Structural Optimization Method and System for a Composite Dry-Type Wall Bushings

Abstract

The present invention relates to an optimization method and system for the structure of a composite dry-type wall bushing, belonging to the technical field of structural optimization. The method first determines the constraint conditions for each parameter of the structure of the composite dry-type wall bushing, and constructs a multi-objective function with the minimization of the weight of the structure and the minimization of the electric field strength at a preset position as the objectives. Then, based on the constraint conditions, with the optimization of the multi-objective function as the goal, an optimization algorithm is used to optimize each parameter of the structure. The present invention converts the problem of structural parameter design into an optimization problem with multiple objectives and multiple constraints for solution in order to obtain a wall bushing structure that can consider the minimum weight and the optimal electric field at key parts, and uses an optimization algorithm for solution, which can improve the calculation speed and be closer to the overall optimal solution.

Description

Technical Field

[0001] The present invention relates to the technical field of structural optimization, and particularly to a structural optimization method and system for a composite dry-type wall bushing. Background Art

[0002] With the development of the power industry, as a new generation of high-voltage power transmission and transformation products, composite dry-type wall bushings are gradually used to replace porcelain wall bushings, and are mainly applied to electrical insulation and support of high-voltage conductors, incoming and outgoing lines of vacuum circuit breakers, etc. The structural optimization design of composite dry-type wall bushings mainly focuses on using simulation means. However, using simulation means can only optimize local key parts and cannot comprehensively consider the influence of multiple factors on multiple mutually exclusive objectives. Summary of the Invention

[0003] In view of this, the present invention provides a structural optimization method and system for a composite dry-type wall bushing to comprehensively consider the influence of multiple factors on multiple mutually exclusive objectives and improve the effect of overall parameter optimization.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A structural optimization method for a composite dry-type wall bushing, the method comprising the following steps:

[0006] Determine the constraint conditions for each parameter of the structure of the composite dry-type wall bushing; the parameters include: the radius R of the copper conductor rod Cu 、the length L of the copper conductor rod Cu 、the thickness d of the epoxy fiberglass rod EP 、the length L of the epoxy fiberglass rod EP 、the number of umbrella skirt groups n;

[0007] Construct a multi-objective function with the goal of minimizing the weight of the structure and minimizing the electric field strength at a preset position;

[0008] Based on the constraint conditions and with the goal of optimizing the multi-objective function, use an optimization algorithm to optimize each parameter of the structure to obtain a parameter optimization result.

[0009] Optionally, the constraint conditions are:

[0010]

[0011] Wherein, R Cu,min and R Cu,max are respectively the minimum value and the maximum value of the radius R of the copper conductor rod Cu , L Cu,min and L Cu,max are respectively the minimum value and the maximum value of the length L of the copper conductor rod Cu , d EP,min and dEP,max are the minimum and maximum values of the thickness d of the epoxy fiberglass rod, L EP and L EP,min and L EP,max are the minimum and maximum values of the length L of the epoxy fiberglass rod, n EP and n min and n max are the minimum and maximum values of the number of umbrella skirt groups n respectively.

[0012] Optionally, the multi-objective function is:

[0013]

[0014] where f1 is the first objective function aiming at minimizing the weight of the structure, f2 is the second objective function aiming at minimizing the field strength at a preset position, W is the weight of the wall bushing, E Cu is the field strength at the preset position;

[0015] The calculation formula of W is:

[0016]

[0017] In the formula, p Cu , p EP , m SR , W Al are the density of the copper conductor rod, the density of the epoxy fiberglass rod, the mass of the umbrella skirt group and the mass of the flange respectively;

[0018] E Cu The solution equation of is:

[0019] In the equation, ε is the dielectric constant at the preset position; is the electric potential at the preset position; ρ is the space charge density at the preset position; σ is the interface charge density at the preset position; J is the current density vector at the preset position, J n and J s respectively represent the normal current density and the tangential current density at the preset position; S e is the charge generation rate, and t represents the time variable.

