A method for optimizing resistance reduction of a plurality of vertical grounding bodies in cooperation with a horizontal grounding type

Abstract

This invention discloses a method for optimizing the coordinated resistance reduction of multiple vertical and horizontal grounding electrodes. It obtains the soil resistivity, vertical grounding electrodes, and horizontal grounding electrodes for different target construction areas, and establishes a coordinated current dissipation field model. Based on the model, a multi-objective optimization model is established using the decision variables and constraints of the vertical grounding electrodes. The multi-objective optimization problem is decomposed into multiple single-objective sub-problems, yielding aggregation function values. A decompositional multi-objective evolutionary algorithm is used to minimize the aggregation function values, outputting a Pareto front solution set. The optimal solution is selected from the Pareto front solution set, and the coordinated current dissipation efficiency coefficient is calculated. If the coefficient is in the positive coordination interval, the optimal solution is output as the final optimized configuration scheme; otherwise, the calculation is repeated until the final scheme in the positive coordination interval is output. This invention achieves optimal combined grounding performance and economy by using a decompositional multi-objective evolutionary algorithm to solve the Pareto front.

Description

Technical Field

[0001] This invention belongs to the field of power system grounding technology, and specifically relates to a method for optimizing resistance reduction through the coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations. Background Technology

[0002] This patent addresses the technical challenges in power system grounding engineering in regions with varying soil resistivity. Firstly, existing technologies often treat vertical and horizontal grounding electrodes as a simple superposition, failing to adequately consider the spatial coupling of the current dissipation field and soil utilization optimization, thus hindering the achievement of synergistic resistance reduction effects. Secondly, traditional weighted summation methods are limited in their ability to reflect conflicts between objectives, lack objective basis for weight selection, and yield only a single solution per run, failing to provide complete Pareto front information for engineering decisions. Therefore, this patent addresses these technical problems by establishing a synergistic current dissipation model for vertical and horizontal grounding electrodes and decomposing a multi-objective algorithm, thereby improving the stability and economy of the grounding system.

[0003] Existing grounding technology patents have two shortcomings: Vertical and horizontal grounding electrodes are often calculated independently and then superimposed in a simple parallel configuration without considering the current redistribution and spatial coupling of the current dissipation field between them. The lack of a mutual impedance model for coupling makes it impossible to quantitatively evaluate the shielding effect in the overlapping area, resulting in low material utilization and diminishing marginal resistance reduction, making it difficult to achieve synergistic resistance reduction.

[0004] Multi-objective optimization solutions often employ the weighted summation method, which transforms multiple objectives into single-objective optimization. Different preferred solutions are obtained by adjusting the weight coefficients. However, this method is difficult to reflect the conflict relationship between objectives, and the selection of weights lacks objective basis. Only one solution is obtained per run, which cannot provide complete Pareto front information. Summary of the Invention

[0005] To address the problems in the related technologies, this application provides a method for optimizing resistance reduction through the coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations, thus resolving the issues mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing resistance reduction through the coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations, comprising the following steps: Step S1: Obtain the soil resistivity, vertical grounding electrode, and horizontal grounding electrode for different target construction areas; Step S2: Establish a cooperative current dissipation field model based on soil resistivity, vertical grounding electrode, and horizontal grounding electrode; Step S3: Establish a multi-objective optimization model based on the cooperative flow field model combined with the decision variable vector and constraints of the vertical grounding body; Step S4: Treat the multi-objective optimization model as a multi-objective optimization problem, decompose the multi-objective optimization problem into multiple single-objective sub-problems and process them to obtain the aggregation function value. Use the decomposition multi-objective evolutionary algorithm to minimize the aggregation function value and output the Pareto front solution set. Step S5: Select the comprehensive optimal solution from the Pareto front solution set, calculate the cooperative current dissipation efficiency coefficient corresponding to the comprehensive optimal solution. When the cooperative current dissipation efficiency coefficient is in the positive cooperative interval, output the comprehensive optimal solution as the final optimized configuration scheme. When the cooperative current dissipation efficiency coefficient is not in the positive cooperative interval, return to steps S2-S4 to recalculate until the final optimized configuration scheme in the positive cooperative interval is output.

