10kV distribution network composite cross arm lightning protection simulation analysis method and system

Abstract

The invention relates to the technical field of lightning protection, and discloses a 10kV distribution network composite cross arm lightning protection simulation analysis method and system, and the method comprises the steps: obtaining the shape data, material parameters and lightning impact waveform information of a cross arm, carrying out the three-dimensional modeling and electric field distribution calculation, and obtaining an electric field intensity peak value and electric field space data under the lightning impact; when the electric field intensity peak value exceeds a breakdown electric field intensity threshold value, high-risk area identification is carried out, material voltage withstanding performance verification is carried out in combination with material parameters and lightning shock waveform information, material optimization parameters are output, a three-dimensional model is updated, a three-dimensional electric field is recalculated, and new electric field space data and an electric field intensity change trend are obtained; carrying out geometric configuration screening, outputting an optimal cross arm section form, carrying out lightning protection stability verification, and outputting a final material combination and stability rating; and integrating the new electric field space data, the electric field intensity change trend, the final material combination and stability rating, and outputting a cross arm lightning protection report. The method improves the lightning protection capability of the cross arm.

Description

Technical Field

[0001] This invention relates to the field of lightning protection technology, and in particular to a simulation analysis method and system for lightning protection of 10kV distribution network composite crossarm. Background Technology

[0002] Currently, in 10kV distribution network systems in areas prone to lightning strikes, crossarms, as key components supporting conductors and insulators, directly impact the reliability and safety of the power grid operation due to their lightning protection performance. The complex three-dimensional structure and diverse material combinations (such as composite materials and metal components) of crossarms collectively determine their electric field distribution characteristics under lightning impact. However, due to the variable cross-sectional shape and complex boundary conditions of crossarms, the dielectric parameters of the materials exhibit nonlinear responses during lightning transients. Traditional analysis methods lack advanced electrical digital data processing technology, making it difficult to accurately characterize the dynamic distribution of the three-dimensional electric field. This results in a lack of reliable basis for lightning protection design, easily leading to lightning strike failures or wasted resources.

[0003] In one existing technology, the actual three-dimensional crossarm structure is simplified into a two-dimensional cross-sectional model for analysis. In the boundary condition setting stage, a standard lightning impulse waveform is used as the excitation source, and idealized uniform boundary conditions are configured. Material properties are defined as fixed parameter values, neglecting environmental factors and variations in electromagnetic properties. Based on the simplified model, a static field solver is used to calculate the electric field, employing a uniform mesh generation technique to process the entire computational domain. The finite element method is used to solve for the electric field distribution during the calculation, obtaining a stable solution through iterative calculation. Finally, an electric field distribution cloud map on the two-dimensional cross-section is output, the maximum electric field strength value is extracted, and it is compared with the air breakdown threshold. The comparison results are used to evaluate the lightning protection performance of the crossarm and provide a reference for subsequent design. The entire process is based on a simplified model and idealized assumptions, obtaining electric field distribution data through a standardized calculation procedure.

[0004] Existing technologies rely on simplified models, making it difficult to accurately simulate the dynamic electric field distribution of crossarms under complex three-dimensional structures. Therefore, existing technologies cannot effectively improve the lightning protection capability of crossarms. Summary of the Invention

[0005] This invention provides a simulation analysis method and system for lightning protection of 10kV distribution network composite crossarms, in order to improve the lightning protection capability of crossarms.

[0006] Firstly, in order to solve the above-mentioned technical problems, this invention provides a simulation analysis method for lightning protection of 10kV distribution network composite crossarms, including:

[0007] Acquire the shape data, material parameters, and lightning impact waveform information of the crossarm;

[0008] Based on the shape data, material parameters, and lightning impact waveform information, a three-dimensional model is created to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0009] Based on the gridded three-dimensional model, the three-dimensional electric field distribution is calculated to obtain the peak electric field intensity and electric field spatial data under lightning impact.

[0010] When the peak electric field strength exceeds the preset breakdown electric field strength threshold, high-risk areas are identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, and material optimization parameters are output.

[0011] Based on the material optimization parameters, update the meshed three-dimensional model, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity variation trend;

[0012] Based on the new electric field spatial data and the electric field intensity variation trend, geometric configuration screening is performed to output the optimal crossarm cross section form;

[0013] Based on the optimal crossarm cross section form and the new electric field space data, the lightning protection stability is verified, and the final material combination and stability rating are output.

[0014] By integrating the new electric field spatial data, the electric field intensity variation trend, the final material combination, and the stability rating, a crossarm lightning protection report is output.

[0015] In one optional implementation, the step of performing three-dimensional modeling based on the shape data, the material parameters, and the lightning impact waveform information to obtain a meshed three-dimensional model including dynamic boundary conditions includes:

[0016] Based on the shape data and material parameters, outlier detection and removal are performed using the median absolute deviation, and a hash index algorithm is combined for efficient querying to obtain standard data.

[0017] Based on the standard data, a three-dimensional solid is reconstructed using non-uniform rational B-spline curves, and a gradient descent algorithm is used to smooth the geometric structure to obtain a three-dimensional model.

[0018] Based on the lightning impulse waveform information, the harmonic content is analyzed by fast Fourier transform. When the harmonic content is lower than the preset harmonic content threshold, the waveform parameters are analyzed by the finite difference time-domain method, and time-varying boundary conditions are generated by the total field / scattered field separation algorithm.

[0019] Based on the three-dimensional model and the time-varying boundary conditions, the mesh is generated using the leading edge method, and the mesh quality is optimized using the Laplace smoothing algorithm to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0020] In one optional implementation, the step of calculating the three-dimensional electric field distribution based on the gridded three-dimensional model to obtain the peak electric field intensity and spatial electric field data under lightning impact includes:

[0021] Based on the gridded three-dimensional model and the lightning impact waveform information, the three-dimensional electric field is numerically calculated using the finite-difference time-domain method, and the field strength distribution is analyzed using the bilinear interpolation algorithm to obtain the three-dimensional electric field distribution data.

[0022] Based on the electric field spatial data, the peak electric field intensity is identified using an automatic multi-scale peak search algorithm.