[0020] Optionally, based on the above constraints, aiming at the optimization of the multi-objective function, an optimization algorithm is used to optimize the parameters of the structure to obtain the parameter optimization result, which specifically includes:

[0021] Generate an initial parent population that satisfies the above constraints;

[0022] Select, cross and mutate the parent population to generate a child population that satisfies the above constraints;

[0023] Merge the offspring population and the parent population to obtain a merged population;

[0024] Perform fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population;

[0025] Calculate the crowding degree of each individual in the merged population according to the multi-objective function;

[0026] Select superior individuals from the merged population according to the non-dominated rank and the crowding degree to form the parent population for the next iteration, return to the step "Select, crossover, and mutate the parent population to generate an offspring population" for the next iteration, until the preset number of iterations, and output the individual with the lowest non-dominated rank in the merged population as the parameter optimization result.

[0027] Optionally, the performing fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population specifically includes:

[0028] Determine the first parameter and the second parameter of each individual in the merged population according to the multi-objective function; wherein, the first parameter of the i-th individual is the number of other individuals in the merged population that dominate the i-th individual, and the second parameter of the i-th individual is the set of other individuals dominated by the i-th individual in the merged population; when all the objective function values of the i-th individual are better than the corresponding objective values of the j-th individual, the domination relationship between the i-th individual and the j-th individual is: the i-th individual dominates the j-th individual, otherwise the i-th individual cannot dominate the j-th individual;

[0029] Initialize the value of m to 1;

[0030] Store all individuals in the merged population with the first parameter equal to 0 into the set F m and set the non-dominated rank of each individual in the set F m to m;

[0031] Subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set F m ;

[0032] Let the value of m increase by 1, store the individuals with the first parameter equal to 0 in the union into the set F m and set the non-dominated rank of each individual in the set F m to m, and return to the step "Subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set F m " until the union is empty.

[0033] A structure optimization system for a composite dry-type wall bushing, the system comprising:

[0034] A constraint condition determination module for determining the constraint conditions of the parameters of the structure of the composite dry-type wall bushing; the parameters include: the radius R of the copper conductor bar Cu , the length L of the copper conductor bar Cu , the thickness d of the epoxy fiberglass rod EP , the length L of the epoxy fiberglass rod EP , and the number of umbrella skirt groups n;

[0035] A multi-objective function construction module for constructing a multi-objective function with the minimization of the weight of the structure and the minimization of the electric field strength at a preset position as the objectives;

[0036] A parameter optimization module for optimizing the parameters of the structure based on the constraint conditions with the optimization of the multi-objective function as the objective, and obtaining a parameter optimization result by using an optimization algorithm.

[0037] Optionally, the constraint conditions are:

[0038]

[0039] Wherein, R Cu,min and R Cu,max are respectively the minimum value and the maximum value of the radius R of the copper conductor bar Cu , L Cu,min and L Cu,max are respectively the minimum value and the maximum value of the length L of the copper conductor bar Cu , d EP,min and d EP,max are respectively the minimum value and the maximum value of the thickness d of the epoxy fiberglass rod EP , L EP,min and L EP,max are respectively the minimum value and the maximum value of the length L of the epoxy fiberglass rod EP , n min and n max are respectively the minimum value and the maximum value of the number of umbrella skirt groups n.

[0040] Optionally, the multi-objective function is:

[0041]

[0042] Wherein, f1 is the first objective function with the minimization of the weight of the structure as the objective, f2 is the second objective function with the minimization of the electric field strength at a preset position as the objective, W is the weight of the structure, and E Cu is the electric field strength at the preset position;

[0043] The calculation formula of W is:

[0044]

[0045] Wherein, p Cu 、p EP 、m SR 、W Al are the density of the copper conductor bar, the density of the epoxy fiberglass rod, the mass of the umbrella skirt group, and the mass of the flange respectively;

[0046] The solution equation for E Cu is as follows:

[0047] In the equation, ε is the dielectric constant at the preset position; is the electric potential at the preset position; ρ is the space charge density at the preset position; σ is the interface charge density at the preset position; J is the current density vector at the preset position, and J n and J s respectively represent the normal current density and the tangential current density at the preset position; S e is the charge generation rate, and t represents the time variable.

