Grounding grid defect inversion method based on improved multi-objective rime optimization algorithm
By improving the multi-objective hoarfrost optimization algorithm, the nonlinearity and instability problems in the grounding system inversion process are solved, and the accurate synchronous inversion of grounding grid defect size and apparent resistivity is realized, which is suitable for power network grounding defect detection.
Patent Information
- Application Number
- CN202511644199.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-01-23
AI Technical Summary
Existing grounding system defect diagnosis methods face challenges such as rapid signal attenuation, non-uniform interference from the medium, and parameter coupling, resulting in a highly nonlinear and unstable inversion process. Conventional optimization algorithms are prone to falling into local optima and cannot provide reliable size estimates, thus affecting the comprehensiveness of grounding system health assessment.
An improved multi-objective rime optimization algorithm is adopted, combined with Sobol sequence, dynamic reverse initialization, elite path guidance and adaptive Levy flight disturbance, to design a grounding grid defect inversion method, which realizes end-to-end synchronous inversion of transient electromagnetic signals, defect size and apparent resistivity.
It achieves accurate synchronous inversion of grounding grid defect size and apparent resistivity, with a convergence speed faster than traditional methods and performance superior to conventional genetic algorithms and particle swarm optimization. It is suitable for non-destructive quantitative analysis and graphical diagnosis of grounding grids.
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Figure CN121389799A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of non-destructive testing of grounding grids, and in particular to a grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm. BACKGROUND
[0002] As a key protective element of power infrastructure, the performance of the grounding system directly affects the rapid release of abnormal current and the smooth control of system voltage. In the event of lightning attack or line failure, it can effectively alleviate the rise of ground potential gradient, thereby protecting the isolation performance of electrical devices and reducing the risk of step voltage, which is crucial for the safety of on-site workers. However, underground conductors are long-term exposed to soil medium, which are easily affected by electrochemical corrosion and mechanical stress, leading to problems such as cross-section reduction, loose connection and even complete rupture, which in turn causes an increase in grounding impedance and a decrease in heat resistance. With the growth of power network capacity and the increase of current intensity, the integrity monitoring of the grounding system has become a key point and technical threshold for maintenance work. Existing grounding system defect diagnosis methods mainly include electrochemical survey, structural node analysis and magnetic field induction detection. Electrochemical survey is limited to local points and cannot achieve global coverage. Structural node analysis relies on network topology and requires system shutdown for execution, and is strongly dependent on predefined geometric parameters. Magnetic field induction detection uses surface current injection and captures magnetic response to infer the state of underground conductors. Although these methods have their own characteristics, they usually involve power interruption or ground excavation, limiting their application in continuous operation scenarios.
[0003] In recent years, transient electromagnetic methods have gradually been applied to non-destructive testing of grounding systems due to their non-contact, deep detection and sensitivity to conductors. In addition, current defect inversion strategies rely on simplified models such as the "smoke ring" theory, which is difficult to support accurate quantification of defect degree due to harsh assumption conditions and low calculation accuracy. More complexly, size inversion faces challenges such as rapid signal decay, medium non-uniform interference and parameter coupling, resulting in highly nonlinear and unstable inversion processes, and conventional optimization algorithms are prone to local optimization, which cannot provide reliable size estimates, thereby affecting the comprehensiveness of grounding system health assessment.
[0004] Therefore, there is a need for a grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm. SUMMARY
[0005] In view of the challenges of signal rapid attenuation, medium non-uniform interference and parameter coupling in the prior art size inversion, which leads to highly nonlinear and unstable inversion process, the conventional optimization algorithm is easy to fall into local optimum and cannot provide reliable size estimation, thereby affecting the comprehensiveness of the health assessment of the grounding system, the present application provides a grounding grid defect inversion method based on improved multi-objective fog optimization algorithm, which can effectively capture the complex correlation between transient electromagnetic signal and defect size and apparent resistivity, realize efficient synchronous inversion from end to end, and has significant engineering value and broad application potential. A grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm, comprising the following steps: S1: establishing a transient electromagnetic two-dimensional forward model for simulating the electromagnetic response of underground medium; S2: designing an improved multi-objective fog optimization algorithm model, wherein Sobol sequence and dynamic reverse initialization are used to improve the quality of the population, elite path guiding mechanism is used to strengthen local optimization, adaptive Levy flight disturbance is used to balance global search, and Pareto front screening is used to realize multi-objective coordination; S3: verifying the effectiveness of the algorithm through multi-layer soil electrical model simulation, and adjusting parameters to optimize performance; S4: inputting the measured transient electromagnetic signal into the optimized algorithm model to realize end-to-end synchronous accurate inversion of the grounding grid defect size and apparent resistivity, generating a defect size and apparent resistivity profile for quantitative diagnosis of the corrosion defect of the grounding grid.
