Substation grounding grid collaborative optimization method under layered soil conditions
By constructing a composite soil structure model and employing a multi-objective particle swarm optimization algorithm, the problems of soil model simplification and single optimization method in substation grounding grid design were solved, achieving high-precision and comprehensive safety and economic optimization of the grounding grid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YUNNAN HENGCHANG DESIGN CONSULTING CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-06-05
Smart Images
Figure CN122154486A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system technology, and more specifically to a method for collaborative optimization of substation grounding grids under stratified soil conditions. Background Technology
[0002] The substation grounding grid is a key facility to ensure the safe operation of power equipment and the safety of personnel. Its main functions are to reduce grounding resistance, equalize the ground surface potential, and limit contact voltage and step voltage. The soil at actual substation sites often presents a complex layered structure, including different layers in the horizontal direction and abrupt changes in the medium in the vertical direction. The traditional grounding grid design process first involves surveying the soil resistivity at the site to determine the target grounding resistance. Then, it involves preliminary design of the dimensions, spacing, and burial depth of the horizontal grid and vertical grounding electrodes, calculation of grounding resistance, step voltage, and contact voltage, verification of thermal stability, grid layout, and finally, completion of construction drawings. Existing soil models are overly simplified, only considering uniform or horizontal stratification structures and ignoring the influence of vertical stratification on soil resistivity distribution, resulting in large deviations in grounding grid simulation calculations. Traditional grounding grid optimization methods often employ single-objective optimization, failing to simultaneously consider multiple safety indicators such as surface potential non-uniformity coefficient and contact voltage, resulting in an incomplete design scheme. Existing optimization processes use only one design variable and lack the ability to coordinate and adjust different parameters in a systematic manner, thus limiting the optimization effect. Summary of the Invention
[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for collaborative optimization of substation grounding grid under layered soil conditions to solve the technical problems mentioned in the background art.
[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for collaborative optimization of substation grounding grids under stratified soil conditions, comprising the following steps: Step S1: Set up measuring lines and measuring points in the substation site, and use the Wenner four-electrode method to measure resistivity to form a field survey dataset. Step S2: Construct a composite soil structure model that includes horizontal and vertical stratification. Based on the field survey dataset, perform forward and inverse calculations to obtain the optimal composite soil structure parameters. Step S3: Based on the optimal composite soil structure parameters, establish a simulation model of the substation grounding grid, and use a multi-objective particle swarm optimization algorithm to perform collaborative optimization of the grounding grid; Step S4: Output the optimized grounding grid design scheme and save it.
[0005] In a preferred embodiment, in step S1, parallel measuring lines with a spacing of 15m are arranged along orthogonal directions within the substation site. The parallel measuring lines cover the substation site, and multiple measuring points are set on each parallel measuring line. The spacing between the measuring points is set in a logarithmic increasing manner, covering the detection range from shallow to deep layers. The minimum spacing is 1m, and the maximum spacing is 50m.
[0006] In a preferred embodiment, in step S1, at each measuring point, four electrodes are driven into the ground at equal intervals, with the electrode depth being 1 / 20 of the measuring point interval. Current is injected into the two outer electrodes, and the potential difference between the two inner electrodes is measured. The electrode spacing is changed, and the apparent resistivity P corresponding to different electrode spacings is recorded. The formula for calculating the apparent resistivity P is P=2πaΔV / I, where a is the electrode spacing, ΔV is the potential difference between the two inner electrodes, and I is the current injected into the two outer electrodes. The electrode spacing a and the apparent resistivity P calculated based on the electrode spacing a are summarized to form a field survey dataset.
[0007] In a preferred embodiment, in step S2, a composite soil structure model including horizontal and vertical layers is constructed, wherein the horizontal layering parameters include the resistivity and thickness of each layer, and the vertical layering parameters include the vertical interface position and the resistivity on both sides of the interface. Forward and inverse calculations are performed. During the forward calculation, based on the composite soil structure model, the Green's function in the multi-layer soil is calculated using the mirror method, and then the theoretical resistivity LP corresponding to the measured electrode spacing is calculated.
[0008] In a preferred embodiment, during step S2, the inversion calculation uses the minimum root mean square error of the apparent resistivity P and the theoretical resistivity LP as the objective function. An improved particle swarm optimization algorithm is used to solve for the parameter vector, outputting the optimal parameter θ that minimizes the objective function. The root mean square error objective function is... In the formula, M is the total number of measurements at all measuring points and electrode spacing, θ is the parameter vector to be optimized, j is the j-th measuring point, Pj is the resistivity of the j-th side, and LPj is the theoretical resistivity of the j-th measuring point.