[0048] Optionally, the parameter optimization module specifically includes:

[0049] An initialization sub-module for generating an initial parent population that satisfies the constraint conditions;

[0050] A child population generation module for performing selection, crossover, and mutation on the parent population to generate a child population that satisfies the constraint conditions;

[0051] A population merging module for merging the child population and the parent population to obtain a merged population;

[0052] A fast non-dominated sorting sub-module for performing fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population;

[0053] A crowding degree calculation sub-module for calculating the crowding degree of each individual in the merged population according to the multi-objective function;

[0054] An individual selection sub-module for selecting better individuals from the merged population according to the non-dominated rank and the crowding degree to form the parent population for the next iteration, returning to the step of "performing selection, crossover, and mutation on the parent population to generate a child population", performing the next iteration, until the preset number of iterations, and outputting the individual with the lowest non-dominated rank in the merged population as the parameter optimization result.

[0055] Optionally, the fast non-dominated sorting sub-module specifically includes:

[0056] An individual parameter determination unit for determining a first parameter and a second parameter of each individual in the merged population according to the multi-objective function; wherein, the first parameter of the i-th individual is the number of other individuals in the merged population that dominate the i-th individual, and the second parameter of the i-th individual is the set of other individuals dominated by the i-th individual in the merged population; when all the objective function values of the i-th individual are better than the corresponding objective values of the j-th individual, the domination relationship between the i-th individual and the j-th individual is: the i-th individual dominates the j-th individual, otherwise the i-th individual cannot dominate the j-th individual;

[0057] An initialization unit for initializing the value of m to 1;

[0058] A first non-dominated rank determination unit for storing all individuals in the merged population whose first parameter is equal to 0 into set F m and setting the non-dominated rank of each individual in set F m to m;

[0059] A first parameter update unit for subtracting 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m ;

[0060] A second non-dominated rank determination unit for increasing the value of m by 1, storing the individuals in the union whose first parameter is equal to 0 into set F m and setting the non-dominated rank of each individual in set F m to m, and returning to the step "subtracting 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m " until the union is empty.

[0061] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0062] The present invention discloses a structural optimization method and system for a composite dry-type wall bushing. The method first determines the constraint conditions of the parameters of the structure of the composite dry-type wall bushing, and constructs a multi-objective function with the minimization of the weight of the structure and the minimization of the electric field strength at a preset position as the objectives; then, based on the constraint conditions, with the optimization of the multi-objective function as the objective, an optimization algorithm is used to optimize the parameters of the structure. In order to obtain a wall bushing structure that can consider the minimum weight and the optimal electric field at the key parts, the present invention transforms the structural parameter design problem into a multi-objective and multi-constraint optimization problem for solution, and uses an optimization algorithm for solution, which can improve the calculation speed and be closer to the overall optimal solution. Description of the Drawings

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0064] Figure 1 The flowchart of a structural optimization method for a composite dry-type wall bushing provided in Embodiment 1 of the present invention;

[0065] Figure 2 The schematic diagram of a structural optimization method for a composite dry-type wall bushing provided in Embodiment 1 of the present invention;

[0066] Figure 3 The cross-sectional structure diagram of the composite dry-type wall bushing provided in Embodiment 1 of the present invention. Detailed implementation manners

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0068] The object of the present invention is to provide a structural optimization method and system for a composite dry-type wall bushing to comprehensively consider the influence of multiple factors on multiple mutually exclusive objectives and improve the effect of overall parameter optimization.

[0069] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific implementation manners.

