Insulating pull rod lightweight design method based on multi-objective optimization algorithm

Abstract

The invention discloses an insulating pull rod lightweight design method based on a multi-objective optimization algorithm, and aims to solve the problems of heavy weight and heavy workload of a bearing tool in hot-line work of an ultra-high voltage transmission line. The method comprises the following steps: setting electrical and mechanical property parameters of the insulating pull rod; then, a three-dimensional model is built through SolidWorks, and finite element analysis is carried out through COMSOL; then, an optimized data set is obtained through joint simulation of MATLAB, COMSOL and SolidWorks; secondly, establishing a mathematical optimization model, solving by adopting a particle swarm-grey wolf optimization algorithm, and improving the algorithm through Sine-Tent-Cosine chaotic mapping population initialization and a crisscross strategy; and finally performing simulation verification on the optimization scheme. The device is applied to the lightweight design of large-load insulating force-bearing tools, effectively shortens the development period of the tools, saves the production cost, improves the safety coefficient of hot-line work, and remarkably reduces the workload of hot-line workers.

Description

Technical Field

[0001] The invention relates to the technical field of live working tools, and in particular to a lightweight design method for insulating pull rods based on a multi-objective optimization algorithm. Background Art

[0002] With the rapid development of the power industry, the construction and operation and maintenance of UHV transmission lines have become an important issue in the power sector. UHV transmission lines have the characteristics of high voltage level, large transmission capacity, and long transmission distance. Their safe and stable operation is crucial to the reliability and economy of the power system. However, live work on UHV transmission lines, especially the replacement of insulator strings, places extremely high demands on the performance of load-bearing tools.

[0003] Traditional load-bearing tools for live working, such as insulating pull rods, are often designed to meet basic electrical and mechanical properties, while ignoring the need for lightweight design. With the increase in voltage levels of UHV transmission lines and the increase in the weight of insulator strings, the length and weight of load-bearing tools for live working have also increased, which not only increases the difficulty and risk of live working, but also seriously affects the safety and work efficiency of live working personnel.

[0004] In the optimization research of tools for live working, trial and error optimization and finite element simulation optimization methods have been applied to a certain extent. Trial and error optimization finds the optimal solution through repeated experiments and modification of design parameters, but this method is time-consuming and labor-intensive, and the optimization effect is limited by the experience and intuition of designers. The finite element simulation optimization method simulates and analyzes the electrical and mechanical properties of the tools by establishing a three-dimensional model and a finite element analysis model of the tools, thereby optimizing the design parameters. However, with the increasing difficulty of live working on UHV transmission lines, traditional optimization methods have been unable to meet the needs of lightweight design of load-bearing tools for live working.

[0005] At present, the research on lightweight design of load-bearing tools for live working has gradually become a hot topic, but the use of multi-objective optimization algorithms for lightweight design is still a blank. Multi-objective optimization algorithms can comprehensively consider multiple design objectives, such as mass, electric field strength, stress and strain, and achieve lightweight design of tools by finding the optimal balance point between these objectives. Compared with traditional optimization methods, multi-objective optimization algorithms have higher optimization efficiency and better optimization effects, and can better meet the needs of lightweight design of load-bearing tools for live working.

[0006] Therefore, technical personnel are urgently needed to develop lightweight design schemes for live working load-bearing tools that comprehensively consider the insulation performance and mechanical properties, and use multi-objective optimization algorithms to optimize the design of tools to reduce the workload of live working personnel, improve work efficiency, and enhance the safety of live working. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for lightweight design of large-tonnage insulating pull rods based on a multi-objective optimization algorithm, so as to solve the technical problems existing in the field of existing live working tools, especially in the lightweight design of large-load insulating pull rods.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: a lightweight design method for insulating pull rods based on a multi-objective optimization algorithm, comprising the following steps: Step 1: Setting the electrical and mechanical properties of the insulation rod for UHV live working; Step 2: Analytical calculation of the constraints of the three-dimensional model. Use the three-dimensional modeling software to build a three-dimensional model of the insulating pull rod, and use the finite element analysis software to set the constraints of the finite element analysis; Step 3: Data set acquisition, determine the optimization parameter range through orthogonal experimental design, and use MATLAB, COMSOL and SolidWorks joint simulation to record data; Step 4: Mathematical optimization model analysis, select the solution method that transforms multi-objective optimization into single-objective optimization, set the objective function, design variables and constraints; Step 5: Optimization method selection, using particle swarm-grey wolf optimization algorithm, and improving it through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategy; Step 6: Model optimization and simulation verification. Use the optimal solution obtained by the optimization method to construct the optimal design solution, and verify the optimization solution through the finite element simulation analysis model.

[0009] In the preferred solution, the electrical and mechanical properties of the insulating pull rod for ultra-high voltage live working in Step 1 are set, including determining the maximum load capacity of the insulating pull rod, the connection method of each component and the lightweight target, and the connection method of each component includes wedge connection, crimping connection and internal cone thread connection.