[0009] In a preferred embodiment, when establishing the simulation model of the substation grounding grid in step S3, the finite element method is used to establish the grounding grid simulation model. The soil model is set as the optimal composite soil structure model obtained by inversion in step S2. The grid is locally refined at the interface between the horizontal and vertical layers. The grid size of the refined area is 1 / 5 to 1 / 10 of the minimum conductor spacing of the grounding grid. When optimizing the grounding grid, the grounding resistance, surface potential distribution, contact voltage and step voltage of the grounding grid are calculated.
[0010] In a preferred embodiment, the collaborative optimization in step S3 adopts a Pareto-dominated multi-objective particle swarm optimization algorithm, with the optimization objectives being the minimum grounding resistance, the minimum surface potential non-uniformity coefficient, the maximum contact voltage, and the maximum step voltage being lower than their respective safety limits. The optimized design variables include the spacing of horizontal conductors, the arrangement depth of vertical conductors, the conductor cross-sectional shape and material, and the number and position of equalizing rings.
[0011] In a preferred embodiment, the optimized grounding grid design scheme output in step S4 includes: the burial depth of the horizontal grounding electrode, the length and spacing of the vertical grounding electrode, the conductor connection method, anti-corrosion measures, and the location and amount of resistance reducing agent.
[0012] In a preferred embodiment, during step S4, when storing the data, the optimized grounding grid design scheme is associated with the composite soil structure parameters obtained by inversion and stored in the substation grounding grid database. The data in the database can generate a design report that conforms to power industry standards for display.
[0013] In a preferred embodiment, each data record stored in the substation grounding grid database includes: a unique identifier ID for the composite soil structure parameters, a horizontal layer resistivity vector, a horizontal layer thickness vector, a vertical interface location vector, resistivity on both sides of the vertical interface, optimized grounding grid geometric parameters, a timestamp of the design report generation, and the corresponding power industry standard version number.
[0014] The technical effects and advantages of this invention are as follows: 1. This invention constructs a composite soil structure model that includes horizontal and vertical layers, and performs forward and inverse calculations based on on-site Wenner quadrupole method measured data to obtain the optimal composite soil structure parameters. This significantly improves the realism of the soil model and the accuracy of the grounding grid simulation calculation, providing reliable data support for subsequent optimization and ensuring the accuracy of the optimization. 2. This invention employs a Pareto-dominated multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing grounding resistance, minimizing the surface potential non-uniformity coefficient, and ensuring that the maximum contact voltage and maximum step voltage are below the safety limits. This achieves synergistic optimization of multiple safety indicators, effectively improving the safety and economy of the grounding grid, and making the final design scheme sufficiently comprehensive. 3. This invention uses the spacing of horizontal conductors, the arrangement depth of vertical conductors, the cross-sectional shape and material of conductors, and the number and position of equalizing rings as optimization variables. It uses a multi-objective particle swarm optimization algorithm for collaborative optimization and employs the finite element method to locally refine the mesh at the layer interface. This improves the global optimization capability of the grounding grid design, reduces the grounding resistance and the ground surface potential gradient, and ensures that the contact voltage and step voltage meet the safety limit requirements. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the substation grounding grid collaborative optimization method of the present invention. Detailed Implementation
[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The substation grounding grid collaborative optimization method under layered soil conditions involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Reference Figure 1 This invention provides a method for collaborative optimization of substation grounding grids under stratified soil conditions, comprising the following steps: Step S1: Set up measuring lines and measuring points in the substation site, and use the Wenner four-electrode method to measure resistivity to form a field survey dataset. Step S2: Construct a composite soil structure model that includes horizontal and vertical stratification. Based on the field survey dataset, perform forward and inverse calculations to obtain the optimal composite soil structure parameters. Step S3: Based on the optimal composite soil structure parameters, establish a simulation model of the substation grounding grid, and use a multi-objective particle swarm optimization algorithm to perform collaborative optimization of the grounding grid; Step S4: Output the optimized grounding grid design scheme and save it.
[0018] In this embodiment, the application can be specifically described as a complete process of on-site survey → composite soil modeling → forward and inverse calculation → multi-objective particle swarm optimization → scheme output, thereby realizing integrated collaborative optimization from soil parameter acquisition to grounding grid design. Therefore, compared with traditional methods, this application can systematically consider soil stratification characteristics, and consider them in both horizontal and vertical directions, taking into account multiple safety and economic indicators, significantly improving the accuracy, safety and economy of grounding grid design, and solving the problems of simplified soil models, single optimization objectives and isolated design variables in the prior art.
