitude, and boundary voltage to obtain a potential distribution;

[0044] injecting a low-frequency current into the fault area, reconstructing the resistivity distribution under the excitation of the low-frequency current, and constructing an inverse problem objective function;

[0045] Obtaining the magnitude of the low-frequency current and the node position where the low-frequency current is injected into the fault area, and iteratively solving the resistivity distribution based on the inverse problem objective function in combination with the potential distribution;

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

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

[0048]

[0049] Where U(ρ) represents the calculated value of the electrode voltage; V represents the measured value of the electrode voltage; α represents the regularization parameter; L represents the regularized unit 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 method.

[0051] Compared with the existing technology, the present invention provides a grounding grid fault diagnosis method based on NRBO-ELT. Compared with the method in the existing technology that only relies on ELT technology for fault imaging, when facing a complex and changeable grounding grid environment, the present application first adopts the NRBO algorithm to locate corrosion, and solves the branch resistance change multiple through the optimization algorithm, avoiding the complicated mathematical model solution process, and realizing fast and efficient positioning of the corrosion point; then, the ELT technology is used to perform electrical impedance imaging on the grounding grid part where corrosion is located, so as to realize visual observation, which is more accurate and comprehensive than the existing grounding grid fault monitoring method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method of the present invention;

[0053] Figure 2 This is the EIT principle diagram of the present invention;

[0054] Figure 3 The grounding grid model in embodiment 2 of the present invention;

[0055] Figure 4 This is a topological diagram of the grounding network in Example 2 of the present invention;

[0056] Figure 5 This is a diagram of the diagnosis results in Example 2 of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0058] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; 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 that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0059] Example 1

[0060] To address the problems existing in the prior art, the present invention proposes a grounding grid fault diagnosis method based on NRBO-ELT, which cleverly combines the electrical network method based on the Newton-Raphson optimization algorithm (NRBO) with electrical impedance tomography (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. On this basis, the present invention further combines EIT technology and uses a non-invasive, non-destructive measurement method to intuitively display the resistivity distribution within the grounding grid. This not only improves the accuracy and reliability of diagnosis, but also reduces the cost and risk of the diagnostic process, providing a strong guarantee for the safe and stable operation of the power system. This significantly improves maintenance efficiency.

[0061] The process is as follows Figure 1 Shown, including:

[0062] S1. Build 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. Use multiple rotation excitation measurements to inject DC excitation current into the accessible nodes to obtain the measurement values ​​of the accessible nodes and obtain the initial value of the branch resistance.

[0063] Since the number of accessible nodes is much smaller than the total number of nodes, there is pathological in the solution process, and the result is difficult to converge to the true value or even does not converge. It is necessary to expand the data volume by measuring the voltage values ​​under multiple excitation methods to improve the pathological problems of the electrical network method.

[0064] The equation can be constructed as follows:

[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 represents the branch impedance matrix; A represents the node-branch correlation matrix, which is an n×b dimensional matrix. If the node is positively correlated with the branch, the corresponding element in the matrix is ​​+1; if the node is negatively correlated with the branch, the corresponding element in the matrix is ​​-1; if the node is uncorrelated with the branch, the corresponding element in the matrix is ​​0; U n represents the node voltage matrix; I n Represents the node current matrix. If there are k excitation modes during the test, it is an (n-1)×k dimensional matrix. The current injected by the current source is represented by I. The elements of the matrix are: the injection node represents +1, the outflow node represents -1, and the remaining nodes represent 0.

[0067] In the Ith excitation mode, the node-branch correlation matrix and the initial branch resistance are known. The potential of each node in this excitation mode can be calculated based on electrical network theory. For the eth (e = 1, 2, 3, ... I) excitation mode, the following formula exists:

[0068]

[0069] in, Represents the node current column vector under the e-th excitation.

[0070] In detail, the whole process includes:

[0071] A1. Randomly select 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. Reselect two other reachable nodes and repeat steps A1 to A2 until the termination condition is met.

[0074] S2. The change ratio of the branch resistance value in the diagnosis relative to the initial resistance value is used as the parameter to be solved. A fitness function is constructed based on the measured value and the calculated value, and the NRBO algorithm is used to solve the problem and obtain the optimal value of the change ratio.

[0075] S21. Construct a fitness function.

[0076]

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

[0078] S22. Find the optimal value.

[0079] S221 . Obtain a boundary of the variation multiple 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 for the variation multiple of the branch resistance value.