[0006] Preferably, in step S1, a three-layer soil-conductor-soil electrical model is used for two-dimensional forward simulation, the thickness h1, h2, h3 and apparent resistivity ρ1, ρ2, ρ3 of each layer are set as parameter ranges for generating electromagnetic response signals, and the focus parameters include the conductor layer defect size h2 and apparent resistivity ρ2.
[0007] Preferably, in step S1, the soil electrical parameter range is: h1∈[0.4,1.6] m; h2∈[0.2,0.7] m; h3∈[0.4,1.6] m; ρ1∈[15,180] Ω·m; ρ2∈[10 -8 ,10 -5 ] Ω·m; ρ3∈[15,180] Ω·m。
[0008] Preferably, in step S1, one-dimensional forward modeling is performed by a "soil-flat steel-soil" three-layer geoelectric model, the parameter ranges of the thickness h1, h2, h3 and apparent resistivity ρ1, ρ2, ρ3 of each layer are randomly set, a sample library containing 2000 groups of transient electromagnetic response signals is generated, each group of signals is composed of 50 sampling points, and the labels include the apparent resistivity ρ2 and thickness h2 of the flat steel layer.
[0009] Preferably, in step S2, the algorithm model adopts soft fog search for global exploration, and hard fog puncture for local utilization, Sobol sequence is introduced to generate uniform population, dynamic reverse strategy is used to improve the quality of initial solution, the maximum number of iterations is set to 1200, and the population size is set to 50. The Sobol sequence initialization formula is: In the formula, Sobol sequence value of the i-th individual in the j-th dimension, Sobol function of the j-th dimension, i represents the individual index, and j represents the dimension index; The dynamic reverse initialization formula is: In the formula, Reverse individual, Mapped population individual, Lower limit of the parameter, Upper limit of the parameter, Dynamic adjustment parameter, rand Random number in [0, 1]; The soft fog search update formula is: In the formula, Updated individual, Random number in [0, 1], Current optimal individual; The elite path guiding formula is: In the formula, Optimal individual j-th dimension update value, Suboptimal individual j-th dimension value, f Fitness function, j represents the dimension index; The adaptive Levy flight disturbance formula is: In the formula, Disturbed individual, Indicates the step size factor. This represents the step size generated by the Levy distribution.
[0010] Preferably, in step S2, the input of the algorithm is a transient electromagnetic signal sequence, and the output is the conductor layer defect size h2 and apparent resistivity ρ2, thereby achieving multi-objective synchronous inversion.
[0011] Preferably, in step S3, the algorithm performance is evaluated using the mean relative error (MRE), mean square error (MSE), and correlation coefficient (R) indicators, and iterative optimization is performed through simulation using a three-layer soil model.
[0012] Preferably, in step S4, the transient electromagnetic signal is acquired by a coaxial coil device, and the defect size h2 and apparent resistivity ρ2 are synchronously inverted by an improved multi-objective frost optimization algorithm model.
[0013] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the grounding grid defect inversion method based on the improved multi-objective frost optimization algorithm as described above.
[0014] A processor for running a program, wherein the program executes the grounding grid defect inversion method based on the improved multi-objective frost optimization algorithm as described above.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention improves the multi-objective hoarfrost optimization algorithm model, which can efficiently mine the nonlinear relationship between transient electromagnetic signals and defect size and apparent resistivity, achieve accurate synchronous inversion, converge faster than traditional methods, and significantly outperform conventional genetic algorithms, particle swarm optimization and simplified models. It is suitable for non-destructive quantitative analysis and graphical diagnosis of grounding grid conditions and can be widely used in the field of power network grounding defect detection. Attached Figure Description
[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.
[0017] Figure 1 This is a flowchart illustrating an embodiment of the present invention; Figure 2 This is a schematic diagram of a three-layer electrical model according to an embodiment of the present invention; Figure 3A transient electromagnetic response signal example schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION
[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0019] It should be understood that, when used in the specification and the appended claims, the terms “comprise” and “include” indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.
[0020] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms “a”, “an” and “the” are intended to include the plural forms.
[0021] It should be further understood that the term “and / or” used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.