[0019] Reference Figure 1 In step S1, parallel measuring lines with a spacing of 15m are arranged along the orthogonal direction within the substation site. The parallel measuring lines cover the substation site. Multiple measuring points are set on each parallel measuring line. The spacing between the measuring points is set in a logarithmic increasing manner to cover the detection range from shallow to deep layers. The minimum spacing is 1m and the maximum spacing is 50m.
[0020] In this embodiment, by laying out parallel survey lines with a spacing of 15m along an orthogonal direction and adopting a logarithmic increase in the spacing between survey points, the application can cover the detection range from shallow to deep layers with fewer survey points. This ensures both accurate measurement of shallow soil resistivity and effective detection of deep soil structure, avoids redundancy or omission of survey points, improves on-site survey efficiency and data quality, and provides more reliable basic data for subsequent inversion.
[0021] Reference Figure 1 In step S1, at each measuring point, four electrodes are driven into the ground at equal intervals, with the electrode depth being 1 / 20 of the measuring point spacing. Current is injected into the two outer electrodes, and the potential difference between the two inner electrodes is measured. The electrode spacing is changed, and the apparent resistivity P corresponding to different electrode spacings is recorded. The formula for calculating the apparent resistivity P is P=2πaΔV / I, where a is the electrode spacing, ΔV is the potential difference between the two inner electrodes, and I is the current injected into the two outer electrodes. The electrode spacing a and the apparent resistivity P calculated based on the electrode spacing a are summarized to form a field survey dataset.
[0022] In this embodiment, the Winner quadrupole method is used for measurement, and the electrode depth is 1 / 20 of the electrode spacing. This reduces the influence of electrode contact resistance and surface inhomogeneity. Apparent resistivity at different depths is measured by changing the electrode spacing, and the formula is used... Accurate calculation of apparent resistivity can obtain a resistivity dataset that reflects the vertical and horizontal changes of soil, providing accurate input for constructing a composite soil structure model and improving the reliability of the inversion results.
[0023] Reference Figure 1 In step S2, a composite soil structure model containing horizontal and vertical layers is constructed. The horizontal layer parameters include the resistivity and thickness of each layer, and the vertical layer parameters include the vertical interface position and the resistivity on both sides of the interface. Forward and inverse calculations are performed. During the forward calculation, based on the composite soil structure model, the Green's function in the multi-layer soil is calculated using the mirror method, and then the theoretical resistivity LP corresponding to the measured electrode spacing is calculated.
[0024] In this embodiment, the present application constructs a composite soil structure model that includes both horizontal and vertical stratification (vertical boundaries), breaking through the limitations of traditional models that only consider horizontal stratification or uniform soil. The model constructed in this application is more comprehensive. After construction, the forward modeling calculation uses the mirror method to calculate the Green's function of the multi-layered soil, which can efficiently and accurately calculate the theoretical resistivity under arbitrary electrode arrangement, providing accurate theoretical values for inversion and making the soil model closer to the complex geological conditions of the actual substation site.
[0025] Reference Figure 1In step S2, during the inversion calculation, the objective function is to minimize the root mean square error of the apparent resistivity P and the theoretical resistivity LP. An improved particle swarm optimization algorithm is used to solve for the parameter vector, outputting the optimal parameter θ that minimizes the objective function. The root mean square error objective function is... In the formula, M is the total number of measurements at all measuring points and electrode spacing, θ is the parameter vector to be optimized, j is the j-th measuring point, Pj is the resistivity of the j-th side, and LPj is the theoretical resistivity of the j-th measuring point.
[0026] In this embodiment, when performing inversion calculations, the objective function is to minimize the root mean square error between the measured apparent resistivity and the theoretical resistivity. An improved particle swarm optimization algorithm is used to solve the parameter vector. This objective function can effectively measure the model fitting accuracy and avoid the absolute error being affected by the resistivity order of magnitude. The improved particle swarm optimization algorithm has the characteristics of strong global search capability and fast convergence speed. It can stably output the optimal composite soil structure parameters that minimize the error and ensure the authenticity of the soil model parameters.
[0027] Reference Figure 1 In step S3, when establishing the simulation model of the substation grounding grid, the finite element method is used to establish the grounding grid simulation model. The soil model is set as the optimal composite soil structure model obtained by inversion in step S2. The grid is locally refined at the interface between the horizontal and vertical layers. The grid size of the refined area is 1 / 5 to 1 / 10 of the minimum conductor spacing of the grounding grid. When optimizing the grounding grid, the grounding resistance, surface potential distribution, contact voltage and step voltage of the grounding grid are calculated.