[0070] Embodiment 1

[0071] As Figure 1 and 2 shown, Embodiment 1 of the present invention provides a structural optimization method for a composite dry-type wall bushing. As Figure 3 shown, the composite dry-type wall bushing includes a copper conductor rod, an inner insulation (epoxy fiberglass rod), an outer insulation (umbrella skirt made of high-temperature vulcanized silicone rubber, that is, the silicone rubber umbrella skirt in Figure 3 , and an umbrella skirt group is composed of an adjacent large umbrella skirt and a small umbrella skirt), and a flange (exemplarily an aluminum flange), and has advantages such as uniform internal electric field distribution, pollution resistance, water repellency, and aging resistance.

[0072] The method includes the following steps:

[0073] Step 101, determine the constraint conditions for each parameter of the structure of the composite dry-type through-wall bushing; the parameters include: the radius R of the copper conductor rod Cu and the length L of the copper conductor rod Cu the thickness d of the epoxy fiberglass rod EP and the length L of the epoxy fiberglass rod EP the number n of umbrella skirt groups.

[0074] The constraint conditions are:

[0075]

[0076] Among them, R Cu,min and R Cu,max are respectively the minimum value and the maximum value of the radius R of the copper conductor rod Cu , L Cu,min and L Cu,max are respectively the minimum value and the maximum value of the length L of the copper conductor rod Cu , d EP,min and d EP,max are respectively the minimum value and the maximum value of the thickness d of the epoxy fiberglass rod EP , L EP,min and L EP,max are respectively the minimum value and the maximum value of the length L of the epoxy fiberglass rod EP , n min and n max are respectively the minimum value and the maximum value of the number n of umbrella skirt groups.

[0077] Step 102, construct a multi-objective function with the goal of minimizing the weight of the structure and minimizing the field strength at a preset position. It is found in the simulation that the field strength at both ends of the copper conductor rod is the most concentrated, so the end of the copper conductor rod is set as the preset position.

[0078] The multi-objective function is:

[0079]

[0080] Among them, f1 is the first objective function with the goal of minimizing the weight of the structure, f2 is the second objective function with the goal of minimizing the field strength at the preset position, W is the weight of the structure, and E Cu is the field strength at the preset position;

[0081] The calculation formula of W is:

[0082]

[0083] In the formula, p Cu , p EP , m SR , W Al are respectively the density of the copper conductor rod, the density of the epoxy fiberglass rod, the mass of the umbrella skirt group and the mass of the flange;

[0084] E Cu It can be obtained by the Gauss formula and two current continuity equations using the finite element method:

[0085]

[0086] In the equation, ε is the permittivity at the preset position, F / m; is the electric potential at the preset position, V; ρ is the space charge density at the preset position, C / m 3 ; σ is the interfacial charge density at the preset position, C / m 2 ; J is the current density vector at the preset position, A / m 2 , J n and J s respectively represent the normal current density and tangential current density at the preset position, A / m 2 ; S e is the charge generation rate, (m 3 s) -1 ; t represents the time variable.

[0087] Step 103: Based on the constraint conditions, with the optimization of the multi-objective function as the goal, use an optimization algorithm to optimize each parameter of the structure to obtain the parameter optimization result.

[0088] The step 103 of optimizing each parameter of the structure based on the constraint conditions with the optimization of the multi-objective function as the goal to obtain the parameter optimization result specifically includes:

[0089] Generate an initial parental population that meets the constraint conditions, that is, set the population generation number G = 0, and randomly generate an initial parental population P with a population size of N G .

[0090] Select, cross, and mutate the parental population to generate an offspring population that meets the constraint conditions, that is, generate an offspring population Q with a population size of N from P G through three genetic operators of selection, crossover, and mutation G .

[0091] Merge the offspring population and the parental population to obtain a merged population, that is, merge the parental population P G and the offspring population Q G to form a merged population R with a population size of 2N G .