[0010] In a preferred solution, the three-dimensional modeling software in Step 2 is SolidWorks, and the finite element analysis software is COMSOL.

[0011] In the preferred solution, the parameter range is optimized in Step 3 by selecting the components and parameter ranges of the insulating pull rod that need to be optimized based on actual design experience and finite element simulation test results.

[0012] In the preferred scheme, the orthogonal experimental design in Step 3 is designed by SPSS software to design an orthogonal experimental scheme, specifically, different levels of multiple factors are combined based on the orthogonal table to ensure that each factor is independent of each other, and the optimal factor combination is determined. MATLAB is used to call SolidWorks and COMSOL, wherein SolidWorks is used to perform three-dimensional modeling of the insulating pull rod, and COMSOL is used to perform electric field and mechanical simulation. MATLAB is used to adjust the parameters of key components of the three-dimensional model in SolidWorks, and COMSOL is called to add materials to the three-dimensional model, impose constraints, and divide the grid, so as to realize parametric modeling and fully automatic modeling iterative analysis, and obtain the required optimized data set.

[0013] In the preferred solution, in the orthogonalized test design in Step 3, each factor is the size of each key part, and the number of levels is four, which are the maximum electric field intensity, the maximum stress, the maximum strain and the total mass of the component. The orthogonal table of the test is determined according to the number of factor levels.

[0014] In the preferred solution, the objective function of the mathematical optimization model in Step 4 is to minimize the mass of the insulating pull rod, the design variables are the specific component parameters of the insulating pull rod optimization design, the constraints include the design variable range constraints, the maximum electric field strength, the maximum stress and the maximum strain constraints, and the mathematical expression of the mathematical optimization model is: (1) In the formula, is the weight of the insulating rod, is the material density of each part of the insulating rod, is the maximum allowable stress, is the maximum strain, is the maximum electric field strength, is the cross-sectional area of ​​the component, is the component length, is the maximum allowable strain of the insulating rod.

[0015] In the preferred solution, the optimization method in Step 5 uses a particle swarm-grey wolf optimization algorithm, and further improves the particle swarm-grey wolf algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies. The Sine-Tent-Cosine chaotic mapping population initialization includes a composite chaotic system, which is used to overcome the shortcomings of limited chaotic behavior and non-uniform data distribution of the generated chaotic sequence. The mathematical function expression of the Sine-Tent-Cosine chaotic mapping population initialization is: (2) In the formula, is the initial solution, is the generated solution, is a control parameter within (0, 1).

[0016] In the preferred solution, the vertical and horizontal crossover strategy in Step 5 enhances the global search capability through the horizontal crossover search factor and the vertical crossover search factor to maintain population diversity. All individuals perform arithmetic crossover in two dimensions, i.e., vertical crossover, and its mathematical model is: (3) In the formula, is a random number in (0, 1), For the The parent individual of the dimension, For the The parent individual of the dimension; The mathematical model of the arithmetic crossover between two different individuals in all dimensions, i.e. horizontal crossover, is: (4) In the formula, and are two random numbers in (-1, 1), and are two random numbers in (0, 1), and For the The parent individual of the dimension and .

[0017] In the preferred solution, if the optimization solution in Step 6 cannot meet the constraints and target requirements such as electrical performance, mechanical properties and lightweight of the ultra-high voltage load-bearing tools, the mathematical optimization model or optimization method is adjusted until the optimized design solution meets the actual design requirements.