[0007] Furthermore, the specific process of step S2 is as follows: Based on the soil resistivity, vertical grounding electrode and horizontal grounding electrode obtained in step S1, a three-dimensional current-dissipating field physical structure is constructed. The vertical grounding electrode is equivalent to a column current source, and the potential distribution of the column current source is calculated using the area integral method. ; For the vertical grounding electrode at the field point The potential distribution generated at that location; The horizontal grounding electrode is equivalent to a line current source, and the potential distribution of the line current source is calculated using line integral. ; For the horizontal grounding electrode at the field point The potential distribution generated at that location; based on and calculate ; For composite grounding system at the field point The total potential distribution generated at that location; Based on the total potential distribution, the field point Placed on the surface of the grounding electrode, the maximum surface potential of the grounding electrode is obtained. Based on the maximum surface potential of the grounding electrode and the current injected into the composite grounding system Calculate the grounding resistance of the composite grounding system ; based on Define the cooperative flow efficiency coefficient ; Establish the cooperative flow efficiency coefficient Evaluation criteria; Based on the three-dimensional diffused flow field physical structure, combined with the above , , , and the cooperative flow dissipation efficiency coefficient The evaluation criteria were used to complete the construction of the cooperative flow field model.

[0008] Furthermore, in the cooperative flow field model A multi-objective optimization model is established by combining the decision variable vector and constraints of the vertical grounding electrode, which is expressed as follows: ; ; In the formula, The minimization objective is for a multi-objective optimization model; The first objective function is the grounding resistance of the composite grounding system; The second objective function is material cost; Total material cost; This refers to the number of vertical grounding electrodes; The length of the vertical grounding electrode; Cost of materials per unit length of grounding electrode; Reduce the number of vertical grounding electrodes by one; For the first Root and First The spacing between vertical grounding electrodes; Cost per unit length of excavation; This is a vector of decision variables.

[0009] Furthermore, constraints are set, indicating that: ; In the formula, This refers to the minimum allowable construction spacing between vertical grounding electrodes; This refers to the maximum allowable construction spacing between vertical grounding electrodes; The shielding effect coefficient; This refers to the number of vertical grounding electrodes; It is an exponential function; It is a cosine function; No. Root and First The azimuth angle between the vertical grounding electrodes; Among them, the constraints include construction space constraints and shielding effect constraints; Due to construction space constraints; the above The calculation formula is the shielding effect constraint.

[0010] Furthermore, the specific process for obtaining the aggregate function value is as follows: In the multi-objective optimization model and As a multi-objective optimization problem, the multi-objective optimization problem is decomposed into M single-objective sub-problems. Each single-objective sub-problem is processed by weight vector aggregation to obtain... ,express: ; In the formula, The value of the aggregate function; This is the weight vector; This represents the current optimal value of the objective function, which is also the ideal point. This is for calculating the maximum value. The total number of objective functions; For the first The weight coefficients corresponding to each objective; For the first One objective function; For the first The current optimal value of each objective function.

[0011] Furthermore, the specific process of outputting the Pareto front solution set is as follows: The aggregation function value is obtained by using a decomposition multi-objective evolutionary algorithm. The minimization solution is performed, and the specific iterative process is as follows: The decomposition multi-objective evolutionary algorithm first calculates each weight vector. The Euclidean distance between them is chosen to be the same as the first. The nearest Euclidean distance to the weight vector Each weight vector will The subproblem corresponding to the weight vector is the first... Neighborhood structure of sub-problems; These are preset neighborhood parameters; Based on the neighborhood structure, the initial population and ideal points are generated. The initial population contains the first... The neighborhood solutions to each subproblem are then selected from the neighborhood structure, along with the parent decision variable vector. Perform crossover and mutation to generate an updated decision variable vector; Calculate the aggregation function value corresponding to the updated decision variable vector, and compare it with the aggregation function value corresponding to the current neighborhood solution. If the aggregation function value of the updated decision variable vector is smaller, the updated decision variable vector is determined to be better, and the ideal point is updated using the updated decision variable vector. With neighborhood solutions; Achieve the above ideal point After updating with the neighborhood solution, the loop is controlled by the iteration termination condition. If the current iteration number of the decomposition multi-objective evolutionary algorithm has not reached the maximum iteration number, it returns to continue the evolution and solution. If the current iteration number of the decomposition multi-objective evolutionary algorithm reaches the maximum iteration number, it stops iterating. All updated neighborhood solutions are compared pairwise, and the updated neighborhood solutions that are not mutually dominant are selected to form the non-dominated solution set. The non-dominated solution set is output as the Pareto front solution set.