[0023] When the peak value of the electric field intensity exceeds the preset insulation tolerance threshold, an optimized early warning signal is generated and the calculation continues to be completed to obtain the electric field space data.

[0024] In one optional implementation, when the peak electric field strength exceeds a preset breakdown electric field strength threshold, a high-risk area is identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, outputting material optimization parameters, including:

[0025] Based on the electric field spatial data, high-risk areas are identified using a region growing algorithm, and the coordinates of the high-risk areas are obtained.

[0026] Based on the coordinates of the high-risk area, the dielectric constant and conductivity parameters of the corresponding locations are retrieved from the material parameter database using a hash index algorithm to obtain the material parameters of the high-risk area;

[0027] Based on the material parameters of the high-risk area and the lightning impact waveform information, the material pressure resistance performance is verified using a decision tree algorithm to obtain the material parameter verification results.

[0028] When the material parameter verification results show that the material performance does not meet the preset performance requirements, a material alternative is searched using a non-dominated sorting genetic algorithm to obtain the material optimization parameters.

[0029] In one optional implementation, updating the meshed 3D model based on the material optimization parameters and recalculating the 3D electric field distribution to obtain new electric field spatial data and electric field intensity variation trends includes:

[0030] Based on the material optimization parameters, the dielectric constant and conductivity parameters of the meshed 3D model are updated using a hash table mapping algorithm to obtain a new 3D model;

[0031] Based on the new three-dimensional model, spatial discretization is performed using the forward propulsion method, and boundary parameters are configured using the Dirichlet boundary condition setting method to obtain discretized spatial coordinates.

[0032] Based on the discretized spatial coordinates and the lightning impulse waveform information, the electric field distribution is calculated using the finite-difference time-domain method to obtain new electric field spatial data.

[0033] Based on the new electric field spatial data and the electric field spatial data, the transient field strength data is processed by the short-time Fourier transform algorithm to obtain the trend of electric field strength change.

[0034] In one optional implementation, the step of performing geometric configuration screening based on the new electric field spatial data and the electric field intensity variation trend to output the optimal crossarm cross-section shape includes:

[0035] The cross-section forms of the crossarms are obtained from a pre-established geometric shape database and classified using the K-means clustering algorithm to obtain the classified cross-section forms.

[0036] Based on the classified crossarm cross-section form, the new electric field spatial data, and the electric field intensity variation trend, stress field simulation calculation is performed by finite element analysis to obtain stress distribution data and multi-condition stability data for each cross-section form under different working conditions.

[0037] When the maximum stress value in the stress distribution data is lower than the preset allowable stress threshold of the material, the failure risk of each cross-section is quantitatively assessed by the entropy weight method to obtain the risk assessment result.

[0038] Based on the risk assessment results and the multi-condition stability data, the distribution pattern characteristics are extracted using principal component analysis to determine the optimal cross-section form.

[0039] In one optional implementation, the step of verifying lightning protection stability based on the optimal crossarm cross-section and the new electric field space data, and outputting the final material combination and stability rating, includes:

[0040] Based on the optimal cross-section shape, matching candidate material combinations are retrieved from the pre-stored material database using the K-means clustering algorithm to obtain material combination data;

[0041] Based on the material combination data and the new electric field spatial data, the electric field distribution and mechanical stress distribution are calculated by the finite element analysis method to obtain multi-physics field coupling data containing electric field strength and mechanical stress values.

[0042] When the electric field strength is lower than a preset electric field strength threshold and the mechanical stress value is lower than a preset mechanical stress threshold, the material's compressive strength and mechanical stability are verified using a decision tree algorithm to obtain the performance verification results.

[0043] Based on the performance verification results, the comprehensive stability index of the material combination data is quantified by the entropy weight method to obtain the stability rating result;

[0044] By fusing the stability rating results with multiphysics coupling data using principal component analysis, the optimal performance characteristics are extracted, and the final material combination and corresponding stability rating are determined.

[0045] Secondly, this invention provides a 10kV distribution network composite crossarm lightning protection simulation analysis system, comprising:

[0046] The data acquisition module is used to acquire the shape data, material parameters, and lightning impact waveform information of the crossarm;

[0047] The three-dimensional model building module is used to perform three-dimensional modeling based on the shape data, the material parameters and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0048] The three-dimensional electric field analysis module is used to calculate the three-dimensional electric field distribution based on the gridded three-dimensional model, and obtain the peak electric field intensity and electric field spatial data under lightning impact.

[0049] The material optimization module is used to identify high-risk areas when the peak electric field strength exceeds a preset breakdown electric field strength threshold, and to verify the material withstand voltage performance by combining the material parameters and the lightning impulse waveform information, and output material optimization parameters.

[0050] The electric field trend analysis module is used to update the meshed three-dimensional model according to the material optimization parameters, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity change trend.

[0051] The optimal crossarm analysis module is used to perform geometric configuration screening based on the new electric field spatial data and the electric field intensity variation trend, and output the optimal crossarm cross-section form;

[0052] The material combination analysis module is used to verify the lightning protection stability based on the optimal crossarm cross section form and the new electric field space data, and output the final material combination and stability rating.

[0053] The output module integrates the new electric field spatial data, the electric field intensity variation trend, the final material combination, and the stability rating to output a crossarm lightning protection report.

[0054] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the 10kV distribution network composite crossarm lightning protection simulation analysis method described in any one of the above.

[0055] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the 10kV distribution network composite crossarm lightning protection simulation analysis method described in any one of the above.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) This invention detects and removes outliers by using the absolute deviation of the median, which can effectively remove noise and error information in the data and ensure the quality of the input data. Then, it combines the hash index algorithm to perform efficient data query, which can quickly and accurately obtain the required data and improve the data processing efficiency. Combined with high-precision three-dimensional modeling and dynamic boundary condition construction, it improves the model's realism and simulation accuracy, and provides a reliable foundation for subsequent analysis.

[0058] (2) The present invention uses the finite difference time-domain method to calculate the three-dimensional electric field, which can obtain continuous and accurate three-dimensional electric field distribution data. By identifying the peak value, the local maximum value is calculated in different scale spaces, and by comparing the distribution law of the maximum value at each scale, the false peak point is eliminated, thereby accurately determining the peak value of the global electric field intensity and its spatial coordinate position, realizing accurate simulation of the electric field distribution under lightning impact and effective location of high-risk areas.