[0018] The lightweight design method of insulating pull rod based on multi-objective optimization algorithm provided by the present invention has the following beneficial effects: 1. The present invention solves the technical problems existing in the field of existing live working tools, especially in the lightweight design of large-load insulating pull rods, and meets the demand for lightweight design of load-bearing tools; 2. Compared with the traditional trial and error optimization and finite element simulation optimization methods, the present invention adopts a multi-objective optimization algorithm, which is more effective and stable; 3. The present invention adopts MATLAB, SolidWorks, COMSOL and other multi-software joint simulation to realize the automated operation of parametric modeling and system simulation analysis, thereby improving simulation efficiency and accuracy; 4. The present invention improves the particle swarm-grey wolf optimization algorithm, and improves the calculation efficiency and accuracy of the algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies, and the mass of the insulating pull rod end is significantly reduced; 5. The optimized insulating pull rod of the present invention realizes a lightweight design while ensuring the electrical and mechanical performance requirements. The lightweight design reduces the burden on live workers and improves the safety index and work efficiency of the operation; 6. The method of the present invention adopts automated simulation and optimization algorithms, which shortens the research and development cycle and saves production costs; 7. The present invention is based on a multi-objective optimization algorithm, fully considering the mutual influence between mechanical and electrical performance and lightweight, and the design scheme is more in line with actual operation requirements, and is more universal, flexible and excellent. ; 8. The PSO-GWO (Particle Swarm Optimization-Grey Wolf Optimization) hybrid algorithm combining the particle swarm and the grey wolf algorithm in the present invention can better process high-dimensional data, improve the global search capability, reduce the probability of falling into the local optimal trap, and has good robustness. At the same time, the hybrid algorithm is further improved by Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategy, so that the algorithm calculation efficiency and accuracy are higher; 9. The present invention comprehensively considers the mechanical properties and electrical properties of the insulating pull rod, achieves its weight reduction through an optimization algorithm, and ensures its reliability and safety in live working on UHV transmission lines. This method is not only suitable for the lightweight design of insulating pull rods, but can also be extended to the lightweight design of other large-load insulating load-bearing tools, providing new ideas and methods for the technological progress and performance improvement of live working tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 The design process of the present invention; Figure 2 This is a schematic diagram of the key parts of the insulation pull rod end optimization of the present invention; Figure 3 The electric field intensity distribution cloud diagram of the upper end of the optimization scheme of the present invention is a simulation verification result; Figure 4 The upper end strain distribution cloud diagram of the simulation verification result of the optimization scheme of the present invention; Figure 5 The stress distribution cloud diagram of the upper end of the optimization scheme of the present invention is a simulation verification result; Figure 6The electric field intensity distribution cloud diagram of the lower end of the optimization scheme of the present invention is a simulation verification result; Figure 7 The strain distribution cloud diagram of the lower end of the optimization scheme of the present invention is a simulation verification result; Figure 8 This is a cloud diagram of the stress distribution at the lower end of the simulation verification result of the optimization scheme of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments: Example 1 like Figures 1 to 8 As shown, the lightweight design method of insulating pull rod based on multi-objective optimization algorithm includes the following steps: Step 1: Setting the electrical and mechanical properties of the insulation rod for UHV live working; Step 2: Analytical calculation of the constraints of the three-dimensional model. Use the three-dimensional modeling software to build a three-dimensional model of the insulating pull rod, and use the finite element analysis software to set the constraints of the finite element analysis; Step 3: Data set acquisition, determine the optimization parameter range through orthogonal experimental design, and use MATLAB, COMSOL and SolidWorks joint simulation to record data; Step 4: Mathematical optimization model analysis, select the solution method that transforms multi-objective optimization into single-objective optimization, set the objective function, design variables and constraints; Step 5: Optimization method selection, using particle swarm-grey wolf optimization algorithm, and improving it through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategy; Step 6: Model optimization and simulation verification. Use the optimal solution obtained by the optimization method to construct the optimal design solution, and verify the optimization solution through the finite element simulation analysis model.

[0021] In this embodiment, the electrical and mechanical properties of the insulating rod for ultra-high voltage live working in Step 1 are set, including determining the maximum load capacity of the insulating rod, the connection method of each component and the lightweight target. The connection method of each component includes wedge connection, crimping connection and internal cone thread connection.

[0022] Furthermore, the three-dimensional modeling software in Step 2 is SolidWorks, and the finite element analysis software is COMSOL.

[0023] Furthermore, the parameter range optimization in Step 3 is to select the components and parameter ranges of the insulating pull rod that need to be optimized based on actual design experience and finite element simulation test results.

[0024] Furthermore, the orthogonal experimental design in Step 3 is designed with SPSS software to design an orthogonal experimental plan, specifically, different levels of multiple factors are combined based on the orthogonal table to ensure that each factor is independent of each other, determine the optimal factor combination, and use MATLAB to call SolidWorks and COMSOL, wherein SolidWorks is used to perform three-dimensional modeling of the insulating pull rod, and COMSOL is used to perform electric field and mechanical simulation. MATLAB is used to adjust the parameters of key components of the three-dimensional model in SolidWorks, and COMSOL is called to add materials to the three-dimensional model, impose constraints, and divide the grid, so as to realize parametric modeling and fully automatic modeling iterative analysis, and obtain the required optimized data set.

[0025] Furthermore, in the orthogonalized test design in Step 3, each factor is the size of each key part, and the number of levels is four, which are the maximum electric field intensity, the maximum stress, the maximum strain and the total mass of the component. The orthogonal table of the experiment is determined according to the number of factor levels.

[0026] Furthermore, the objective function of the mathematical optimization model in Step 4 is to minimize the mass of the insulating pull rod, the design variables are the specific component parameters of the insulation pull rod optimization design, and the constraints include the design variable range constraints, the maximum electric field strength, the maximum stress and the maximum strain constraints. The mathematical expression of the mathematical optimization model is: (1) In the formula, is the weight of the insulating rod, is the material density of each part of the insulating rod, is the maximum allowable stress, is the maximum strain, is the maximum electric field strength, is the cross-sectional area of ​​the component, is the component length, is the maximum allowable strain of the insulating rod.