[0012] Furthermore, the specific process of step S5 is as follows: The process of selecting the comprehensive optimal solution from the Pareto front solution set is as follows: The Pareto front solution set is normalized, and the Euclidean distance from the objective function value corresponding to each updated decision variable vector in the Pareto front solution set to the updated ideal point is calculated. The updated decision variable vector with the smallest Euclidean distance is selected as the comprehensive optimal solution. Calculate the cooperative flow dissipation efficiency coefficient corresponding to the integrated optimal solution. ; Determine the cooperative flow dissipation efficiency coefficient corresponding to the comprehensive optimal solution. Whether it is in the positive coordination interval, when At that time, make a judgment Within the positive coordination interval, the vertical grounding electrode length, number of vertical grounding electrodes, and the first vertical grounding electrode corresponding to the comprehensive optimal solution will be considered. Root and First The spacing between the vertical grounding electrodes is output as the final optimized configuration scheme. When the calculated optimal solution corresponds to the cooperative dissipation efficiency coefficient If the output is not in the positive coordination interval, return to steps S2 to S4 to recalculate until the final optimized configuration scheme that is in the positive coordination interval is output.

[0013] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program code, and the processor is used to call the program code stored in the memory to execute a method for optimizing the resistance reduction of multiple vertical grounding electrodes and horizontal grounding types.

[0014] A non-volatile computer storage medium stores computer-executable instructions that execute a method for optimizing resistance reduction in a combination of multiple vertical grounding electrodes and horizontal grounding configurations.

[0015] Compared with existing technologies, the present invention has the following advantages: This invention equates vertical grounding electrodes to column current sources and horizontal grounding electrodes to line current sources, obtaining the total potential distribution based on the superposition principle, and defines a cooperative current dissipation efficiency coefficient as a quantitative evaluation index for the cooperative resistance reduction effect. A multi-objective optimization model is established with the goals of minimizing grounding resistance and material cost, and a decomposed multi-objective evolutionary algorithm is used to solve the Pareto front, achieving a comprehensive optimization of grounding performance and economy. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This is a physical structure diagram of the three-dimensional flow field of the present invention.

[0018] Figure 3 This is a comparison diagram of the potential distribution of the current field in the vertical grounding electrode and the horizontal grounding electrode of the present invention.

[0019] Figure 4 This is a comparison of the iterative convergence curves of different optimization algorithms of this invention.

[0020] Figure 5 This is a diagram illustrating the Pareto front solution set distribution and the overall optimal solution for multi-objective optimization in this invention. Detailed Implementation

[0021] like Figure 1 As shown, the present invention provides a technical solution: a method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations, comprising: Step S1: Obtain the soil resistivity, vertical grounding electrode, and horizontal grounding electrode for different target construction areas; Step S2: Establish a cooperative current dissipation field model based on soil resistivity, vertical grounding electrode, and horizontal grounding electrode; Step S3: Establish a multi-objective optimization model based on the cooperative flow field model combined with the decision variable vector and constraints of the vertical grounding body; Step S4: Treat the multi-objective optimization model as a multi-objective optimization problem, decompose the multi-objective optimization problem into multiple single-objective sub-problems and process them to obtain the aggregation function value. Use the decomposition multi-objective evolutionary algorithm to minimize the aggregation function value and output the Pareto front solution set. Step S5: Select the comprehensive optimal solution from the Pareto front solution set, calculate the cooperative current dissipation efficiency coefficient corresponding to the comprehensive optimal solution. When the cooperative current dissipation efficiency coefficient is in the positive cooperative interval, output the comprehensive optimal solution as the final optimized configuration scheme. When the cooperative current dissipation efficiency coefficient is not in the positive cooperative interval, return to steps S2-S4 to recalculate until the final optimized configuration scheme in the positive cooperative interval is output.