[0092] Perform fast non - dominated sorting on the individuals in the combined population according to the multi - objective function to determine the non - dominated rank of each individual in the combined population. The performing fast non - dominated sorting on the individuals in the combined population according to the multi - objective function to determine the non - dominated rank of each individual in the combined population specifically includes: determining the first parameter and the second parameter of each individual in the combined population according to the multi - objective function; where the first parameter of the \(i\) - th individual is the number of other individuals in the combined population that dominate the \(i\) - th individual, and the second parameter of the \(i\) - th individual is the set of other individuals in the combined population that are dominated by the \(i\) - th individual; when all the objective function values of the \(i\) - th individual are better than the corresponding objective values of the \(j\) - th individual, the domination relationship between the \(i\) - th individual and the \(j\) - th individual is: the \(i\) - th individual dominates the \(j\) - th individual, otherwise the \(i\) - th individual cannot dominate the \(j\) - th individual; initialize the value of \(m\) to 1; store all the individuals in the combined population whose first parameter is equal to 0 into the set \(F\). m and set the non - dominated rank of each individual in the set \(F\) to \(m\); subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set \(F\). Let the value of \(m\) increase by 1, store the individuals in the union whose first parameter is equal to 0 into the set \(F\). m and set the non - dominated rank of each individual in the set \(F\) to \(m\), return to the step "subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set \(F\)" until the union is empty. m Calculate the crowding degree of each individual in the combined population according to the multi - objective function. The formula for calculating the crowding degree is as follows: m where \(P\) represents the crowding degree of the \(i\) - th individual, and \(f\) and \(f\) respectively represent the \(k\) - th objective function values in the multi - objective functions of the \((i - 1)\) - th individual and the \((i + 1)\) - th individual. m Select relatively better individuals from the combined population according to the non - dominated rank and the crowding degree to form the parental population for the next iteration, that is, increase the population generation \(G\) by 1, stratify the individuals in the combined population \(R\) according to the non - dominated rank, and at the same time sort the individuals in each non - dominated layer according to the crowding degree; select the top \(N\) individuals in the sorting to form the parental population \(P\) for the next iteration. m

[0093]

[0094]

[0095] i k,(i-1) k,(i+1)

[0096] G G

[0097] ​​​​​​​​​Return to the step of "selecting, crossing, and mutating the parental population to generate an offspring population", and perform the next iteration until the preset number of iterations. Output the individual with the lowest non-dominated rank in the combined population as the parameter optimization result. Determine whether the iteration ends: If the population generation number G is less than the maximum iteration number G max , return to the step of "selecting, crossing, and mutating the parental population to generate an offspring population" and perform the next iteration; if G is greater than or equal to G max , then end the iteration.

[0098] Embodiment 2

[0099] Embodiment 2 of the present invention provides a structural optimization system for a composite dry-type wall bushing. The system includes:

[0100] A constraint condition determination module for determining the constraint conditions of the parameters of the structure of the composite dry-type wall bushing; the parameters include: the radius R of the copper conductor rod Cu , the length L of the copper conductor rod Cu , the thickness d of the epoxy fiberglass rod EP , the length L of the epoxy fiberglass rod EP , and the number of umbrella skirt groups n.

[0101] The constraint conditions are:

[0102]

[0103] Among them, R Cu,min and R Cu,max are respectively the minimum and maximum values of the radius R of the copper conductor rod Cu , L Cu,min and L Cu,max are respectively the minimum and maximum values of the length L of the copper conductor rod Cu , d EP,min and d EP,max are respectively the minimum and maximum values of the thickness d of the epoxy fiberglass rod EP , L EP,min and L EP,max are respectively the minimum and maximum values of the length L of the epoxy fiberglass rod EP , n min and n max are respectively the minimum and maximum values of the number of umbrella skirt groups n.

[0104] A multi-objective function construction module for constructing a multi-objective function with the goal of minimizing the weight of the structure and minimizing the field strength at a preset position.