[0059] (3) The present invention identifies high-risk areas based on the region growth algorithm. This process can accurately locate the area where the crossarm is most likely to be damaged by lightning. Combining material parameter retrieval and withstand voltage verification, the optimization algorithm is used to output material improvement schemes. This realizes the complete process from high-risk area identification to material parameter retrieval and then to material optimization. The final output material optimization parameters can effectively improve the insulation performance and lightning protection capability of the crossarm.

[0060] (4) This invention achieves dual optimization of crossarm cross section and material combination through electric field trend analysis, multi-condition stress simulation and risk quantification assessment, thereby improving its comprehensive stability and operational reliability under lightning impact. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the simulation analysis method for lightning protection of 10kV distribution network composite crossarm provided in the first embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the 10kV distribution network composite crossarm lightning protection simulation analysis system provided in the second embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Reference Figure 1 The first embodiment of the present invention provides a simulation analysis of lightning protection for a 10kV distribution network composite crossarm, including the following steps:

[0065] S11, acquire the shape data, material parameters and lightning impact waveform information of the crossarm;

[0066] S12, perform three-dimensional modeling based on the shape data, the material parameters and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions;

[0067] S13, calculate the three-dimensional electric field distribution based on the gridded three-dimensional model to obtain the peak electric field intensity and electric field spatial data under lightning impact;

[0068] S14, when the peak value of the electric field strength exceeds the preset breakdown electric field strength threshold, high-risk areas are identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, and material optimization parameters are output.

[0069] S15, based on the material optimization parameters, update the meshed three-dimensional model, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity variation trend;

[0070] S16. Based on the new electric field spatial data and the electric field intensity change trend, perform geometric configuration screening and output the optimal crossarm cross section form;

[0071] S17, verify the lightning protection stability based on the optimal crossarm cross section form and the new electric field space data, and output the final material combination and stability rating;

[0072] S18, integrate the new electric field spatial data, the electric field intensity change trend, the final material combination, and the stability rating, and output a crossarm lightning protection report.

[0073] In step S11, the shape data, material parameters, and lightning impact waveform information of the crossarm are obtained.

[0074] Specifically, the shape data comes from the three-dimensional geometric measurement results of the crossarm, including the dimensions, curvature, and connection methods of various parts of the crossarm, such as the geometric properties of the width, height, and chamfer radius of the crossarm section. Material parameters include the dielectric constant, conductivity, breakdown field strength, and mechanical strength parameters of the composite materials used in the crossarm; these parameters are obtained through material performance testing or technical data provided by the manufacturer. Lightning impulse waveform information comes from standard lightning impulse voltage waveforms or actual measured lightning current data, including wavefront time, wave tail time, peak voltage, and waveform harmonic components. This step reads the above data from measurement equipment, databases, or user input through a data interface, and performs format standardization and preliminary verification to ensure data integrity and consistency, providing accurate input for subsequent three-dimensional modeling and electric field simulation, laying the foundation for high-precision simulation analysis.

[0075] In step S12, a three-dimensional model is performed based on the shape data, the material parameters, and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0076] In one specific implementation, the step of performing three-dimensional modeling based on the shape data, the material parameters, and the lightning impact waveform information to obtain a meshed three-dimensional model including dynamic boundary conditions includes:

[0077] Based on the shape data and material parameters, outlier detection and removal are performed using the median absolute deviation, and a hash index algorithm is combined for efficient querying to obtain standard data.

[0078] Based on the standard data, a three-dimensional solid is reconstructed using non-uniform rational B-spline curves, and a gradient descent algorithm is used to smooth the geometric structure to obtain a three-dimensional model.

[0079] Based on the lightning impulse waveform information, the harmonic content is analyzed by fast Fourier transform. When the harmonic content is lower than the preset harmonic content threshold, the waveform parameters are analyzed by the finite difference time-domain method, and time-varying boundary conditions are generated by the total field / scattered field separation algorithm.

[0080] Based on the three-dimensional model and the time-varying boundary conditions, the mesh is generated using the leading edge method, and the mesh quality is optimized using the Laplace smoothing algorithm to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0081] Specifically, the input shape data (such as the width, height, and chamfer radius of the crossarm cross section) and material parameters (such as dielectric constant and conductivity) are preprocessed first. The median absolute deviation algorithm is used to calculate the median of all geometric data, including width and height, and to calculate the absolute deviation of each data point from the median. Then, the median of these absolute deviations is calculated. Finally, data points exceeding three times the median (e.g., crossarm lengths exceeding 10 meters) are identified as anomalies and removed. Simultaneously, a hash index algorithm is used, with material number or geometric coordinates as keys, to construct a hash table. Hash collisions are resolved using chaining, enabling efficient querying and matching of massive amounts of data (such as tens of thousands of material attribute records) in the material parameter database. This ensures that each geometric feature point in the shape data can be quickly associated with its corresponding dielectric constant and conductivity value, thus outputting standard data that is anomaly-free and consistently correlated.

[0082] Next, based on the standard data, a non-uniform rational B-spline curve algorithm is used to reconstruct the three-dimensional entity: by inputting the three-dimensional coordinate set of the crossarm feature points as control points, and assigning weight values ​​to each control point, and combining the node vector to define the curve segment connection method, a mathematical model that accurately describes the complex surface of the crossarm is constructed; on this basis, the gradient descent algorithm is used to smooth the surface, using the sum of squares of the surface curvature as the objective function, and by iteratively calculating and adjusting the coordinates of the control points to minimize the objective function value, thereby eliminating irregular protrusions or sharp edges on the surface and generating a smooth and physically accurate three-dimensional model.

[0083] The lightning impulse waveform information is processed as follows: the time-domain waveform signal is converted into a frequency-domain signal using the Fast Fourier Transform algorithm, and the ratio of each harmonic amplitude to the fundamental amplitude is calculated to obtain the harmonic content value. When this value is lower than the harmonic content threshold determined based on simulation accuracy requirements and a large number of historical lightning waveform statistics (for example, it can be set to 5%), the finite-difference time-domain method is used to discretize the waveform into a time step sequence. Maxwell's equations are solved at each time step to analyze the waveform parameters of the wavefront time value, wave tail time value, and peak voltage value.