[0028] In this embodiment, the finite element method is used to establish a grounding grid simulation model, and the soil model is set as the optimal composite soil structure obtained by inversion. The grid is locally refined at the interface between the horizontal and vertical layers, which can accurately simulate the drastic changes in electric field and potential at the layer interface, avoid the calculation error caused by conventional uniform grid, and accurately calculate the grounding resistance, surface potential distribution, contact voltage and step voltage, providing a high-fidelity performance evaluation for subsequent multi-objective optimization.
[0029] Reference Figure 1 The collaborative optimization in step S3 adopts a Pareto-dominated multi-objective particle swarm optimization algorithm. The optimization objectives are to minimize the grounding resistance, minimize the surface potential non-uniformity coefficient, and ensure that the maximum contact voltage and the maximum step voltage are lower than their respective safety limits. The optimized design variables include the spacing of horizontal conductors, the arrangement depth of vertical conductors, the cross-sectional shape and material of conductors, and the number and position of equalizing rings. Pareto-dominated multi-objective particle swarm optimization algorithms include the following steps: Step A1: Initialize the particle swarm, set the population size to 50, the position vector of each particle to parameter θ, and the velocity vector to be randomly initialized; Step A2: Calculate the fitness value for each particle. , where M is the number of electrode spacings; Step A3: Adopt adaptive inertia weights Where w_max=0.9, w_min=0.4, t is the current iteration number, and T_max is the maximum iteration number; Step A4: Every 10 iterations, apply a Gaussian perturbation to the global optimal solution to prevent premature convergence; Step A5: When the relative change in the global optimal fitness is less than 1e-4 in 20 consecutive iterations or the maximum number of iterations of 200 is reached, the iteration is terminated.
[0030] In this embodiment, the application employs a Pareto-dominated multi-objective particle swarm optimization algorithm. The optimization objectives are to minimize grounding resistance, minimize the surface potential non-uniformity coefficient, and ensure that the contact voltage and step voltage are below the safety limit. This achieves simultaneous optimization of multiple conflicting indicators. The optimization variables include horizontal conductor spacing, vertical conductor placement depth, conductor cross-sectional shape and material, number and location of equalizing rings, etc. This system can coordinately adjust the key parameters of the grounding grid, avoid the limitations of single-variable optimization, and finally obtain the globally optimal grounding grid design scheme.
[0031] Reference Figure 1 The optimized grounding grid design scheme output in step S4 includes: the burial depth of the horizontal grounding body, the length and spacing of the vertical grounding electrode, the conductor connection method, anti-corrosion measures, and the location and amount of resistance reducing agent.
[0032] In this embodiment of the application, the final optimized design scheme output by this application specifically includes detailed engineering parameters such as the burial depth of the horizontal grounding electrode, the length and spacing of the vertical grounding electrode, the conductor connection method, anti-corrosion measures, and the location and amount of resistance-reducing agent. These parameters can guide the construction drawing design and on-site construction, ensure the feasibility of the optimization results, effectively reduce grounding resistance, balance the surface potential, suppress contact voltage and step voltage, and improve the corrosion resistance and service life of the grounding grid.
[0033] Reference Figure 1In step S4, during storage, the optimized grounding grid design scheme is associated with the composite soil structure parameters obtained by inversion and stored in the substation grounding grid database. The data in the database can generate a design report that conforms to power industry standards for display. Each data record stored in the substation grounding grid database includes: a unique identifier ID of the composite soil structure parameters, a horizontal layer resistivity vector, a horizontal layer thickness vector, a vertical interface position vector, resistivity on both sides of the vertical interface, optimized grounding grid geometric parameters, a timestamp of the design report generation, and the corresponding power industry standard version number.
[0034] In this embodiment, the optimized grounding grid design scheme and the composite soil structure parameters obtained by inversion are associated and stored in the substation grounding grid database. Design reports conforming to power industry standards can be generated as needed, thereby realizing the structured storage and standardized output of design data. This facilitates the reference and reuse of similar substations, design review, and operation and maintenance management, and improves the standardization and traceability of design work. Each record in the database contains a unique identifier ID of the composite soil structure parameters, horizontal layer resistivity vector and thickness vector, vertical interface position and resistivity on both sides, optimized grounding grid geometric parameters, timestamp, and standard version number. The structured storage method adopted in this application supports fast retrieval, version comparison, and data reuse, which facilitates the analysis and optimization of historical design schemes, and provides a standardized data foundation for future intelligent design systems or big data analysis.