[0105] The multi-objective function is:

[0106]

[0107] Among them, f1 is the first objective function aiming at minimizing the weight of the structure, f2 is the second objective function aiming at minimizing the field strength at a preset position, W is the weight of the structure, and E Cu is the field strength at the preset position;

[0108] The calculation formula of W is:

[0109]

[0110] In the formula, p Cu 、p EP 、m SR 、W Al are the density of copper conductor bar, the density of epoxy fiberglass rod, the mass of umbrella skirt group and the mass of flange respectively;

[0111] E Cu The solution equation of is:

[0112] In the equation, ε is the permittivity at the preset position; is the electric potential at the preset position; ρ is the space charge density at the preset position; σ is the interface charge density at the preset position; J is the current density vector at the preset position, J n and J s respectively represent the normal current density and the tangential current density at the preset position; S e is the charge generation rate, and t represents the time variable.

[0113] The parameter optimization module is used to optimize each parameter of the structure based on the constraint conditions with the optimization of the multi-objective function as the goal, and obtain the parameter optimization result by using an optimization algorithm.

[0114] The parameter optimization module specifically includes: an initialization sub-module for generating an initial parental population that meets the constraint conditions; a filial population generation module for performing selection, crossover and mutation on the parental population to generate a filial population that meets the constraint conditions; a population merging module for merging the filial population and the parental population to obtain a merged population; a fast non-dominated sorting sub-module for performing fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population; a crowding degree calculation sub-module for calculating the crowding degree of each individual in the merged population according to the multi-objective function; an individual selection sub-module for selecting superior individuals from the merged population according to the non-dominated rank and the crowding degree to form the parental population for the next iteration, returning to the step "perform selection, crossover and mutation on the parental population to generate a filial population", and performing the next iteration until the preset number of iterations, and outputting the individual with the lowest non-dominated rank in the merged population as the parameter optimization result.

[0115] Among them, the fast non-dominated sorting sub-module specifically includes: an individual parameter determination unit, configured to determine a first parameter and a second parameter of each individual in the merged population according to the multi-objective function; wherein, the first parameter of the i-th individual is the number of other individuals in the merged population that dominate the i-th individual, and the second parameter of the i-th individual is a set of other individuals dominated by the i-th individual in the merged population; when all the objective function values of the i-th individual are better than the corresponding objective values of the j-th individual, the domination relationship between the i-th individual and the j-th individual is: the i-th individual dominates the j-th individual, otherwise the i-th individual cannot dominate the j-th individual; an initialization unit, configured to initialize the value of m to 1; a first non-dominated rank determination unit, configured to store all individuals in the merged population whose first parameter is equal to 0 into the set F m and set the non-dominated rank of each individual in the set F m to m; a first parameter update unit, configured to subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set F m ; a second non-dominated rank determination unit, configured to increment the value of m by 1, store the individuals in the union whose first parameter is equal to 0 into the set F m and set the non-dominated rank of each individual in the set F m to m, and return to the step "subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in the set F m " until the union is empty.

[0116] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0117] The present invention aims to obtain a wall bushing structure that can consider the minimum weight and the optimal electric field at key parts, and transforms the structure design problem into a multi-objective and multi-constraint optimization problem for solution. By using an optimization algorithm and introducing concepts such as fast non-dominated sorting, crowding degree, and elite retention strategy, the calculation speed can be improved and the overall optimal solution can be approached more closely.