[0084] Lightning impulse waveform information is processed using a total field / scattered field separation algorithm. The algorithm's implementation is as follows: First, an equivalent incident wave source is set in the boundary region of the computational domain. This source generates an incident electric field function based on the lightning impulse waveform parameters (including peak voltage, wavefront time, and wavetail time). The incident field is calculated using the analytical expression of the double exponential function of the standard lightning waveform. At each time step, the incident field distribution throughout the computational domain is directly calculated using this expression. The total field is obtained by numerically solving Maxwell's equations. The scattered field is obtained by subtracting the incident field from the total field. To effectively absorb the emitted scattered waves, an absorbing boundary condition based on the Perfectly Matched Layer (PML) technique is set at the outer boundary of the computational domain to suppress non-physical reflections, allowing the scattered field to penetrate the computational domain without reflection. Through this separation method, the total field formula is used inside the computational domain, and the scattered field formula is used in the boundary region, thereby generating a time-varying boundary condition function that accurately characterizes the propagation characteristics of lightning waves. Subsequently, a leading-edge meshing method was used for mesh generation: the triangular mesh of the 3D model surface obtained from the previous processing was used as the initial leading edge. Triangles on the leading edge were selected sequentially as base planes, and new nodes were generated by advancing along the normal direction of the base planes. The new nodes were connected to the three vertices of the base planes to form tetrahedral elements. After each generation of new elements, the leading edge was updated, the occupied faces were deleted, and the newly formed faces were added, until the entire computational domain was completely filled with tetrahedral elements.

[0085] Finally, the Laplacian smoothing algorithm is applied to optimize the mesh quality: traverse all internal mesh nodes, calculate the average value of the coordinates of the adjacent nodes of each node, use this average value as the new position, and iteratively adjust the node coordinates to make the geometry of each tetrahedral element as close as possible to a regular tetrahedron. The final output is a high-quality meshed 3D model that combines geometric details, material properties, and dynamic electromagnetic boundaries.

[0086] This model accurately characterizes the structural features and material distribution of the crossarm and couples the transient excitation of lightning strikes, providing a high-precision and physically realistic numerical simulation basis for subsequent electric field calculations.

[0087] In step S13, the three-dimensional electric field distribution is calculated based on the gridded three-dimensional model to obtain the peak electric field intensity and electric field spatial data under lightning impact.

[0088] In one specific implementation, the step of calculating the three-dimensional electric field distribution based on the gridded three-dimensional model to obtain the peak electric field intensity and spatial electric field data under lightning impact includes:

[0089] Based on the gridded three-dimensional model and the lightning impact waveform information, the three-dimensional electric field is numerically calculated using the finite-difference time-domain method, and the field strength distribution is analyzed using the bilinear interpolation algorithm to obtain the three-dimensional electric field distribution data.

[0090] Based on the electric field spatial data, the peak electric field intensity is identified using an automatic multi-scale peak search algorithm.

[0091] When the peak value of the electric field intensity exceeds the preset insulation tolerance threshold, an optimized early warning signal is generated and the calculation continues to be completed to obtain the electric field space data.

[0092] Specifically, the gridded 3D model containing dynamic boundary conditions and lightning impulse waveform information output in step S12 are used as input data. First, the 3D electric field is numerically calculated using the finite-difference time-domain method: the computational domain is discretized into cubic grid cells, and each cell node stores the electric field strength value and magnetic field strength value; based on the dielectric constant and conductivity values ​​defined in the gridded 3D model, as well as the peak voltage value, wavefront time value, and wave tail time value in the lightning impulse waveform information, Maxwell's curl equation is solved at each time step using the central difference scheme to update the electric field strength values ​​of each grid node in the entire computational domain; after the calculation is completed, the field strength values ​​of the discrete grid points are interpolated using the bilinear interpolation algorithm. Using the 3D spatial coordinates as input, the grid cell containing the point to be calculated is located, and a weighted average is calculated based on the electric field strength values ​​at the eight vertices of the cell to obtain continuously distributed 3D electric field distribution data, which contains the electric field strength vector at each location point in space.

[0093] Subsequently, based on the three-dimensional electric field distribution data, an automatic multi-scale peak finding algorithm is used to identify the peak electric field intensity. First, local maxima of the electric field intensity modulus are calculated within different scales (e.g., 1 mm, 5 mm, 10 mm neighborhoods). Then, by comparing the distribution patterns of these maxima at each scale, false peaks are eliminated, ultimately determining the global electric field intensity peak and its spatial coordinates. When the identified electric field intensity peak exceeds the insulation withstand threshold determined statistically based on historical test data of the composite material's breakdown field strength (e.g., 30 kV / mm, which is the upper limit of withstand capability determined based on the material's breakdown electric field strength threshold and considering a safety margin), an insulation risk is identified in the current design, and a warning signal is output. The subsequent process proceeds to the material optimization and re-verification stage (steps S14 and S15), ultimately yielding the electric field spatial data under the current simulation. This step achieves accurate simulation of the three-dimensional electric field distribution of the crossarm under lightning strikes and intelligent identification of high-risk points, providing crucial data support for subsequent material optimization.

[0094] In step S14, when the peak value of the electric field strength exceeds the preset breakdown electric field strength threshold, a high-risk area is identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, and material optimization parameters are output.

[0095] In one specific implementation, when the peak electric field strength exceeds a preset breakdown electric field strength threshold, a high-risk area is identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, outputting material optimization parameters, including:

[0096] Based on the electric field spatial data, high-risk areas are identified using a region growing algorithm, and the coordinates of the high-risk areas are obtained.

[0097] Based on the coordinates of the high-risk area, the dielectric constant and conductivity parameters of the corresponding locations are retrieved from the material parameter database using a hash index algorithm to obtain the material parameters of the high-risk area;

[0098] Based on the material parameters of the high-risk area and the lightning impact waveform information, the material pressure resistance performance is verified using a decision tree algorithm to obtain the material parameter verification results.

[0099] When the material parameter verification results show that the material performance does not meet the preset performance requirements, a material alternative is searched using a non-dominated sorting genetic algorithm to obtain the material optimization parameters.