[0080] In detail, the method for generating random population is:

[0081]

[0082] in, represents the j-th dimension position of the n-th individual; lb represents the upper limit of the parameter to be solved; ub represents the lower limit of the parameter to be solved; N p Indicates the total population.

[0083] Then for a population there exists:

[0084]

[0085] Among them, X n represents the individual matrix of the nth population; Each element of vector x is N p Power operation.

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

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

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

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

[0090] Search step size: Among them, rand means taking a random number; (1, dim means 1 row; X b represents the optimal solution; Indicates the position of individual n at the current iteration number IT;

[0091] Direction guidance parameters: Where a and b represent random numbers between (0, 1); represents the position of individual r1 randomly selected from the population; represents the position of individual r2 randomly selected from the population; X b Indicates 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 parameter. Perform an initial update on the individual position based on the first intermediate position and the second intermediate position to obtain a candidate position.

[0093] i. Use Newton's method to derive the root position based on the updated individual positions.

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

[0095]

[0096] Where Δx represents the search step size.

[0097] Adding and subtracting the above two equations to obtain the first-order derivative and the second-order derivative, the expressions are:

[0098]

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

[0100]

[0101] Among them, r n+1 Indicates the root position of the nth individual at the current iteration; represents the position of the nth individual in the current iteration; Δx represents the search step length.

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

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

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

[0105]

[0106] Where c represents the weight coefficient; δ represents the adaptive coefficient; Indicates the position of individual n at 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 solution or the worst solution is updated.

[0108] Specifically,

[0109]

[0110] in, represents the final position of the individual at the IT+1th iteration after optimization; θ1 represents a uniform random number of (-1, 1); θ2 represents a uniform random number of (-0.5, 0.5); μ1 and μ2 represent random numbers; δ represents an adaptive parameter; represents the candidate position; X b represents the optimal solution; Indicates the position of individual n in the current iteration.

[0111] S3. Divide the fault area based on the optimal value of the change multiple, image the fault area using ELT technology, and 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 algorithm. 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 the Maxwell equations, which are simplified to the boundary value problem of the Laplace equation under the quasi-static approximation. The continuous medium is discretized into a three-dimensional grid system containing N units through the finite element method (FEM). Each unit is assigned an initial conductivity to form a parameter vector σ∈R^N. A multi-frequency AC current (typical frequency range 1kHz-1MHz) is applied to the excitation end, and the boundary voltage V∈R^M is measured using a surface electrode array. The mathematical model of the forward problem is established: V=U(σ)+ε, where U(·) is a nonlinear forward operator and ε is the measurement noise. In order to overcome the problem of ill-posed inverse problem during the solution process, a two-layer optimization framework needs to be constructed. The first layer adopts the improved Tikhonov regularization method to transform the inversion problem into a minimization objective function; the second layer introduces a genetic algorithm-particle swarm hybrid optimization strategy to globally search for the optimal solution in the parameter space: the population diversity is maintained by adaptive mutation probability, and the sensitivity matrix is ​​combined with the sensitivity matrix to obtain the optimal solution. The singular value decomposition (SVD) is used to realize local fine search, and the Morozov deviation principle is used to dynamically shrink the search radius during the iteration process to ensure convergence to the physically feasible solution space.

[0113] Details include:

[0114] S31. Construct an electrical impedance method diagnostic model for the fault area, apply a multi-frequency AC current at the excitation end of the electrical impedance method diagnostic model, measure the boundary voltage of the fault area, 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, when conducting a direct impedance test, a certain number of electrodes are installed on the object to be measured and a low-frequency current is applied to it to form a current field. The direct impedance imaging problem is to calculate the potential distribution within the field under the conditions of known conductivity distribution, current injection position and magnitude, and boundary potential. According to the differential form of Ohm's theorem:

[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, according to the current continuity:

[0119]

[0120] in, Represents the vector differentiation operator.

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

[0122]

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

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

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

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

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

[0128] The inverse problem computational model injects a low-frequency current I into the field and reconstructs the resistivity distribution ρ within the field, or its changes, based on the measured potentials of the deployed electrodes. The resistivity distribution within the field is then inferred based on the known location and magnitude of the excitation current and the known electrode potentials.