[0027] Furthermore, the optimization method in Step 5 uses a particle swarm-grey wolf optimization algorithm, and further improves the particle swarm-grey wolf algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies. The Sine-Tent-Cosine chaotic mapping population initialization includes a composite chaotic system, which is used to overcome the shortcomings of limited chaotic behavior and non-uniform data distribution of the generated chaotic sequence. The mathematical function expression of the Sine-Tent-Cosine chaotic mapping population initialization is: (2) In the formula, is the initial solution, is the generated solution, is a control parameter within (0, 1).

[0028] Furthermore, the vertical and horizontal crossover strategy in Step 5 enhances the global search capability through the horizontal crossover search factor and the vertical crossover search factor to maintain population diversity. All individuals perform arithmetic crossover in two dimensions, i.e., vertical crossover, and its mathematical model is: (3) In the formula, is a random number in (0, 1), For the The parent individual of the dimension, For the The parent individual of the dimension; The mathematical model of the arithmetic crossover between two different individuals in all dimensions, i.e. horizontal crossover, is: (4) In the formula, and are two random numbers in (-1, 1), and are two random numbers in (0, 1), and For the The parent individual of the dimension and .

[0029] Furthermore, if the optimization scheme in Step 6 cannot meet the constraints and target requirements such as electrical performance, mechanical properties and lightweight of the UHV load-bearing tools, the mathematical optimization model or optimization method is adjusted until the optimized design scheme meets the actual design requirements.

[0030] Example 2 In another preferred embodiment, based on the above embodiment 1, Figures 1 to 8 As shown, this embodiment describes in detail the technical solution of the insulating pull rod lightweight design method based on the multi-objective optimization algorithm.

[0031] Step 1: Setting the electrical and mechanical properties of the insulating rods for UHV live working, including the maximum load capacity of the insulating rods, the connection method of the various components of the insulating rods, and the setting of the lightweight target of the insulating rods.

[0032] Furthermore, the connection method and lightweight target of the insulating pull rod components can be determined based on the manual of live working tools and the manufacturer's research results. The connection methods of the insulating pull rod components include wedge connection, crimping connection, and internal cone thread connection. The internal cone thread connection method can be selected. The lightweight target is determined as the quality of the insulating pull rod end.

[0033] Furthermore, the maximum load capacity of the insulating rod is determined to be 100 kN according to the load requirements of the insulating rod in the operation mode of replacing the insulator string with double hangers: (4) (5) In the formula, To bear the gravity for the insulator string; is the conductor coefficient; is the nominal cross-sectional area of ​​the conductor; is the vertical span; The load required for the tool.

[0034] Step 2: Analytical calculation of the constraints of the three-dimensional model, specifically including the construction of the finite element analysis model of the insulating rod and the setting of the finite element analysis constraints; that is, the insulation rod simulation model is built through the three-dimensional modeling software, and the electric field strength simulation analysis of the insulating rod in the ultra-high voltage 1000kV ten-splitting conductor transmission line environment and the mechanical simulation analysis under the 150kN tensile force at both ends are performed through the finite element analysis software.

[0035] Furthermore, SolidWorks was used to establish an insulating rod model, and COMSOL finite element solution software was used to realize simulation analysis of the electrical and mechanical properties of the insulating rod. The electric field intensity, stress, and strain obtained by simulation were analyzed.

[0036] Step 3: Data set acquisition, including optimization parameter range setting, orthogonal experimental design, and data recording by joint simulation of MATLAB, COMSOL and SolidWorks.

[0037] Furthermore, the optimization parameter range is set according to the tool design manual and the manufacturer's research results to obtain the key optimization parts and parameter ranges of the insulation pull rod end. For detailed optimization parts, refer to the attached Figure 2 .

[0038] Furthermore, in the orthogonal experimental design, each factor is the size of each key part, and the number of levels is four, which are the maximum electric field intensity, the maximum stress, the maximum strain and the total mass of the component. The four-factor four-level orthogonal experimental design is obtained by SPSS software.

[0039] Furthermore, the MATLAB is jointly simulated with COMSOL and SolidWorks, and MATLAB is used to call SolidWorks and COMSOL, wherein SolidWorks is used to perform three-dimensional modeling of the insulating pull rod, and COMSOL is used to perform electric field and mechanical simulation. MATLAB is used to adjust the parameters of key components of the three-dimensional model in SolidWorks, and COMSOL is called to add materials, impose constraints, and divide the grid to the three-dimensional model, thereby realizing parametric modeling and fully automatic modeling iterative analysis.

[0040] Step 4: The mathematical optimization model analysis specifically includes the selection of multi-objective optimization solution methods, and the setting of objective functions, design variables, and constraints.