[0118] Each embodiment in this specification is described in a progressive manner. The key points of each embodiment are the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0119] In this text, specific examples are used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for helping to understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A structural optimization method for a composite dry-type wall bushing, characterized in that The method includes the following steps: Determine the constraint conditions for each parameter of the structure of the composite dry-type wall bushing; the parameters include: Copper guide rod radius R Cu , Copper guide rod length L Cu 、Epoxy glass fiber rod thickness d EP , Epoxy fiberglass rod length L EP , the number of shed groups n; Construct a multi-objective function with the goal of minimizing the weight of the structure and minimizing the field strength at a preset position; Based on the constraint conditions, with the optimization of the multi-objective function as the goal, use an optimization algorithm to optimize each parameter of the structure to obtain the parameter optimization result; The multi-objective function is: Among them, f1 is the first objective function aiming at minimizing the weight of the structure, f2 is the second objective function aiming at minimizing the field strength at a preset position, W is the weight of the structure, and E Cu is the field strength at the preset position; The calculation formula of W is: where p Cu , p EP , m SR , W Al are the density of the copper conductor bar, the density of the epoxy glass fiber rod, the mass of the petticoat group, and the mass of the flange respectively; E Cu The solution equation for In the equation, ε is the dielectric constant at the preset position; is the electric potential at the preset position; ρ is the space charge density at the preset position; σ is the interfacial charge density at the preset position; J is the current density vector at the preset position, J n and J s respectively represent the normal current density and the tangential current density at the preset position; S e is the charge generation rate, and t represents the time variable; Based on the constraint conditions, with the optimization of the multi-objective function as the goal, use an optimization algorithm to optimize each parameter of the structure to obtain the parameter optimization result, specifically including: Generate an initial parent population that satisfies the constraint conditions; Perform selection, crossover, and mutation on the parent population to generate a child population that satisfies the constraint conditions; Merge the child population and the parent population to obtain a merged population; Perform fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population; Calculate the crowding degree of each individual in the merged population according to the multi-objective function; According to the non-dominated rank and the crowding degree, select the better individuals from the merged population to form the parent population for the next iteration, return to the step "Perform selection, crossover, and mutation on the parent population to generate a child population", and perform the next iteration until the preset number of iterations, and output the individual with the lowest non-dominated rank in the merged population as the parameter optimization result; The step of performing fast non-dominated sorting on the individuals in the merged population according to the multi-objective function to determine the non-dominated rank of each individual in the merged population specifically includes: Determine the first parameter and the second parameter of each individual in the merged population according to the multi-objective function; where, the first parameter of the i-th individual is the number of other individuals in the merged population that dominate the i-th individual, and the second parameter of the i-th individual is the set of other individuals dominated by the i-th individual in the merged population; when all the objective function values of the i-th individual are better than the corresponding objective values of the j-th individual, the dominance relationship between the i-th individual and the j-th individual is: the i-th individual dominates the j-th individual, otherwise the i-th individual cannot dominate the j-th individual; Initialize the value of m to 1; Store all individuals in the merged population whose first parameter is equal to 0 into the set F m and set the non-dominated rank of each individual in the set F m to m; Subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m ; Increment the value of m by 1, store the individuals in the union where the first parameter is equal to 0 into set F m and set the non-dominated rank of each individual in set F m to m, and return to the step "subtract 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m until the union is empty; The calculation formula of the crowding degree is as follows: Among them, P i represents the crowding degree of the i-th individual, f k,(i-1) and f k,(i+1) respectively represent the k-th objective function values in the multi-objective functions of the (i - 1)-th individual and the (i + 1)-th individual.

2. The structural optimization method of the composite dry-type wall bushing according to claim 1, characterized in that, The constraint conditions are: wherein, R Cu,min and R Cu,max are respectively the minimum value and the maximum value of the radius R Cu of the copper conductor bar, L Cu,min and L Cu,max are respectively the minimum value and the maximum value of the length L Cu of the copper conductor bar, d EP,min and d EP,max are respectively the minimum value and the maximum value of the thickness d EP of the epoxy fiberglass rod, L EP,min and L EP,max are respectively the minimum value and the maximum value of the length L EP of the epoxy fiberglass rod, n min and n max are respectively the minimum value and the maximum value of the number of umbrella skirt groups n.