[0100] Specifically, the process takes the three-dimensional electric field distribution data output in step S13 as input. First, it identifies high-risk areas through a region growing algorithm: grid points that exceed the breakdown electric field strength threshold (this threshold represents the minimum field strength at which the material undergoes dielectric breakdown, and is the theoretical basis for setting the insulation tolerance threshold; the insulation tolerance threshold is the maximum allowable operating field strength obtained after considering the safety margin) are used as seed points. The electric field strength values ​​of their adjacent grid points are checked. If the electric field strength value of an adjacent point is higher than 80% of the threshold, it is included in the region. The process is recursively expanded until no new points are added, and finally a set of high-risk area coordinates composed of a series of spatial coordinates is obtained.

[0101] Based on the coordinates of the high-risk area, the dielectric constant and conductivity parameters of the corresponding locations are retrieved from the material parameter database using a hash index algorithm: the three-dimensional index value of the spatial coordinates is used as the key, the hash address is calculated using a division hash function, and collisions are resolved using a linear probing method to quickly obtain the dielectric constant and conductivity values ​​corresponding to each coordinate point in the high-risk area, forming a set of material parameters for the high-risk area.

[0102] Based on material parameters and lightning impulse waveform information (including peak voltage, wavefront time, and wavetail time) in high-risk areas, a decision tree algorithm is used to verify the withstand voltage performance of materials. (The training data for this algorithm comes from a historical material test database. Its features include dielectric constant, conductivity, peak voltage, and wavefront time. Labels are defined using Boolean values ​​based on the withstand and breakdown conditions specified in international insulation standards. Hyperparameters such as tree depth and minimum number of samples per node are selected based on cross-validation results to maximize verification accuracy.) A classification tree model is constructed using dielectric constant and conductivity as feature attributes and waveform parameters as conditional attributes. The optimal splitting attribute is selected by calculating information gain, and the material is judged layer by layer to determine whether it meets the withstand voltage requirements. Finally, the material parameter verification result (satisfied or not satisfied) is output.

[0103] When material parameter verification results show that the material performance does not meet the performance requirements specified in industry standards, a material alternative is searched using a non-dominated sorting genetic algorithm. The algorithm initializes a population of alternative materials with dielectric constant, conductivity, and withstand voltage rating as optimization objectives. Offspring are generated through simulated binary crossover and polynomial mutation. Individual evaluation is performed using fast non-dominated sorting and crowding calculation. After multiple generations of evolution, the optimal material alternative is selected from the Pareto optimal solution set. The final output is the optimized material parameters, including the dielectric constant and conductivity values ​​of the new material. This step enables precise location of high-risk areas in the crossarm and optimization of material performance, providing parameter basis for improving the lightning protection performance of the crossarm.

[0104] In step S15, the meshed three-dimensional model is updated according to the material optimization parameters, and the three-dimensional electric field distribution is recalculated to obtain new electric field spatial data and electric field intensity variation trend.

[0105] In one specific implementation, updating the meshed three-dimensional model based on the material optimization parameters and recalculating the three-dimensional electric field distribution to obtain new electric field spatial data and electric field intensity variation trends includes:

[0106] Based on the material optimization parameters, the dielectric constant and conductivity parameters of the meshed 3D model are updated using a hash table mapping algorithm to obtain a new 3D model;

[0107] Based on the new three-dimensional model, spatial discretization is performed using the forward propulsion method, and boundary parameters are configured using the Dirichlet boundary condition setting method to obtain discretized spatial coordinates.

[0108] Based on the discretized spatial coordinates and the lightning impulse waveform information, the electric field distribution is calculated using the finite-difference time-domain method to obtain new electric field spatial data.

[0109] Based on the new electric field spatial data and the electric field spatial data, the transient field strength data is processed by the short-time Fourier transform algorithm to obtain the trend of electric field strength change.

[0110] Specifically, this process uses the material optimization parameters (including optimized dielectric constant and conductivity values) output in step S14 and the original meshed 3D model as input data. First, the model parameters are updated using a hash table mapping algorithm: using the mesh cell number as the key and the dielectric constant and conductivity values ​​from the material optimization parameters as the values, a hash function is constructed using the division remainder method. Hash collisions are resolved using the chaining method, updating the dielectric constant and conductivity values ​​of each mesh cell to the optimized values, resulting in a new 3D model with updated material properties.

[0111] Based on the new 3D model, spatial discretization is performed using the leading edge method: with the triangular mesh on the model surface as the initial leading edge, tetrahedral elements are generated layer by layer along the normal direction, and each new element inherits the dielectric constant and conductivity values ​​of the corresponding position; the Dirichlet boundary condition setting method is used to configure the boundary parameters, and a fixed potential value determined according to the peak voltage value of the lightning impulse waveform is applied to the boundary nodes of the computational domain, thereby obtaining a discretized spatial coordinate dataset containing node coordinates, element connection relationships and material parameters.

[0112] Based on the discretized spatial coordinates and lightning impulse waveform information (including wavefront time value, wave tail time value, and peak voltage value), the electric field distribution is calculated using the finite-difference time-domain method: the computational domain is discretized into a cubic grid, and at each time step, based on the updated dielectric constant and conductivity values, Maxwell's equations are solved using the central difference scheme to calculate the electric field intensity value of each grid node, thus obtaining new electric field spatial data, which contains the electric field intensity components at each point in space.

[0113] Based on the new electric field spatial data and the original electric field spatial data obtained in step S13, the transient field strength data is processed using the short-time Fourier transform algorithm. The Hanning window function is used to segment the electric field strength time series, and a Fourier transform is performed on each segment to calculate the energy distribution of each frequency band. By comparing the energy changes of the electric field strength in each frequency band before and after optimization, the characteristics of its spectral energy change over time are analyzed, yielding the trend characteristics of the electric field strength change over time, i.e., the electric field strength change trend. This step achieves accurate calculation and trend analysis of the electric field distribution of the crossarm after material optimization.

[0114] In step S16, based on the new electric field spatial data and the electric field intensity change trend, geometric configuration screening is performed to output the optimal crossarm cross section form.