[0022] The specific process of step S2 is as follows: Based on the soil resistivity, vertical grounding electrode, and horizontal grounding electrode obtained in step S1, a three-dimensional current-diffusing field physical structure is constructed, such as... Figure 2 As shown, the horizontal axis is X (m), with a scale range of -4 to 4; the vertical axis is Y (m), with a scale range of -4 to 4; and the vertical axis is Z (m), with a scale range of 1 to -5. The plane where Z=0 is defined as the ground surface. The horizontal grounding electrode is laid at a certain depth below the ground surface, primarily serving as a surface current diffuser. The surface current diffuser field of the horizontal grounding electrode is limited to the vicinity of the plane where the horizontal grounding electrode is located. The vertical grounding electrode extends downwards from the T-junction of the horizontal grounding electrode, utilizing the lower resistivity of the deep soil to achieve deep longitudinal current diffuser, forming a deep current diffuser field. The current diffuser fields of the horizontal and vertical grounding electrodes undergo spatial coupling and superposition within the synergistic current diffuser region shown by the purple dashed line. Based on the principle of current superposition, a three-dimensional current diffuser field physical structure is formed, effectively expanding the current diffuser space range, reducing grounding resistance, and realizing a positive synergistic resistance reduction effect between the deep current diffuser of the vertical grounding electrode and the surface current diffuser of the horizontal grounding electrode.

[0023] The vertical grounding electrode is equivalent to a column current source, and the potential distribution of the column current source is calculated using the area integral method. ,express: (1); In the formula, For the vertical grounding electrode at the field point The potential distribution generated at that location; This refers to the number of vertical grounding electrodes; For the first Surface area (m²) of the vertical grounding electrode; For the first Surface current density of a vertical grounding electrode (A / m²). Pi (a constant); Soil electrical conductivity; For field point P to the first The distance (m) between the surface elements dS of the vertical grounding electrode. The horizontal grounding electrode is equivalent to a line current source, and the potential distribution of the line current source is calculated using line integral. ,express: (2); In the formula, For the horizontal grounding electrode at the field point The potential distribution generated at that location; This refers to the number of horizontal grounding electrodes; Let be the length (m) of the kth horizontal grounding electrode. Let be the surface current density (A / m²) of the kth horizontal grounding electrode. As a venue up to the kth horizontal grounding conductor element Distance (m); calculate ,express: (3); In the formula, For composite grounding system at the field point The total potential distribution generated at that location; Based on the total potential distribution, the field point Placed on the surface of the grounding electrode, the maximum surface potential of the grounding electrode is obtained. According to the current injected into the composite grounding system Calculate the grounding resistance of the composite grounding system ,express: (4); based on Define the cooperative flow efficiency coefficient ,express: (5); In the formula, The separate current dissipation resistance for the horizontal grounding electrode (i.e., the grounding resistance when only the horizontal grounding electrode is laid). The separate current dissipation resistance for the vertical grounding electrode (i.e., the grounding resistance when only the vertical grounding electrode is laid). Establish the cooperative flow efficiency coefficient Evaluation criteria: When When =1, it indicates that the vertical grounding electrode and the horizontal grounding electrode are completely independent and have no synergistic effect; when When the value is greater than 1, it indicates the existence of a negative synergistic effect (mutual shielding); when 0.85 < When <1, it indicates the existence of a positive cooperative effect, reaching a cooperative divergence state; when A value ≤0.85 indicates insufficient synergy and requires optimization of the configuration scheme; Based on the physical structure of the three-dimensional flow field, combined with the above formulas (1) to (5) and the cooperative flow efficiency coefficient, The evaluation criteria were used to complete the construction of the cooperative flow field model.

[0024] like Figure 3 As shown, Figure 3 The comparison of the potential distribution in the current-dissipating field between vertical and horizontal grounding electrodes is shown. Figure 3 The horizontal axis represents X (m), with a scale range of -20 to 20; the vertical axis represents Y (m), with a scale range of -20 to 20; the vertical axis in the figure represents the potential (V), with a scale range of 0 to 5000. Figure 3 include Figure 3 (a) Figure 3 (b) and Figure 3 (c) in the middle; Figure 3 (a) in the figure represents the location of the horizontal grounding electrode. The potential distribution generated at the point has a peak potential (V) concentrated near the origin (X=0, Y=0), and extends along... The current decreases symmetrically in the axial direction, and the current dissipation range is mainly limited to the vicinity of the plane where the horizontal grounding electrode is located.