[0041] Furthermore, the multi-objective optimization solution method selects to convert the multi-objective optimization problem into a single-objective optimization problem, that is, to convert multiple optimization objectives into a final optimization objective, and the others are used as objective constraints; in this implementation case, the minimum mass of the insulating pull rod end is used as the final optimization objective, and the electric field strength, strain, and stress magnitude are used as objective constraints.

[0042] Furthermore, the objective function is the minimum mass of the insulating pull rod end; the design variables are the key parts of the end optimization described in step three; and the constraints specifically include: the parameter range of the design variables and the target constraints of the electric field strength, strain, and stress magnitude.

[0043] Step 5: The optimization method is selected. This implementation case uses the particle swarm-grey wolf optimization algorithm for optimization. Compared with the neural network optimization algorithm, it has faster convergence speed, better iterative optimization effect, and is not easy to fall into the local optimal trap. The optimized insulation pull rod end size is shown in Table 1.

[0044] Table 1 Final results of insulation rod end size optimization

[0045] Step 6: The model optimization solution and simulation verification specifically refers to the optimal design solution obtained by the optimal solution obtained by the optimization method described in step 5, and then calling the finite element simulation analysis model built in step 2 to verify the optimization solution; in this embodiment, the obtained electric field intensity, stress magnitude and strain amount meet the design requirements, and the simulation verification results refer to the attached Figure 3 .

[0046] Furthermore, if the constraints and target conditions such as the electrical performance, mechanical properties and lightweight of the UHV load-bearing tools cannot be met, it is necessary to adjust one or more of the mathematical optimization model described in step 4 and the optimization method selection described in step 5 until the optimized design scheme can meet the actual design requirements.

[0047] Example 3 In another preferred embodiment, based on the above embodiments 1 and 2, as Figures 1 to 8 As shown, this embodiment is a lightweight design of the insulating pull rod end according to the method described in the patent of the present invention.

[0048] First, the electrical and mechanical properties of the insulating rod for UHV live working are set (step 1); this includes the maximum load capacity of the insulating rod, the connection method of the various components of the insulating rod, and the setting of the lightweight target of the insulating rod; in this embodiment, the maximum load capacity of the insulating rod is determined according to the load requirements of the insulating rod in the double-pole replacement insulator string operation mode, and the rated load is set to 100kN; the connection methods of the insulating rod components include wedge connection, crimping connection and internal cone thread connection, and the internal cone thread connection method is preferred in this embodiment; the lightweight target is specifically determined as the weight reduction of the insulating rod end.

[0049] Next, the constraint conditions of the three-dimensional model are analyzed and calculated (step 2). The three-dimensional modeling software SolidWorks is used to build a three-dimensional model of the insulating rod, and the finite element solution software COMSOL is used to simulate and analyze the electric field strength of the insulating rod in the ultra-high voltage 1000kV ten-splitting conductor transmission line environment, as well as the mechanical properties under a tensile force of 150kN at both ends. This step ensures that the subsequent optimization process has an accurate model basis.

[0050] Then, the data set is acquired (step 3); this includes setting the optimization parameter range, orthogonal experimental design, and joint simulation and data recording of MATLAB, COMSOL, and SolidWorks; according to the tool design manual and the manufacturer's survey results, the key parts and parameter ranges for the optimization of the insulating pull rod end are determined. Orthogonal experimental design is implemented using SPSS software, and each factor is the size of each key part, with 4 levels, namely the maximum electric field intensity, maximum stress, maximum strain, and total mass of the component; the joint simulation of MATLAB, COMSOL, and SolidWorks uses MATLAB to call SolidWorks for three-dimensional modeling, and COMSOL for electric field and mechanical simulation, to achieve parametric modeling and fully automatic modeling iterative analysis, and obtain the required optimization data set.

[0051] Next, a mathematical optimization model analysis is performed (step 4). In this embodiment, the multi-objective optimization solution method selects a method of converting a multi-objective optimization problem into a single-objective optimization problem, that is, the minimum mass of the insulating pull rod end is taken as the final optimization target, and the electric field strength, strain amount, and stress size are taken as target constraints; the objective function is set to the minimum mass of the insulating pull rod end, the design variables are the key part parameters of the end optimization determined in step 3, and the constraints include the parameter range of the design variables and the electric field strength, strain amount, and stress size as target constraints.

[0052] Afterwards, an optimization method is selected (step 5); this embodiment adopts the particle swarm-grey wolf optimization algorithm for optimization; compared with the neural network optimization algorithm, the algorithm has a faster convergence speed, better iterative optimization effect, and is not easy to fall into the local optimal trap; at the same time, the algorithm is further improved through the Sine-Tent-Cosine chaotic mapping population initialization and the vertical and horizontal cross strategy to improve the algorithm's computational efficiency and accuracy.