[0129] Apply a certain current excitation to the i-th pair of electrodes and measure the voltage value V of the j-th pair of electrodes ij , where i, j = 1, 2, ..., N, where N represents the number of electrodes. The voltage V ij To calculate the voltage of each electrode. According to the field resistivity, when the resistivity is ρ, the voltage U of each electrode can be obtained. ij (ρ),i,j=1,2,…,N and: U ij =V ij , V ij U represents the measured voltage of the jth pair of electrodes when current excitation is applied to the ith pair of electrodes; ij It represents the calculated voltage corresponding to the jth pair of electrodes when current excitation is applied to the i-th pair of electrodes.

[0130] However, the calculated voltage and the measured voltage are often not completely equal. There are various errors between the two, which makes the above equation difficult to implement. Generally, the error function is constructed by the least squares method:

[0131]

[0132] There are ill-posed problems in the process of finding the resistivity distribution ρ that minimizes E(ρ), so a regularization penalty factor is introduced to correct it, and then the inverse problem model based on the modified Newton-Raphson (MNR) method is derived. The objective function can be expressed as:

[0133]

[0134] Where U(ρ) represents the calculated value of the electrode voltage; V represents the measured value of the electrode voltage; α represents the regularization parameter; L represents the regularized unit 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 position where it is injected into the fault area, and iteratively solve the resistivity distribution based on the inverse problem objective function in combination with the potential distribution.

[0136] In detail, the Taylor expansion of the objective function at ρ(k) is performed, and the Taylor series is taken to the extreme value at the extreme point, and then the first-order derivative and the second-order derivative of the function f are substituted, and the iterative format for the solution can be obtained 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] Among them, ρ (k+1) represents the result of the k+1th resistivity iteration; ρ (k) represents the result of the kth resistivity iteration; J represents the partial derivative matrix of the calculated voltage with respect to resistivity; L represents the regularized unit matrix.

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

[0140] S35. Perform imaging based on resistivity distribution.

[0141] Example 2

[0142] In order to verify the feasibility of the method provided in the above embodiment, in this embodiment, the following Figure 3 Verify the grounding grid shown, 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 adopting multiple rotation excitation measurement methods, 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] In this embodiment, it is assumed that the branch resistors R3, R29, R36 and R40 are corroded. Figure 4 The branch resistance is circled in the middle.

[0145] S2, the change multiple of the branch resistance value relative to the initial resistance value in the diagnosis is used as the parameter to be solved, and the fitness function is constructed based on the measured value and the calculated value. The NRBO algorithm is used to solve the optimal value of the change multiple. The result is as follows: Figure 5 shown.

[0146] S3. Divide the fault area based on the optimal value of the change multiple, image the fault area using ELT technology, and obtain the corrosion status of each branch in the fault area.

[0147] from Figure 5 It can be seen that the branch resistances R3, R29, R36 and R40 have changed compared with the initial values ​​of the branch resistances, that is, the resistances R3, R29, R36 and R40 have been corroded, which is the same as the set situation, that is, the method provided by the present invention is feasible.

[0148] Example 3

[0149] The present invention also provides a grounding grid fault diagnosis system based on NRBO-ELT, comprising a central processing unit (CPU) capable of executing various appropriate actions and processes based on computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0150] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0151] The processing unit performs the various methods and processes described above, such as methods S1 to S3 and A1 to A3. For example, in some embodiments, methods S1 to S3 and A1 to A3 may be implemented as a computer software program, which is 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 onto the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of methods S1 to S3 and A1 to A3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 and A1 to A3 by any other appropriate means (e.g., by means of firmware).

[0152] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0153] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone 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 the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (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 such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A grounding grid fault diagnosis method based on NRBO-ELT, characterized in that: The method comprises: Constructing a grounding network diagnostic model and obtaining all accessible nodes in the grounding network, calculating node voltage values ​​based on the grounding network diagnostic model, and obtaining measured values ​​of accessible nodes and initial branch resistance values; constructing a fitness function based on the measured values ​​and the calculated values; The change multiple 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 multiple is obtained by iterative solution; If the change multiple is greater than a preset value, the branch corresponding to the change multiple is a fault area, and the fault area is imaged using ELT technology; otherwise, it indicates that the corresponding branch has not failed.

2. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 1, characterized in that: The method further includes injecting a DC excitation current into the grounding grid using multiple rotation excitation measurements to obtain the measurement value, including: A1. Select any two accessible nodes from all the accessible nodes as the inflow end and outflow end of the DC excitation current; A2. injecting the DC excitation current from the inflow end and allowing it to flow out from the outflow end, and measuring the node voltages of all accessible nodes under this excitation mode; A3. Reselect two other reachable nodes and repeat steps A1 to A2 until the termination condition is met.

3. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 1, characterized in that: The fitness function is expressed as: Among them, U pi represents the measured value of the voltage at node i; U qi represents the calculated value of the voltage at node i; η represents the number of accessible nodes.

4. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 1, characterized in that: The method for solving the branch resistance value change using the NRBO algorithm includes: Obtaining a boundary of the variation multiple of the branch resistance value, and randomly generating an initial population within the boundary, wherein the position of each individual in the initial population represents a candidate solution for the variation multiple of the branch resistance value; Based on the initial population, iteratively perform the following steps until the iteration end condition is met: Calculate the fitness value of each individual in the population, obtain the optimal fitness value and the minimum fitness value and their corresponding optimal solution and worst solution; Calculate the adaptive coefficient, search step size and direction guidance parameters of the NRBO algorithm; Calculating a root position based on the search step, calculating a first intermediate position and a second intermediate position based on the root position and the direction guidance parameter, and performing an initial update on the position of the individual based on the first intermediate position and the second intermediate position to obtain a candidate position; Based on the adaptive coefficient, the candidate positions are optimized using TAO to obtain the individual final position, the fitness function corresponding to the final position is calculated, and the optimal solution or the worst solution is updated.

5. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 4 is characterized in that: The method for calculating the adaptive coefficient, search step size and direction guidance parameters of the NRBO algorithm is: Adaptive coefficient: Where IT represents the current number of iterations; Max_IT represents the maximum number of iterations; δ represents the adaptive coefficient, and δ∈[-1, 1]; Search step size: Among them, rand means taking a random number; (1, dim means 1 row, dim column vector; X b represents the optimal solution; Indicates the position of individual n at the current iteration number IT; Direction guidance parameters: Where a and b represent random numbers between (0, 1); represents the position of an individual r1 randomly selected from the population; represents the position of the individual r2 randomly selected from the population; X b Indicates the optimal solution.

6. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 4 is characterized in that: The method for obtaining the candidate position is: The root position is derived based on the updated individual position using Newton's method, and its expression is: Among them, x n+1 Indicates the root position of the nth individual at the current iteration; Indicates the position of the nth individual in the current iteration; Δx represents the search step length; The first intermediate value is calculated based on the root position, and its expression is: Where, ρ represents the direction guidance parameter; The second intermediate value is calculated based on the optimal solution, and its expression is: Among them, X b represents the optimal solution; NRSR represents the NRSR operator; Calculate the candidate position, the expression is: Where c represents the weight coefficient; δ represents the adaptive coefficient; Indicates the position of individual n at the current iteration.

7. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 4 is characterized in that: The method for obtaining the final position is: in, represents the final position of the individual at the IT+1th iteration after optimization; θ1 represents a uniform random number of (-1, 1); θ2 represents a uniform random number of (-0.5, 0.5); μ1 and μ2 represent random numbers; δ represents an adaptive parameter; represents the candidate position; X b represents the optimal solution; Indicates the position of individual n in the current iteration.

8. 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: An electrical impedance method diagnostic model of the fault area is constructed, a multi-frequency AC current is applied to the excitation end of the electrical impedance method diagnostic model, the boundary voltage of the fault area is measured, and a forward problem mathematical model is constructed, 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; Obtaining conductivity distribution, current magnitude, and boundary voltage of the fault area under the multi-frequency AC current excitation, and solving a forward problem mathematical model based on the conductivity distribution, current magnitude, and boundary voltage to obtain a potential distribution; injecting a low-frequency current into the fault area, reconstructing the resistivity distribution under the excitation of the low-frequency current, and constructing an inverse problem objective function; Obtaining the magnitude of the low-frequency current and the node position where the low-frequency current is injected into the fault area, and iteratively solving the resistivity distribution based on the inverse problem objective function in combination with the potential distribution; Imaging is performed based on the resistivity distribution.

9. The grounding grid fault diagnosis method based on NRBO-ELT according to claim 8, characterized in that: The objective function of the inverse problem is: Where U(ρ) represents the calculated value of the electrode voltage; V represents the measured value of the electrode voltage; α represents the regularization parameter; L represents the regularized unit matrix; ρ represents the resistivity distribution; and ρ0 represents the initial resistivity distribution.

10. A grounding grid fault diagnosis system based on NRBO-ELT, characterized in that: The system is used to implement the method according to any one of claims 1 to 9.

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