[0025] Figure 3 (b) in the figure represents the location of the vertical grounding electrode. The potential distribution generated at the location is better than that of horizontal grounding bodies because the four vertical grounding bodies are arranged radially, with the peak potential (V) distributed in the circumferential direction, forming a "funnel" shape with a central depression and raised edges.

[0026] Figure 3 (c) in the figure represents the composite grounding system at the field point. The total potential distribution generated at the location, combined with the current dissipation advantages of horizontal and vertical grounding bodies, makes the potential (V) peak area larger and more uniformly distributed, and the degree of surface undulation significantly reduced, indicating that the spatial potential (V) distribution gradient is significantly reduced.

[0027] The specific process of step S3 is as follows: In the cooperative flow field model A multi-objective optimization model is established by combining the decision variable vector and constraints of the vertical grounding electrode, which is expressed as follows: (6); (7); In the formula, The minimization objective is for a multi-objective optimization model; The first objective function is the grounding resistance of the composite grounding system; The second objective function is material cost; Total material cost; This refers to the number of vertical grounding electrodes; The length of the vertical grounding electrode; Cost of grounding electrode per unit length (RMB / m); Reduce the number of vertical grounding electrodes by one; For the first Root and First Spacing between vertical grounding electrodes (m); Cost per unit length of excavation (yuan / m); For decision variable vectors; Define constraints, meaning: (8); In the formula, This refers to the minimum allowable construction spacing between vertical grounding electrodes; This refers to the maximum allowable construction spacing between vertical grounding electrodes; The shielding effect coefficient; It is an exponential function; It is a cosine function; No. Root and First The azimuth angle between the vertical grounding electrodes; in, Due to construction space constraints; the above The calculation formula is the calculation constraint for the shielding effect.

[0028] The specific process of step S4 is as follows: In the multi-objective optimization model and As a multi-objective optimization problem, the multi-objective optimization problem is decomposed into M single-objective sub-problems. Each single-objective sub-problem is processed by weight vector aggregation to obtain... ,express: (9); In the formula, The value of the aggregate function; This is the weight vector; This represents the current optimal value of the objective function, which is also the ideal point. This is for calculating the maximum value. The total number of objective functions; For the first The weight coefficients corresponding to each objective; For the first One objective function; For the first The current optimal value of each objective function; The aggregation function value is obtained by using the multi-objective evolutionary algorithm (MOEA / D). The minimization solution is performed, and the specific iterative process is as follows: The decomposition multi-objective evolutionary algorithm first calculates each weight vector. The Euclidean distance between them is chosen to be the same as the first. The nearest Euclidean distance to the weight vector Each weight vector will The subproblem corresponding to the weight vector is the first... Neighborhood structure of sub-problems; These are preset neighborhood parameters; Based on the neighborhood structure, the initial population and ideal points are generated. The initial population contains the first... The neighborhood solutions to each subproblem are then selected from the neighborhood structure, along with the parent decision variable vector. Perform crossover and mutation to generate an updated decision variable vector; Calculate the aggregation function value corresponding to the updated decision variable vector, and compare it with the aggregation function value corresponding to the current neighborhood solution. If the aggregation function value of the updated decision variable vector is smaller, the updated decision variable vector is determined to be better, and the ideal point is updated using the updated decision variable vector. With neighborhood solutions; Achieve the above ideal point After updating with the neighborhood solution, the loop is controlled by the iteration termination condition. If the current iteration number of the decomposition multi-objective evolutionary algorithm has not reached the maximum iteration number, it returns to continue the evolution and solution. If the current iteration number of the decomposition multi-objective evolutionary algorithm reaches the maximum iteration number, it stops iterating. All updated neighborhood solutions are compared pairwise, and the updated neighborhood solutions that are not mutually dominant are selected to form the non-dominated solution set. The non-dominated solution set is output as the Pareto front solution set.