[0022] In recent years, the transient electromagnetic method has been gradually applied to the nondestructive inspection field of grounding systems due to its non-contact, large detection depth, and conductor sensitivity. In addition, current defect inversion strategies rely on simplified models such as the “smoke ring” theory, which is difficult to support accurate quantification of defect degree due to harsh assumption conditions and low calculation accuracy of defects. More complexly, size inversion faces challenges such as rapid signal decay, medium non-uniform interference, and parameter coupling, resulting in a highly nonlinear and unstable inversion process, and conventional optimization algorithms are prone to fall into local optimization, which cannot provide reliable size estimation, thereby affecting the comprehensiveness of the health assessment of the grounding system. Based on this, the embodiment provides a grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm, which solves the existing technical problems to achieve the effect of end-to-end efficient synchronous inversion.
[0023] Figure 1 A flowchart of a grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm provided by the present application, comprising the following steps: S1, a transient electromagnetic two-dimensional forward model is established to simulate the electromagnetic response of the underground medium; S2, a multi-objective fog optimization algorithm model is designed, wherein the Sobol sequence and dynamic reverse initialization improve the population quality, the elite path guide mechanism strengthens the local optimization, the self-adaptive Levy flight disturbance balances the global search, and the Pareto front screening realizes the multi-objective coordination; S3, the effectiveness of the algorithm is verified through a multi-layer soil electrical model simulation, and the parameters are adjusted to optimize the performance; S4, the transient electromagnetic signals measured in the field are input into the optimized algorithm model to realize the end-to-end synchronous accurate inversion of the defect size and apparent resistivity of the grounding grid, and the defect size and apparent resistivity profile are generated for quantitative diagnosis of the corrosion defects of the grounding grid.
[0024] As shown in Figure 2 Step S1, a two-dimensional forward simulation is performed through a "soil-conductor-soil" three-layer electrical model, the thickness h1, h2, h3 and apparent resistivity ρ1, ρ2, ρ3 of each layer are set as the parameter range, which is used to generate electromagnetic response signals, and the focus parameters include the defect size h2 and apparent resistivity ρ2 of the conductor layer.
[0025] In step S1, the soil electrical parameter range is: h1∈[0.4,1.6] m, h2∈[0.2,0.7] m, h3∈[0.4,1.6] m, ρ1∈[15,180] Ω·m, ρ2∈[10^{-8},10^{-5}] Ω·m, ρ3∈[15,180] Ω·m.
[0026] Moreover, in step S2, the algorithm model uses soft fog search for global exploration, hard fog puncture for local utilization, introduces Sobol sequence to generate uniform population, dynamic reverse strategy to improve the quality of initial solution, and sets the maximum number of iterations to 1200 and the population size to 50.
[0027] Moreover, in step S2, the input of the algorithm is the transient electromagnetic signal sequence, and the output is the defect size h2 and apparent resistivity ρ2 of the conductor layer, realizing multi-objective synchronous inversion.
[0028] Moreover, in step S3, the algorithm performance is evaluated by using the mean relative error MRE, mean square error MSE and correlation coefficient R indicators, and the three-layer soil model simulation is iteratively optimized.
[0029] Moreover, in step S4, the transient electromagnetic signals are collected by a coaxial coil device, and the improved multi-objective fog optimization algorithm model is used to realize the synchronous inversion of the defect size h2 and apparent resistivity ρ2. The error is less than 6%.
[0030] As shown in Figure 3As shown, the ground net defect inversion method based on the improved multi-objective fog optimization algorithm according to the embodiment of the application can effectively establish the complex mapping relationship between the transient electromagnetic signal and the defect size and apparent resistivity, the correlation coefficient of the inversion result and the actual value is stable above 0.96, compared with the traditional smoke ring model and the single particle swarm and differential evolution algorithm, the inversion accuracy and convergence efficiency are both significantly superior, and the method is suitable for ground net corrosion diagnosis and fracture identification, and can be widely applied in the field of non-destructive testing of power system ground net defects.
[0031] To sum up, the application establishes a transient electromagnetic two-dimensional forward model, then designs an improved multi-objective fog optimization algorithm model, then verifies the effectiveness of the algorithm through a multi-layer soil electrical model simulation, adjusts the parameters to optimize the performance, finally inputs the measured transient electromagnetic signal into the optimized algorithm model to realize the end-to-end synchronous accurate inversion of the ground net defect size and apparent resistivity, and generates a defect size and apparent resistivity profile for the quantitative diagnosis of the ground net corrosion defect. Based on this, the application can efficiently mine the nonlinear relationship between the transient electromagnetic signal and the defect size and apparent resistivity, and realize accurate synchronous inversion. In terms of performance, the convergence speed of the application is faster than that of the traditional method, and the performance is obviously better than that of the conventional genetic algorithm, particle swarm optimization and simplified model, and the application is suitable for non-destructive quantitative analysis and graphical diagnosis of the ground net condition, and can be widely applied in the field of power network ground defect detection.