[0053] Finally, the model is optimized and solved and simulated (step 6). The optimal design solution is obtained by the optimal solution obtained by the optimization method described in step 5, and the finite element simulation analysis model built in step 2 is called to verify the optimization solution. In this embodiment, the obtained electric field intensity, stress magnitude and strain amount all meet the design requirements. The simulation verification results are as follows: Figure 3 As shown; the dimensions of the optimized insulating pull rod end are shown in Table 1. The mass is reduced from 1.0638kg before optimization to 0.8465kg after optimization, achieving a significant lightweight effect.

[0054] Example 4 In another preferred embodiment, based on the above embodiments 1 and 2, as Figures 1 to 8 As shown, in order to demonstrate the diversity and practicality of the present invention, this embodiment considers another lightweight design of the insulating pull rod component (such as the core rod part).

[0055] In setting the electrical and mechanical properties of the insulating pull rod for UHV live working (step 1), the maximum load capacity, connection method and lightweight target of the core rod are also determined according to the working requirements and standard regulations; the connection method of the core rod may include crimping connection or internal cone thread connection, etc., which can be selected according to the specific situation.

[0056] In the analytical calculation of the three-dimensional model constraint conditions (step 2), a three-dimensional model is built for the core rod part, and simulation analysis of the electric field and mechanical properties is performed.

[0057] In data set acquisition (step 3), the optimization parameter range is determined according to the mandrel design manual and the manufacturer's research results, an orthogonal experiment is designed, and the data set is acquired through joint simulation of MATLAB, COMSOL, and SolidWorks.

[0058] In the mathematical optimization model analysis (step 4), the method of converting the multi-objective optimization problem into a single-objective optimization problem is also adopted. The objective function is set to minimize the core rod mass, the design variables are the parameters of the key parts of the core rod optimization, and the constraints include the parameter range of the design variables as well as the electric field strength, strain, stress magnitude, etc.

[0059] In the optimization method selection (step 5), different optimization algorithms can be tried, such as the non-dominated sorting multi-objective genetic algorithm, to compare the effects of different algorithms in the core rod lightweight design.

[0060] In the model optimization solution and simulation verification (step 6), the optimal design solution obtained by the optimization method is simulated and verified to ensure that the optimized core rod meets the electrical performance, mechanical performance and lightweight requirements.

[0061] Through the demonstration of the above two embodiments, it can be seen that the lightweight design method of the insulating pull rod based on the multi-objective optimization algorithm described in the present invention can minimize the weight, shorten the research and development cycle, improve the operation safety index, and reduce the burden on operators while ensuring the electrical and mechanical performance requirements of the insulating pull rod.

[0062] In summary, the lightweight design method for insulating pull rods based on a multi-objective optimization algorithm provided by the present invention can minimize the weight, shorten the research and development cycle, improve the operation safety index, and reduce the burden on operators while ensuring the electrical and mechanical performance requirements of the insulating pull rods.

[0063] In the preferred scheme, the electrical and mechanical properties of the insulating pull rod for ultra-high voltage live working in Step 1 are set, including determining the maximum load capacity of the insulating pull rod, the connection method of each component and the lightweight target. The connection method of each component includes wedge connection, crimping connection and internal cone thread connection. The above settings are aimed at improving the safety and efficiency of the operation, ensuring that the insulating pull rod can withstand extreme working conditions without failure in a high voltage environment, and at the same time, by optimizing the connection design, reducing the weight, facilitating operation and improving the flexibility of operation.

[0064] In the preferred solution, the 3D modeling software in Step 2 is SolidWorks, and the finite element analysis software is COMSOL; the above settings can ensure the accuracy and efficiency of the design process. SolidWorks is used to build a 3D model of the product to intuitively display the design details; while COMSOL conducts in-depth finite element analysis to predict and optimize product performance. The two work together to accelerate the product development cycle.

[0065] In the preferred solution, the parameter range optimization in Step 3 is to select the components and parameter ranges of the insulating pull rod that need to be optimized based on actual design experience and finite element simulation test results; the above settings ensure that the parameter adjustment is in line with engineering reality and can effectively verify its improvement effect through simulation. Next, advanced algorithms will be used to iteratively optimize the selected parameters in order to achieve the best balance between insulation performance and structural strength.

[0066] In the preferred scheme, the orthogonal experimental design in Step 3 is designed by SPSS software to design an orthogonal experimental scheme, specifically, different levels of multiple factors are combined based on the orthogonal table to ensure that each factor is independent of each other, and the optimal factor combination is determined. MATLAB is used to call SolidWorks and COMSOL, wherein SolidWorks is used to perform three-dimensional modeling of the insulating pull rod, and COMSOL is used to perform electric field and mechanical simulation. MATLAB is used to adjust the parameters of key components of the three-dimensional model in SolidWorks, and COMSOL is called to add materials to the three-dimensional model, impose constraints, and divide the grid to achieve parametric modeling and fully automatic modeling iterative analysis to obtain the required optimized data set; the above settings can efficiently explore the performance of the insulating pull rod under different conditions, reduce the number of experiments, and speed up the optimization process. Finally, combined with the optimized data set, the performance indicators of the insulating pull rod are comprehensively evaluated to provide a scientific basis for product design and optimization.