[0029] like Figure 4 As shown, Figure 4 This figure compares the iterative convergence curves of different optimization algorithms. The horizontal axis represents the number of iterations, ranging from 0 to 350; the vertical axis represents the iterative convergence index, ranging from 0 to 1. The red solid line represents the Multi-Objective Evolutionary Algorithm (MOEA / D), the blue solid line represents the MOPSO algorithm, and the gray dashed line represents the weighted summation method. As the number of iterations increases, the convergence indices of all three algorithms show a decreasing trend. Among them, the Decomposition Multi-Objective Evolutionary Algorithm (MOEA / D) has the fastest convergence speed and the best stability, converging to 0.033 after 300 iterations; the MOPSO algorithm has the second fastest convergence speed, with a final value of 0.094; the weighted summation method has the slowest convergence, with a final value as high as 0.378, indicating that it is prone to getting trapped in local optima. The results verify the convergence advantage of the Decomposition Multi-Objective Evolutionary Algorithm (MOEA / D) in grounding optimization problems.

[0030] The specific process of step S5 is as follows: The process of selecting the comprehensive optimal solution from the Pareto front solution set is as follows: The Pareto front solution set is normalized, and the Euclidean distance from the objective function value corresponding to each updated decision variable vector in the Pareto front solution set to the updated ideal point is calculated. The updated decision variable vector with the smallest Euclidean distance is selected as the comprehensive optimal solution. Calculate the cooperative flow dissipation efficiency coefficient corresponding to the integrated optimal solution. ; Determine the cooperative flow dissipation efficiency coefficient corresponding to the comprehensive optimal solution. Whether it is in the positive coordination interval, when At that time, make a judgment Within the positive coordination interval, the vertical grounding electrode length, number of vertical grounding electrodes, and the first vertical grounding electrode corresponding to the comprehensive optimal solution will be considered. Root and First The spacing between the vertical grounding electrodes is output as the final optimized configuration scheme. When the calculated optimal solution corresponds to the cooperative dissipation efficiency coefficient If the output is not in the positive coordination interval, return to steps S2 to S4 to recalculate until the final optimized configuration scheme that is in the positive coordination interval is output.

[0031] To verify the effectiveness of the above comprehensive optimal solution, such as Figure 5 As shown, Figure 5 The Pareto front solution set distribution and the annotation map of the comprehensive optimal solution are shown for multi-objective optimization under four different soil resistivity (2250 Ω·m, 2750 Ω·m, 3250 Ω·m and 3750 Ω·m).

[0032] Figure 5 The horizontal axis represents the grounding resistance. The vertical axis represents the total cost. (Yuan).

[0033] Figure 5 (a) in the figure corresponds to a soil resistivity of 2250 Ω·m, and the scale range of the horizontal axis is 0 to 1200. Figure 5 (b) in the figure corresponds to a soil resistivity of 2750 Ω·m, and the horizontal axis scale ranges from 0 to 1500. Figure 5 (c) in the figure corresponds to a soil resistivity of 3250 Ω·m, and the horizontal axis scale ranges from 0 to 1800. Figure 5 (d) in the figure corresponds to a soil resistivity of 3750 Ω·m, and the scale range of the horizontal axis is 0 to 2000. Figure 5 (a) to Figure 5 The vertical axis scale of (d) in the figure ranges from 0 to 4500.

[0034] Figure 5 The blue scatter points in each subplot form a complete Pareto front solution set, clearly showing the grounding resistance. With total cost The trade-off between them: with grounding resistance Increase, total cost It is monotonically decreasing, forming a typical convex Pareto front solution set. Figure 5 Taking (a) (soil resistivity 2250 Ω·m) as an example, the optimal solution is located in the knee region of the leading edge curve (R=234.60 Ω, C=945 yuan), and the optimal solution is located in the grounding resistance... With total cost An optimal balance was achieved between them. Calculations showed that... =0.864, verifying the effectiveness of the coordinated configuration of multiple vertical and horizontal grounding electrodes. Furthermore, as the soil resistivity increases, the optimal range of grounding resistance expands.