[0035] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. The units and algorithm steps of the various examples described in the embodiments can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0036] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0038] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for collaborative optimization of substation grounding grids under stratified soil conditions, characterized in that: Includes the following steps: Step S1: Set up measuring lines and measuring points in the substation site, and use the Wenner four-electrode method to measure resistivity to form a field survey dataset. Step S2: Construct a composite soil structure model that includes horizontal and vertical stratification. Based on the field survey dataset, perform forward and inverse calculations to obtain the optimal composite soil structure parameters. Step S3: Based on the optimal composite soil structure parameters, establish a simulation model of the substation grounding grid, and use a multi-objective particle swarm optimization algorithm to perform collaborative optimization of the grounding grid; Step S4: Output the optimized grounding grid design scheme and save it.
2. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: In step S1, parallel measuring lines with a spacing of 15m are arranged along the orthogonal direction within the substation site. The parallel measuring lines cover the substation site, and multiple measuring points are set on each parallel measuring line. The spacing between the measuring points is set in a logarithmic increasing manner, covering the detection range from shallow to deep layers. The minimum spacing is 1m and the maximum spacing is 50m.
3. The method for collaborative optimization of substation grounding grids under stratified soil conditions according to claim 1, characterized in that: In step S1, at each measuring point, four electrodes are driven into the ground at equal intervals, with the electrode depth being 1 / 20 of the measuring point spacing. Current is injected into the two outer electrodes, and the potential difference between the two inner electrodes is measured. The electrode spacing is changed, and the apparent resistivity P corresponding to different electrode spacings is recorded. The formula for calculating the apparent resistivity P is P=2πaΔV / I, where a is the electrode spacing, ΔV is the potential difference between the two inner electrodes, and I is the current injected into the two outer electrodes. The electrode spacing a and the apparent resistivity P calculated based on the electrode spacing a are summarized to form a field survey dataset.
4. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: In step S2, a composite soil structure model containing horizontal and vertical layers is constructed. The horizontal layer parameters include the resistivity and thickness of each layer, and the vertical layer parameters include the vertical interface position and the resistivity on both sides of the interface. Forward and inverse calculations are performed. During the forward calculation, based on the composite soil structure model, the Green's function in the multi-layer soil is calculated using the mirror method, and then the theoretical resistivity LP corresponding to the measured electrode spacing is calculated.
5. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: In step S2, during the inversion calculation, the objective function is to minimize the root mean square error of both the apparent resistivity P and the theoretical resistivity LP. An improved particle swarm optimization algorithm is used to solve for the parameter vector, outputting the optimal parameter θ that minimizes the objective function. The root mean square error objective function is... In the formula, M is the total number of measurements at all measuring points and electrode spacing, θ is the parameter vector to be optimized, j is the j-th measuring point, Pj is the resistivity of the j-th side, and LPj is the theoretical resistivity of the j-th measuring point.
6. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: In step S3, when establishing the simulation model of the substation grounding grid, the finite element method is used to establish the grounding grid simulation model. The soil model is set as the optimal composite soil structure model obtained by inversion in step S2. The grid is locally refined at the interface between the horizontal and vertical layers. The grid size of the refined area is 1 / 5 to 1 / 10 of the minimum conductor spacing of the grounding grid. When optimizing the grounding grid, the grounding resistance, surface potential distribution, contact voltage and step voltage of the grounding grid are calculated.
7. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: The collaborative optimization in step S3 adopts a Pareto-dominated multi-objective particle swarm optimization algorithm. The optimization objectives are to minimize the grounding resistance, minimize the surface potential non-uniformity coefficient, and ensure that the maximum contact voltage and the maximum step voltage are lower than their respective safety limits. The optimized design variables include the spacing of horizontal conductors, the arrangement depth of vertical conductors, the cross-sectional shape and material of conductors, and the number and position of equalizing rings.
8. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 1, characterized in that: The optimized grounding grid design scheme output in step S4 includes: the burial depth of the horizontal grounding body, the length and spacing of the vertical grounding electrode, the conductor connection method, anti-corrosion measures, and the location and amount of resistance reducing agent.
9. The method for collaborative optimization of substation grounding grids under stratified soil conditions according to claim 1, characterized in that: In step S4, during storage, the optimized grounding grid design scheme is associated with the composite soil structure parameters obtained by inversion and stored in the substation grounding grid database. The data in the database can generate a design report that conforms to power industry standards for display.
10. The method for collaborative optimization of substation grounding grid under stratified soil conditions according to claim 9, characterized in that: Each data record stored in the substation grounding grid database includes: a unique identifier ID for the composite soil structure parameters, a horizontal layer resistivity vector, a horizontal layer thickness vector, a vertical interface location vector, resistivity on both sides of the vertical interface, optimized grounding grid geometric parameters, a timestamp of the design report generation, and the corresponding power industry standard version number.