[0067] In the preferred solution, the orthogonal test design in Step 3, each factor is the size of each key part, the number of levels is four, which are the maximum electric field intensity, the maximum stress, the maximum strain and the total mass of the component. The orthogonal table of the experiment is determined according to the number of factor levels. The above settings are intended to comprehensively evaluate the impact of each key dimension on the performance indicators. Through orthogonal analysis, the optimal size combination can be efficiently identified, thereby optimizing product design and improving overall performance. At the same time, this solution provides a solid foundation for subsequent multi-objective optimization.

[0068] In the preferred solution, the objective function of the mathematical optimization model in Step 4 is to minimize the mass of the insulating pull rod, the design variables are the specific component parameters of the optimized design of the insulating pull rod, and the constraints include the design variable range constraints, the maximum electric field strength, the maximum stress and the maximum strain constraints; the above settings ensure that the insulating pull rod can achieve the best quality while meeting the safety and reliability requirements. By accurately controlling the parameters of each component, the extreme working conditions that may occur in the design process are effectively avoided, providing reliable guarantee for practical applications.

[0069] In the preferred scheme, the optimization method in Step 5 selects the particle swarm-grey wolf optimization algorithm, and further improves the particle swarm-grey wolf algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies. The Sine-Tent-Cosine chaotic mapping population initialization includes a composite chaotic system, which is used to overcome the shortcomings of limited chaotic behavior and non-uniform data distribution of the generated chaotic sequence; the above settings not only enhance the global search capability of the algorithm, but also effectively avoid the algorithm from falling into local optimality through the vertical and horizontal cross strategies, improve the stability and convergence speed of the algorithm, and make the optimization process more efficient and accurate.

[0070] In the preferred solution, the vertical and horizontal cross-search strategy in Step 5 enhances the global search capability through horizontal cross-search factors and vertical cross-search factors to maintain population diversity; the above settings ensure that the algorithm can both delve into local details and jump out of local optimality when exploring the solution space, effectively avoiding premature convergence; at the same time, the intensity of the search factor is dynamically adjusted so that the algorithm can maintain efficient search at different stages.

[0071] In the preferred solution, if the optimization solution in Step 6 cannot meet the constraints and target requirements such as electrical performance, mechanical properties and lightweight of the UHV load-bearing tools, the mathematical optimization model or optimization method is adjusted until the optimized design solution meets the actual design requirements; the above settings need to be combined with simulation verification and experimental testing to ensure that various performance parameters meet the standards; at the same time, a feedback mechanism is established for continuous optimization and iteration to cope with possible future technical challenges and standard updates, and to ensure the leadership and reliability of UHV load-bearing tools.

[0072] In summary, the method for lightweight design of large-tonnage insulating rods based on a multi-objective optimization algorithm proposed in the present invention overcomes the technical limitations of the traditional trial and error method and the existing single finite element simulation optimization method, solves the technical problems existing in the field of existing live working tools, especially in the lightweight design of large-load insulating rods, and meets the needs of lightweight design of load-bearing tools; the present invention applies a multi-objective optimization algorithm to the lightweight design of insulating rods for the first time, and both the design ideas and methods are innovative; the method adopts the joint simulation of multiple software such as MATLAB, COMSOL and SolidWorks, and realizes parametric modeling and fully automatic modeling iterative analysis, which is novel in the field of insulating rod design; in the selection and improvement of the optimization algorithm, the document proposes the Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies, and these technologies are also applied for the first time in the optimization algorithm for insulating rod design; the method not only focuses on the weight reduction of the insulating rod, but also comprehensively considers the optimization of electrical properties and mechanical properties, and achieves the goal of meeting performance requirements through a multi-objective optimization algorithm. Under the premise of maximizing the goal of weight reduction; from electrical and mechanical performance setting, three-dimensional model constraint analysis and calculation, data set acquisition, mathematical optimization model analysis, optimization method selection to model optimization solution and simulation verification, this document describes a complete and systematic optimization process, which is creative in the field of insulating rod design; the present invention improves the optimization algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategy, improves the convergence speed and global search ability of the algorithm, and reduces the probability of falling into the local optimal trap, which reflects creativity at the algorithm level; in summary, this scheme applies multi-objective optimization algorithm and joint simulation technology to the lightweight design of insulating rods for the first time, and proposes innovative chaotic mapping population initialization and vertical and horizontal cross strategy, and improves and upgrades the comprehensive performance optimization method, the systematic optimization process and the optimization algorithm; the present invention has brought a revolutionary breakthrough in the field of insulating rod design, significantly improved the design efficiency and product performance, set a new benchmark for technological innovation in related fields, and has broad application prospects and market value.