[0035] The results show that, within the soil resistivity range of 2250~3750Ω·m, the resistance reduction rate of the synergistic configuration is 30.8%~34.5% compared with the single horizontal grounding electrode. The advantages are particularly significant in high-resistivity soil environments, providing a theoretical basis for the optimized design of grounding devices for transmission line towers, substations and distributed new energy power plants.

[0036] An electronic device includes a processor, a memory, and a bus, wherein the processor and the memory are connected via the bus, wherein the memory is used to store a set of program code, and the processor is used to call the program code stored in the memory to execute a method for optimizing the resistance reduction of multiple vertical grounding electrodes and horizontal grounding types.

[0037] A non-volatile computer storage medium stores computer-executable instructions that execute a method for optimizing resistance reduction in a combination of multiple vertical grounding electrodes and horizontal grounding configurations.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations, characterized in that, Includes the following steps: Step S1: Obtain the soil resistivity, vertical grounding electrode, and horizontal grounding electrode for different target construction areas; Step S2: Establish a cooperative current dissipation field model based on soil resistivity, vertical grounding electrode, and horizontal grounding electrode; Step S3: Establish a multi-objective optimization model based on the cooperative flow field model combined with the decision variable vector and constraints of the vertical grounding body; Step S4: Treat the multi-objective optimization model as a multi-objective optimization problem, decompose the multi-objective optimization problem into multiple single-objective sub-problems and process them to obtain the aggregation function value. Use the decomposition multi-objective evolutionary algorithm to minimize the aggregation function value and output the Pareto front solution set. Step S5: Select the comprehensive optimal solution from the Pareto front solution set, calculate the cooperative current dissipation efficiency coefficient corresponding to the comprehensive optimal solution. When the cooperative current dissipation efficiency coefficient is in the positive cooperative interval, output the comprehensive optimal solution as the final optimized configuration scheme. When the cooperative current dissipation efficiency coefficient is not in the positive cooperative interval, return to steps S2-S4 to recalculate until the final optimized configuration scheme in the positive cooperative interval is output.

2. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations as described in claim 1, characterized in that: The specific process of step S2 is as follows: Based on the soil resistivity, vertical grounding electrode and horizontal grounding electrode obtained in step S1, a three-dimensional current-dissipating field physical structure is constructed. The vertical grounding electrode is equivalent to a column current source, and the potential distribution of the column current source is calculated using the area integral method. ; For the vertical grounding electrode at the field point The potential distribution generated at that location; The horizontal grounding electrode is equivalent to a line current source, and the potential distribution of the line current source is calculated using line integral. ; For the horizontal grounding electrode at the field point The potential distribution generated at that location; based on and calculate ; For composite grounding system at the field point The total potential distribution generated at that location; Based on the total potential distribution, the field point Placed on the surface of the grounding electrode, the maximum surface potential of the grounding electrode is obtained. ; Based on the maximum surface potential of the grounding electrode and the current injected into the composite grounding system Calculate the grounding resistance of the composite grounding system ; based on Define the cooperative flow efficiency coefficient ; Establish the cooperative flow efficiency coefficient Evaluation criteria; Based on the three-dimensional diffused flow field physical structure, combined with the above , , , and the cooperative flow dissipation efficiency coefficient The evaluation criteria were used to complete the construction of the cooperative flow field model.

3. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations according to claim 2, characterized in that: In the cooperative flow field model A multi-objective optimization model is established by combining the decision variable vector and constraints of the vertical grounding electrode, which is expressed as follows: ； ； In the formula, The minimization objective is for a multi-objective optimization model; The first objective function is the grounding resistance of the composite grounding system; The second objective function is material cost; Total material cost; This refers to the number of vertical grounding electrodes; The length of the vertical grounding electrode; Cost of materials per unit length of grounding electrode; Reduce the number of vertical grounding electrodes by one; For the first Root and First The spacing between vertical grounding electrodes; Cost per unit length of excavation; This is a vector of decision variables.

4. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations according to claim 3, characterized in that: Define constraints, meaning: ； In the formula, This refers to the minimum allowable construction spacing between vertical grounding electrodes; This refers to the maximum allowable construction spacing between vertical grounding electrodes; The shielding effect coefficient; This refers to the number of vertical grounding electrodes; It is an exponential function; It is a cosine function; No. Root and First The azimuth angle between the vertical grounding electrodes; Among them, the constraints include construction space constraints and shielding effect constraints; Due to construction space constraints; the above The calculation formula is the shielding effect constraint.

5. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations according to claim 4, characterized in that: The specific process for obtaining the aggregate function value is as follows: In the multi-objective optimization model and As a multi-objective optimization problem, the multi-objective optimization problem is decomposed into M single-objective sub-problems. Each single-objective sub-problem is processed by weight vector aggregation to obtain... ,express: ； In the formula, The value of the aggregate function; This is the weight vector; This represents the current optimal value of the objective function, which is also the ideal point. This is for calculating the maximum value. The total number of objective functions; For the first The weight coefficients corresponding to each objective; For the first One objective function; For the first The current optimal value of each objective function.

6. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations according to claim 5, characterized in that: The specific process of outputting the Pareto front solution set is as follows: The aggregation function value is obtained by using a decomposition multi-objective evolutionary algorithm. The minimization solution is performed, and the specific iterative process is as follows: The decomposition multi-objective evolutionary algorithm first calculates each weight vector. The Euclidean distance between them is chosen to be the same as the first. The nearest Euclidean distance to the weight vector Each weight vector will The subproblem corresponding to the weight vector is the first... Neighborhood structure of sub-problems; These are preset neighborhood parameters; Based on the neighborhood structure, the initial population and ideal points are generated. The initial population contains the first... The neighborhood solutions to each subproblem are then selected from the neighborhood structure, along with the parent decision variable vector. Perform crossover and mutation to generate an updated decision variable vector; Calculate the aggregation function value corresponding to the updated decision variable vector, and compare it with the aggregation function value corresponding to the current neighborhood solution. If the aggregation function value of the updated decision variable vector is smaller, the updated decision variable vector is determined to be better, and the ideal point is updated using the updated decision variable vector. With neighborhood solutions; Achieve the above ideal point After updating with the neighborhood solution, the loop is controlled by the iteration termination condition. If the current iteration number of the decomposition multi-objective evolutionary algorithm has not reached the maximum iteration number, it returns to continue the evolution and solution. If the current iteration number of the decomposition multi-objective evolutionary algorithm reaches the maximum iteration number, it stops iterating. All updated neighborhood solutions are compared pairwise, and the updated neighborhood solutions that are not mutually dominant are selected to form the non-dominated solution set. The non-dominated solution set is output as the Pareto front solution set.

7. The method for optimizing resistance reduction through coordinated use of multiple vertical grounding electrodes and horizontal grounding configurations according to claim 6, characterized in that: The specific process of step S5 is as follows: The process of selecting the comprehensive optimal solution from the Pareto front solution set is as follows: The Pareto front solution set is normalized, and the Euclidean distance from the objective function value corresponding to each updated decision variable vector in the Pareto front solution set to the updated ideal point is calculated. The updated decision variable vector with the smallest Euclidean distance is selected as the comprehensive optimal solution. Calculate the cooperative flow dissipation efficiency coefficient corresponding to the integrated optimal solution. ; Determine the cooperative flow dissipation efficiency coefficient corresponding to the comprehensive optimal solution. Whether it is in the positive coordination interval, when At that time, make a judgment Within the positive coordination interval, the vertical grounding electrode length, number of vertical grounding electrodes, and the first vertical grounding electrode corresponding to the comprehensive optimal solution will be considered. Root and First The spacing between the vertical grounding electrodes is output as the final optimized configuration scheme. When the calculated optimal solution corresponds to the cooperative dissipation efficiency coefficient If the output is not in the positive coordination interval, return to steps S2 to S4 to recalculate until the final optimized configuration scheme that is in the positive coordination interval is output.

8. An electronic device, characterized in that, The device includes a processor, a memory, and a bus. The processor and the memory are connected via the bus. The memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute the method for optimizing the cooperative resistance reduction of multiple vertical grounding electrodes and horizontal grounding types as described in any one of claims 1-7.

9. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer can execute instructions to perform the method for optimizing the resistance reduction of multiple vertical grounding electrodes and horizontal grounding types as described in any one of claims 1-7.

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