[0032] Those skilled in the art can appreciate that the units of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both, and the components of the examples have been described in the above description in general terms to clearly illustrate the interchangeability of hardware and software. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.
[0033] In the embodiments provided by the application, it should be understood that the division of units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.
[0034] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit.
[0035] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-0nly Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.
[0036] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.
Claims
1. A grounding grid defect inversion method based on an improved multi-objective fog optimization algorithm, characterized in that, The method comprises the following steps: S1: establishing a transient electromagnetic two-dimensional forward model for simulating the electromagnetic response of the underground medium; S2: designing an improved multi-objective fog optimization algorithm model, wherein the Sobol sequence and dynamic reverse initialization are used to improve the population quality, the elite path guide mechanism is used to strengthen local optimization, the self-adaptive Levy flight disturbance is used to balance global search, and the Pareto front screening is used to realize multi-objective coordination; S3: verifying the effectiveness of the algorithm through multi-layer soil electrical model simulation, and adjusting the parameters to optimize the performance; S4: inputting the measured transient electromagnetic signal into the optimized algorithm model to realize the end-to-end synchronous accurate inversion of the grounding grid defect size and apparent resistivity, and generating a defect size and apparent resistivity profile for quantitative diagnosis of the corrosion defect of the grounding grid.
2. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S1, a two-dimensional forward simulation is performed through a "soil-conductor-soil" three-layer electrical model, the parameter ranges of the thickness h1, h2, h3 and the apparent resistivity ρ1, ρ2, ρ3 of each layer are set, and the electromagnetic response signal is generated, and the focus parameters include the defect size h2 and the apparent resistivity ρ2 of the conductor layer.
3. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S1, the soil electrical parameter ranges are: h1∈[0.4,1.6] m; h2∈[0.2,0.7] m; h3∈[0.4,1.6] m; ρ1∈[15,180] Ω·m; p2 e [10 -8 ,10 -5 ] Ω·m; ρ3∈[15,180] Ω·m.
4. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S1, a one-dimensional forward simulation is performed through a "soil-flat steel-soil" three-layer electrical model, the parameter ranges of the thickness h1, h2, h3 and the apparent resistivity ρ1, ρ2, ρ3 of each layer are randomly set, a sample library containing 2000 groups of transient electromagnetic response signals is generated, each group of signals is composed of 50 sampling points, and the labels include the apparent resistivity ρ2 and the thickness h2 of the flat steel layer.
5. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S2, the algorithm model uses soft fog optimization for global exploration, hard fog optimization for local optimization, introduces Sobol sequence to generate uniform population, dynamic reverse strategy to improve initial solution quality, and sets the maximum number of iterations to 1200 and the population size to 50; wherein the Sobol sequence initialization formula is: wherein denotes the Sobol sequence value of the i-th individual in the j-th dimension, denotes the Sobol function of the j-th dimension, i denotes the individual index, and j denotes the dimension index; The dynamic reverse initialization formula is: wherein, represents the inverse individual, represents the mapped population individual, represents the parameter lower bound, represents the parameter upper bound, represents the dynamically adjusted parameter, rand represents a random number between [0, 1]; The soft fog optimization update formula is: wherein, denotes the updated individual, denotes a random number between [0, 1], denotes the current best individual; The elite path guide formula is: wherein denotes the j-th dimension update value of the best individual, denotes the j-th dimension value of the second best individual, f denotes the fitness function, j denotes the dimension index; The self-adaptive Levy flight disturbance formula is: wherein, denotes the perturbed individual, denotes the step factor, denotes the step generated by the Levy distribution.
6. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S2, the input of the algorithm is the transient electromagnetic signal sequence, and the output is the defect size h2 and the apparent resistivity ρ2 of the conductor layer, realizing multi-objective synchronous inversion.
7. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S3, the algorithm efficiency is evaluated by using the mean relative error MRE, the mean square error MSE and the correlation coefficient R, and the three-layer soil model simulation is iteratively optimized.
8. The grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to claim 1, characterized in that, In step S4, the transient electromagnetic signal is collected by a coaxial coil device, and the improved multi-objective fog optimization algorithm model is used to realize the synchronous inversion of the defect size h2 and the apparent resistivity ρ2.
9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein when the program runs, the device where the computer readable storage medium is located executes the grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to any one of claims 1 to 8.
10. A processor, comprising: The processor is configured to run a program, and the program is configured to perform the grounding grid defect inversion method based on the improved multi-objective fog optimization algorithm according to any one of claims 1 to 8 when the program is running.