Claims

1. A lightweight design method for insulating pull rods based on a multi-objective optimization algorithm, characterized in that: The method comprises the following steps: Step 1: Setting the electrical and mechanical properties of the insulation rod for UHV live working; Step 2: Analytical calculation of the constraints of the three-dimensional model. Use the three-dimensional modeling software to build a three-dimensional model of the insulating pull rod, and use the finite element analysis software to set the constraints of the finite element analysis; Step 3: Data set acquisition, determine the optimization parameter range through orthogonal experimental design, and use MATLAB, COMSOL and SolidWorks joint simulation to record data; Step 4: Mathematical optimization model analysis, select the solution method that transforms multi-objective optimization into single-objective optimization, set the objective function, design variables and constraints; Step 5: Optimization method selection, using particle swarm-grey wolf optimization algorithm, and improving it through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategy; Step 6: Model optimization and simulation verification. Use the optimal solution obtained by the optimization method to construct the optimal design solution, and verify the optimization solution through the finite element simulation analysis model.

2. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The electrical and mechanical properties of the insulating rod for ultra-high voltage live working in Step 1 are set, including determining the maximum load capacity of the insulating rod, the connection method of each component and the lightweight target. The connection method of each component includes wedge connection, crimping connection and internal cone thread connection.

3. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The three-dimensional modeling software in Step 2 is SolidWorks, and the finite element analysis software is COMSOL.

4. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The parameter range optimization in Step 3 is to select the components and parameter ranges of the insulating pull rod that need to be optimized based on actual design experience and finite element simulation test results.

5. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The orthogonal experimental design in Step 3 is designed by using SPSS software to design an orthogonal experimental plan, specifically, different levels of multiple factors are combined based on the orthogonal table to ensure that each factor is independent of each other, and the optimal factor combination is determined. MATLAB is used to call SolidWorks and COMSOL, wherein SolidWorks is used to perform three-dimensional modeling of the insulating pull rod, and COMSOL is used to perform electric field and mechanical simulation. MATLAB is used to adjust the parameters of key components of the three-dimensional model in SolidWorks, and COMSOL is called to add materials to the three-dimensional model, impose constraints, and divide the grid, so as to realize parametric modeling and fully automatic modeling iterative analysis, and obtain the required optimized data set.

6. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 6 is characterized in that: In the orthogonal test design in Step 3, each factor is the size of each key part, and the number of levels is four, which are the maximum electric field intensity, the maximum stress, the maximum strain and the total mass of the component. The orthogonal table of the test is determined according to the number of factor levels.

7. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The objective function of the mathematical optimization model in Step 4 is to minimize the mass of the insulating pull rod, the design variables are the specific component parameters of the insulating pull rod optimization design, and the constraints include the design variable range constraints, the maximum electric field strength, the maximum stress and the maximum strain constraints. The mathematical expression of the mathematical optimization model is: （1）； In the formula, is the weight of the insulating rod, is the material density of each part of the insulating rod, is the maximum allowable stress, is the maximum strain, is the maximum electric field strength, is the cross-sectional area of ​​the component, is the length of the component, is the maximum allowable strain of the insulating rod.

8. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The optimization method in Step 5 uses the particle swarm-grey wolf optimization algorithm, and further improves the particle swarm-grey wolf algorithm through Sine-Tent-Cosine chaotic mapping population initialization and vertical and horizontal cross strategies. The Sine-Tent-Cosine chaotic mapping population initialization includes a composite chaotic system, which is used to overcome the shortcomings of limited chaotic behavior and non-uniform data distribution of the generated chaotic sequence. The mathematical function expression of the Sine-Tent-Cosine chaotic mapping population initialization is: （2）； In the formula, is the initial solution, is the generated solution, is a control parameter within (0, 1).

9. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1 is characterized in that: The vertical and horizontal crossover strategy in Step 5 enhances the global search capability through the horizontal crossover search factor and the vertical crossover search factor to maintain population diversity. All individuals perform arithmetic crossover in two dimensions, i.e. vertical crossover, and its mathematical model is: （3）； In the formula, is a random number in (0, 1), For the The parent individual of the dimension, For the The parent individual of the dimension; The mathematical model of the arithmetic crossover between two different individuals in all dimensions, i.e. horizontal crossover, is: （4）； In the formula, and are two random numbers in (-1, 1), and are two random numbers in (0, 1), and For the The parent individual of the dimension and .

10. The lightweight design method for insulating pull rods based on a multi-objective optimization algorithm according to claim 1, characterized in that: If the optimization scheme in Step 6 cannot meet the constraints and target conditions such as electrical performance, mechanical properties and lightweight of the UHV load-bearing tools, the mathematical optimization model or optimization method is adjusted until the optimized design scheme meets the actual design requirements.

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