[0115] In one specific implementation, the step of performing geometric configuration screening based on the new electric field spatial data and the electric field intensity variation trend to output the optimal crossarm cross-section shape includes:

[0116] The cross-section forms of the crossarms are obtained from a pre-established geometric shape database and classified using the K-means clustering algorithm to obtain the classified cross-section forms.

[0117] Based on the classified crossarm cross-section form, the new electric field spatial data, and the electric field intensity variation trend, stress field simulation calculation is performed by finite element analysis to obtain stress distribution data and multi-condition stability data for each cross-section form under different working conditions.

[0118] When the maximum stress value in the stress distribution data is lower than the preset allowable stress threshold of the material, the failure risk of each cross-section is quantitatively assessed by the entropy weight method to obtain the risk assessment result.

[0119] Based on the risk assessment results and the multi-condition stability data, the distribution pattern characteristics are extracted using principal component analysis to determine the optimal cross-section form.

[0120] Specifically, firstly, data on the cross-sectional forms of crossarms are obtained from a pre-established geometric shape database, including geometric parameters (such as cross-sectional width, height, and wall thickness) for various cross-sectional types, including rectangular, circular, and I-shaped crossarms. These cross-sectional forms are then classified using a K-means clustering algorithm: the K value is pre-defined within a range based on the geometric diversity of the cross-sections, and the clustering error trend corresponding to different K values ​​is evaluated using the elbow method. The point where the error decrease rate significantly slows down is selected as the optimal number of clusters. K initial cluster centers are randomly selected, and the Euclidean distance (based on the cross-sectional geometric parameters) from each cross-sectional form to the cluster center is calculated. The cross-sections are then assigned to the nearest cluster, and the cluster centers are recalculated. This process is iterated until the cluster centers no longer change, ultimately yielding a set of classified cross-sectional forms of crossarms.

[0121] It should be noted that after the geometric parameters of the crossarm section are clustered by K-Means, the electric field and stress distribution of the same category of sections under lightning impact show a high degree of consistency, which greatly simplifies the subsequent screening process.

[0122] Based on the cross-sectional forms of the classified crossarms, the new electric field spatial data, and the electric field intensity variation trend, stress field simulation calculations are performed using the finite element method: finite element models of each cross-sectional form are established, and mechanical and thermal loads derived from the electric field intensity variation trend are applied. The stress distribution data (including stress tensor values ​​at each node) and multi-condition stability data (including maximum deformation and safety factor values) for each cross-sectional form under different working conditions are obtained. When the maximum stress value in the stress distribution data is lower than the allowable stress threshold (e.g., 160 MPa) determined statistically from material fatigue test data, the failure risk of each cross-sectional form is quantitatively assessed using the entropy weight method: the information entropy value of each assessment index (such as maximum electric field intensity, maximum stress, and safety factor value) is calculated, the index weights are determined based on the entropy values, and the risk assessment result is obtained through weighted calculation.

[0123] Based on the risk assessment results and multi-condition stability data, principal component analysis was used to extract distribution patterns. The multi-dimensional evaluation index data were standardized, the covariance matrix was calculated, and eigenvalues ​​and eigenvectors were solved. Principal components with a cumulative contribution rate exceeding 85% were selected. In the new feature space formed by these principal components, the comprehensive score for each cross-section form was calculated, and the cross-section form with the highest comprehensive score was determined as the optimal cross-section form. This step achieved multi-objective optimization based on electric field characteristics and mechanical performance, ensuring that the selected cross-section form exhibits optimal comprehensive performance under lightning impact.

[0124] In step S17, lightning protection stability is verified based on the optimal crossarm cross section form and the new electric field space data, and the final material combination and stability rating are output.

[0125] In one specific implementation, the step of verifying lightning protection stability based on the optimal crossarm cross-section and the new electric field space data, and outputting the final material combination and stability rating, includes:

[0126] Based on the optimal cross-section shape, matching candidate material combinations are retrieved from the pre-stored material database using the K-means clustering algorithm to obtain material combination data;

[0127] Based on the material combination data and the new electric field spatial data, the electric field distribution and mechanical stress distribution are calculated by the finite element analysis method to obtain multi-physics field coupling data containing electric field strength and mechanical stress values.

[0128] When the electric field strength is lower than a preset electric field strength threshold and the mechanical stress value is lower than a preset mechanical stress threshold, the material's compressive strength and mechanical stability are verified using a decision tree algorithm to obtain the performance verification results.

[0129] Based on the performance verification results, the comprehensive stability index of the material combination data is quantified by the entropy weight method to obtain the stability rating result;

[0130] By fusing the stability rating results with multiphysics coupling data using principal component analysis, the optimal performance characteristics are extracted, and the final material combination and corresponding stability rating are determined.

[0131] Specifically, firstly, based on the geometric characteristic parameters (including cross-sectional width, height, and wall thickness) of the optimal cross-section, a matching candidate material combination is retrieved from a pre-stored material database using the K-means clustering algorithm: using the cross-sectional geometric parameters as feature vectors, the Euclidean distance with each material combination in the material database is calculated, and the top K material combinations with the smallest distances are taken as the candidate set, thus obtaining material combination data containing dielectric constant, conductivity, and tensile strength values.

[0132] Based on the material combination data and the new electric field space data, the electric field distribution and mechanical stress distribution are calculated by the finite element analysis method: a finite element model containing the optimal cross-sectional shape and candidate material combinations is established, electric field loads and mechanical loads derived from the new electric field space data are applied, and multi-physics coupling data containing the electric field intensity value and mechanical stress value of each node is obtained by solving.

[0133] When the electric field strength value is lower than the electric field strength threshold determined by the long-term endurance test of the insulating material (e.g., 20 kV / mm) and the mechanical stress value is lower than the mechanical stress threshold determined by the material strength test standard (e.g., 150 MPa), the material's pressure resistance performance and mechanical stability are verified by a decision tree algorithm: using the dielectric constant, conductivity, and tensile strength values ​​as feature attributes, and the electric field strength threshold and mechanical stress threshold as classification criteria, a binary decision tree is constructed. The optimal splitting attribute is selected by calculating the Gini index, and the material combination is judged layer by layer to determine whether it meets the performance requirements, thus obtaining the performance verification results.