3. A structural optimization system for a composite dry-type wall bushing, characterized in that, The system includes: A constraint condition determination module, configured to determine constraint conditions for each parameter of the structure of the composite dry-type through-wall bushing; the parameters include: the radius R of the copper conductor rod Cu , the length L of the copper conductor rod Cu , the thickness d of the epoxy glass fiber rod EP , the length L of the epoxy glass fiber rod EP , the number of umbrella skirt groups n; A multi-objective function construction module, which is used to construct a multi-objective function with the goal of minimizing the weight of the structure and minimizing the field strength at a preset position; A parameter optimization module, which is used to optimize each parameter of the structure based on the constraint conditions with the optimization of the multi-objective function as the goal, and obtain the parameter optimization result; The multi-objective function is: Among them, f1 is the first objective function aiming at minimizing the weight of the structure, f2 is the second objective function aiming at minimizing the field strength at a preset position, W is the weight of the structure, and E Cu is the field strength at the preset position; The calculation formula of W is: where p Cu , p EP , m SR , W Al are the density of the copper conductor bar, the density of the epoxy glass fiber rod, the mass of the petticoat group, and the mass of the flange, respectively; E Cu The solution equation for In the equation, ε is the dielectric constant at the preset position; is the electric potential at the preset position; ρ is the space charge density at the preset position; σ is the interfacial charge density at the preset position; J is the current density vector at the preset position, J n and J s represent the normal current density and the tangential current density at the preset position respectively; S e is the charge generation rate, and t represents the time variable; The parameter optimization module specifically includes: An initialization sub-module, which is used to generate an initial parent population that satisfies the constraint conditions; A child population generation module, which is used to perform selection, crossover, and mutation on the parent population to generate a child population that satisfies the constraint conditions; A population merging module, which is used to merge the child population and the parent population to obtain a merged population; A fast non-dominated sorting sub-module, which is used to perform fast non-dominated sorting on individuals in the merged population according to the multi-objective function, and determine the non-dominated rank of each individual in the merged population; A crowding degree calculation sub-module, which is used to calculate the crowding degree of each individual in the merged population according to the multi-objective function; An individual selection sub-module, which is used to select superior individuals from the merged population according to the non-dominated rank and the crowding degree, form the parental population for the next iteration, return to the step "select, crossover and mutate the parental population to generate the offspring population", and perform the next iteration until the preset number of iterations, and output the individual with the lowest non-dominated rank in the merged population as the parameter optimization result; The fast non-dominated sorting sub-module specifically includes: An individual parameter determination unit, which is used to determine the first parameter and the second parameter of each individual in the merged population according to the multi-objective function; wherein, the first parameter of the i-th individual is the number of other individuals in the merged population that dominate the i-th individual, and the second parameter of the i-th individual is the set of other individuals in the merged population that are dominated by the i-th individual; when all the objective function values of the i-th individual are better than the corresponding objective values of the j-th individual, the dominance relationship between the i-th individual and the j-th individual is: the i-th individual dominates the j-th individual, otherwise the i-th individual cannot dominate the j-th individual; An initialization unit, which is used to initialize the value of m to 1; The first non-dominated rank determination unit is used to store all individuals in the merged population whose first parameter is equal to 0 into the set F m and set the non-dominated rank of each individual in the set F m to m; A first parameter updating unit for subtracting 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m ; A second non-dominated rank determination unit is configured to increment the value of m by 1, store the individuals in the union whose first parameter is equal to 0 into set F m and set the non-dominated rank of each individual in set F m to m, and return to the step of "subtracting 1 from the first parameter of each individual in the union of the second parameters of each individual in set F m " until the union is empty.

4. The structural optimization system of the composite dry-type wall bushing according to claim 3, characterized in that, The constraint condition is: Among them, R Cu,min and R Cu,max are the minimum and maximum values of the radius R Cu of the copper conductor bar respectively, L Cu,min and L Cu,max are the minimum and maximum values of the length L Cu of the copper conductor bar respectively, d EP,min and d EP,max are the minimum and maximum values of the thickness d EP of the epoxy fiberglass rod respectively, L EP,min and L EP,max are the minimum and maximum values of the length L EP of the epoxy fiberglass rod respectively, n min and n max are the minimum and maximum values of the number of umbrella skirt groups n respectively; The calculation formula of the crowding degree is as follows: Among them, P i represents the crowding degree of the i-th individual, and f k,(i-1) and f k,(i+1) respectively represent the k-th objective function values in the multi-objective functions of the (i - 1)-th individual and the (i + 1)-th individual.

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