[0134] Based on the performance verification results, the comprehensive stability index of the material combination data is quantified by the entropy weight method: the information entropy value of each performance index (including pressure resistance level and mechanical strength) is calculated, the weight of each index is determined according to the entropy value, and the stability rating result (expressed as a percentage) is obtained by weighted calculation.

[0135] This method integrates stability rating results with multiphysics coupling data using principal component analysis: Multidimensional index data are standardized, eigenvalues ​​and eigenvectors of the covariance matrix are calculated, and principal components with a cumulative contribution rate exceeding 90% are selected. The comprehensive performance score of each material combination is calculated in the new feature space formed by these principal components. Finally, the material combination with the highest comprehensive score is determined as the final material combination, and its corresponding stability rating is output. This step achieves optimized selection of material combinations based on multiphysics coupling analysis, ensuring that the crossarm has optimal lightning protection stability and mechanical reliability under lightning impact.

[0136] In step S18, the new electric field spatial data, the electric field intensity change trend, the final material combination, and the stability rating are integrated to output a crossarm lightning protection report.

[0137] Specifically, the new electric field spatial data generated in step S15 (including the electric field intensity components Ex, Ey, and Ez at each point in space), the electric field intensity variation trend obtained in step S15 (including the energy distribution characteristics of each frequency band over time), the final material combination determined in step S17 (including the dielectric constant, conductivity, and tensile strength values), and the stability rating output in step S17 (rating results expressed as a percentage) are used as input data.

[0138] A standard data structure is constructed using the XML Document Object Model, establishing a mapping relationship between the coordinates of each grid point in the new electric field spatial data and the corresponding electric field intensity value. The XPath query language is used to achieve the association retrieval of electric field distribution data and material parameters. A Word document template is created based on the Apache POI library, and the DOM4J parser is used to convert the electric field spatial data into a three-dimensional field intensity distribution table, which includes key indicators such as the maximum value, minimum value, and coordinates of high-risk areas.

[0139] The JFreeChart library was used to generate a curve showing the change of electric field intensity over time. The time series in milliseconds was plotted on the x-axis, and the field strength in kilovolts per millimeter was plotted on the y-axis to represent the field strength change throughout the entire lightning strike process. Characteristic values ​​at the wavefront and wave tail time points were labeled. The dielectric constant, conductivity, and tensile strength values ​​of the final material composition were then filled into the material parameter list. Simultaneously, the stability rating score was converted into a grade designation (e.g., 90-100 points for Grade A, 80-89 points for Grade B).

[0140] This report comprehensively presents the electric field characteristics, material properties, and stability evaluation of crossarms under lightning strikes, providing authoritative technical basis for lightning protection design.

[0141] Reference Figure 2 The second embodiment of the present invention provides a 10kV distribution network composite crossarm lightning protection simulation analysis system, comprising:

[0142] The data acquisition module is used to acquire the shape data, material parameters, and lightning impact waveform information of the crossarm;

[0143] The three-dimensional model building module is used to perform three-dimensional modeling based on the shape data, the material parameters and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions.

[0144] The three-dimensional electric field analysis module is used to calculate the three-dimensional electric field distribution based on the gridded three-dimensional model, and obtain the peak electric field intensity and electric field spatial data under lightning impact.

[0145] The material optimization module is used to identify high-risk areas when the peak electric field strength exceeds a preset breakdown electric field strength threshold, and to verify the material withstand voltage performance by combining the material parameters and the lightning impulse waveform information, and output material optimization parameters.

[0146] The electric field trend analysis module is used to update the meshed three-dimensional model according to the material optimization parameters, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity change trend.

[0147] The optimal crossarm analysis module is used to perform geometric configuration screening based on the new electric field spatial data and the electric field intensity variation trend, and output the optimal crossarm cross-section form;

[0148] The material combination analysis module is used to verify the lightning protection stability based on the optimal crossarm cross section form and the new electric field space data, and output the final material combination and stability rating.

[0149] The output module integrates the new electric field spatial data, the electric field intensity variation trend, the final material combination, and the stability rating to output a crossarm lightning protection report.

[0150] It should be noted that the 10kV distribution network composite crossarm lightning protection simulation analysis device provided in this embodiment of the invention is used to execute all the process steps of the 10kV distribution network composite crossarm lightning protection simulation analysis method in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0151] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a 10kV distribution network composite crossarm lightning protection simulation analysis program. When the processor executes the computer program, it implements the steps described in the various embodiments of the 10kV distribution network composite crossarm lightning protection simulation analysis method, for example... Figure 1The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the 10kV distribution network composite crossarm lightning protection simulation analysis module.

[0152] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0153] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0154] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0155] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0156] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0157] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0158] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A simulation analysis method for lightning protection of a 10kV distribution network composite crossarm, characterized in that, include: Acquire the shape data, material parameters, and lightning impact waveform information of the crossarm; Based on the shape data, material parameters, and lightning impact waveform information, a three-dimensional model is created to obtain a meshed three-dimensional model containing dynamic boundary conditions. Based on the gridded three-dimensional model, the three-dimensional electric field distribution is calculated to obtain the peak electric field intensity and electric field spatial data under lightning impact. When the peak electric field strength exceeds the preset breakdown electric field strength threshold, high-risk areas are identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information, and material optimization parameters are output. Based on the material optimization parameters, update the meshed three-dimensional model, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity variation trend; Based on the new electric field spatial data and the electric field intensity variation trend, geometric configuration screening is performed to output the optimal crossarm cross section form; Based on the optimal crossarm cross section form and the new electric field space data, the lightning protection stability is verified, and the final material combination and stability rating are output. By integrating the new electric field spatial data, the electric field intensity variation trend, the final material combination, and the stability rating, a crossarm lightning protection report is output.

2. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, The step of performing three-dimensional modeling based on the shape data, the material parameters, and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions includes: Based on the shape data and material parameters, outlier detection and removal are performed using the median absolute deviation, and a hash index algorithm is combined for efficient querying to obtain standard data. Based on the standard data, a three-dimensional solid is reconstructed using non-uniform rational B-spline curves, and a gradient descent algorithm is used to smooth the geometric structure to obtain a three-dimensional model. Based on the lightning impulse waveform information, the harmonic content is analyzed by fast Fourier transform. When the harmonic content is lower than the preset harmonic content threshold, the waveform parameters are analyzed by the finite difference time-domain method, and time-varying boundary conditions are generated by the total field / scattered field separation algorithm. Based on the three-dimensional model and the time-varying boundary conditions, the mesh is generated using the leading edge method, and the mesh quality is optimized using the Laplace smoothing algorithm to obtain a meshed three-dimensional model containing dynamic boundary conditions.

3. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, The calculation of the three-dimensional electric field distribution based on the gridded three-dimensional model, to obtain the peak electric field intensity and spatial data of the electric field under lightning impact, includes: Based on the gridded three-dimensional model and the lightning impact waveform information, the three-dimensional electric field is numerically calculated using the finite-difference time-domain method, and the field strength distribution is analyzed using the bilinear interpolation algorithm to obtain the three-dimensional electric field distribution data. Based on the electric field spatial data, the peak electric field intensity is identified using an automatic multi-scale peak search algorithm. When the peak value of the electric field intensity exceeds the preset insulation tolerance threshold, an optimized early warning signal is generated and the calculation continues to be completed to obtain the electric field space data.

4. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, When the peak electric field strength exceeds a preset breakdown electric field strength threshold, a high-risk area is identified, and the material withstand voltage performance is verified by combining the material parameters and the lightning impulse waveform information. Optimized material parameters are then output, including: Based on the electric field spatial data, high-risk areas are identified using a region growing algorithm, and the coordinates of the high-risk areas are obtained. Based on the coordinates of the high-risk area, the dielectric constant and conductivity parameters of the corresponding locations are retrieved from the material parameter database using a hash index algorithm to obtain the material parameters of the high-risk area; Based on the material parameters of the high-risk area and the lightning impact waveform information, the material pressure resistance performance is verified using a decision tree algorithm to obtain the material parameter verification results. When the material parameter verification results show that the material performance does not meet the preset performance requirements, a material alternative is searched using a non-dominated sorting genetic algorithm to obtain the material optimization parameters.

5. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, The step of updating the meshed 3D model based on the material optimization parameters and recalculating the 3D electric field distribution to obtain new electric field spatial data and electric field intensity variation trends includes: Based on the material optimization parameters, the dielectric constant and conductivity parameters of the meshed 3D model are updated using a hash table mapping algorithm to obtain a new 3D model; Based on the new three-dimensional model, spatial discretization is performed using the forward propulsion method, and boundary parameters are configured using the Dirichlet boundary condition setting method to obtain discretized spatial coordinates. Based on the discretized spatial coordinates and the lightning impulse waveform information, the electric field distribution is calculated using the finite-difference time-domain method to obtain new electric field spatial data. Based on the new electric field spatial data and the electric field spatial data, the transient field strength data is processed by the short-time Fourier transform algorithm to obtain the trend of electric field strength change.

6. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, The step of selecting the optimal crossarm cross-section based on the new electric field spatial data and the electric field intensity variation trend includes: The cross-section forms of the crossarms are obtained from a pre-established geometric shape database and classified using the K-means clustering algorithm to obtain the classified cross-section forms. Based on the classified crossarm cross-section form, the new electric field spatial data, and the electric field intensity variation trend, stress field simulation calculation is performed by finite element analysis to obtain stress distribution data and multi-condition stability data for each cross-section form under different working conditions. When the maximum stress value in the stress distribution data is lower than the preset allowable stress threshold of the material, the failure risk of each cross-section is quantitatively assessed by the entropy weight method to obtain the risk assessment result. Based on the risk assessment results and the multi-condition stability data, the distribution pattern characteristics are extracted using principal component analysis to determine the optimal cross-section form.

7. The simulation analysis method for lightning protection of 10kV distribution network composite crossarm according to claim 1, characterized in that, The lightning protection stability verification is performed based on the optimal crossarm cross-section and the new electric field space data, and the final material combination and stability rating are output, including: Based on the optimal cross-section shape, matching candidate material combinations are retrieved from the pre-stored material database using the K-means clustering algorithm to obtain material combination data; Based on the material combination data and the new electric field spatial data, the electric field distribution and mechanical stress distribution are calculated by the finite element analysis method to obtain multi-physics field coupling data containing electric field strength and mechanical stress values. When the electric field strength is lower than a preset electric field strength threshold and the mechanical stress value is lower than a preset mechanical stress threshold, the material's compressive strength and mechanical stability are verified using a decision tree algorithm to obtain the performance verification results. Based on the performance verification results, the comprehensive stability index of the material combination data is quantified by the entropy weight method to obtain the stability rating result; By fusing the stability rating results with multiphysics coupling data using principal component analysis, the optimal performance characteristics are extracted, and the final material combination and corresponding stability rating are determined.

8. A simulation analysis system for lightning protection of a 10kV distribution network composite crossarm, characterized in that, include: The data acquisition module is used to acquire the shape data, material parameters, and lightning impact waveform information of the crossarm; The three-dimensional model building module is used to perform three-dimensional modeling based on the shape data, the material parameters and the lightning impact waveform information to obtain a meshed three-dimensional model containing dynamic boundary conditions. The three-dimensional electric field analysis module is used to calculate the three-dimensional electric field distribution based on the gridded three-dimensional model, and obtain the peak electric field intensity and electric field spatial data under lightning impact. The material optimization module is used to identify high-risk areas when the peak electric field strength exceeds a preset breakdown electric field strength threshold, and to verify the material withstand voltage performance by combining the material parameters and the lightning impulse waveform information, and output material optimization parameters. The electric field trend analysis module is used to update the meshed three-dimensional model according to the material optimization parameters, recalculate the three-dimensional electric field distribution, and obtain new electric field spatial data and electric field intensity change trend. The optimal crossarm analysis module is used to perform geometric configuration screening based on the new electric field spatial data and the electric field intensity variation trend, and output the optimal crossarm cross-section form; The material combination analysis module is used to verify the lightning protection stability based on the optimal crossarm cross section form and the new electric field space data, and output the final material combination and stability rating. The output module integrates the new electric field spatial data, the electric field intensity variation trend, the final material combination, and the stability rating to output a crossarm lightning protection report.

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