A structural optimization method and related device for three-pillar insulator

Abstract

The present application provides a structural optimization method and related device for a three-pillar insulator, which can repeatedly change the parameter values ​​of the key structural parameters of the three-pillar insulator to obtain multiple sets of test structural parameter combinations, and respectively generate test electric field distribution data corresponding to each set of test structural parameter combinations. By learning and training the multiple sets of test structural parameter combinations and the test electric field distribution data, a structural optimization model can be generated. The structural optimization model can then be used to extract the influence factors of each key structural parameter on the maximum composite electric field strength on the surface of the three-pillar insulator. Based on this, the structure of the three-pillar insulator is optimized to minimize the surface electric field strength of the three-pillar insulator and obtain the optimal structural parameter combination. In this way, the structural stability of the three-pillar insulator can be improved and the probability of discharge failure can be reduced, thereby increasing the safety margin of the GIL during operation and ensuring the safe and stable operation of the GIL.

Description

Technical Field

[0001] The present application relates to the field of gas insulation technology, and in particular to a structural optimization method and related devices for a three-pillar insulator. Background Art

[0002] Three-post insulators are a core component of gas-insulated transmission lines (GILs), directly impacting their operational reliability and stability. However, with the continuous improvement of transmission levels and expansion of transmission scale, discharge failures caused by three-post insulators are becoming increasingly common, seriously impacting the safe and stable operation of power grids. Therefore, it is necessary to optimize the structural design of three-post insulators to ensure safer and more reliable operation of GIL transmission corridors.

[0003] In the existing technology, there are few optimization solutions for the structural parameters of three-post insulators, and the relationship between these parameters and electric field strength is not clearly defined. As a result, existing technologies have difficulty improving the uneven electric field distribution on the surface of three-post insulators, resulting in a low safety margin during GIL operation. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defects of uneven electric field distribution on the surface of three-pillar insulators and low safety margin in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing the structure of a three-pillar insulator, comprising:

[0006] Obtaining an electric field simulation model of a three-pillar insulator; wherein the electric field simulation model is used to determine electric field distribution data of the three-pillar insulator based on a central cylinder width parameter, a central cylinder diameter parameter, and a connection fillet radius parameter of the three-pillar insulator;

[0007] Generate multiple sets of test structure parameter combinations based on a predetermined width value range, diameter value range, and fillet radius value range; wherein the test structure parameter combinations include a center cylinder width value, a center cylinder diameter value, and a connection fillet radius value;

[0008] Performing simulation calculations on each group of test structure parameter combinations based on the electric field simulation model, and obtaining test electric field distribution data corresponding to each group of test structure parameter combinations;

[0009] Performing learning and training on each group of test structure parameter combinations and the test electric field distribution data corresponding to each group of test structure parameter combinations to obtain a structure optimization model; the structure optimization model is used to respectively calculate the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0010] Taking minimizing the electric field intensity on the surface of the insulator as the optimization goal, according to the structural optimization model, the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter are optimized to obtain the optimal structural parameter combination.

[0011] In one embodiment, the optimization objective is to minimize the electric field intensity on the surface of the insulator, and according to the structural optimization model, the parameters of the central cylinder width, the central cylinder diameter, and the fillet radius of the connection are optimized to obtain the optimal structural parameter combination, including:

[0012] Performing data fitting on each group of the test structure parameter combinations and the test electric field distribution data corresponding to each group of the test structure parameter combinations to obtain a fitness function; wherein the fitness function is used to calculate the fitness value based on the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0013] Taking minimizing the electric field intensity on the surface of the insulator as the optimization goal, according to the structural optimization model and the fitness function, an improved grey wolf optimization algorithm is used to optimize the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter, and the optimal structural parameter combination is obtained.

[0014] In one embodiment, the fitness function is:

[0015] f=k1×f(a)+k2×f(b)+k3×f(c)

[0016]

[0017] Wherein, f is the individual fitness value; k1 is the first preset coefficient; f(a) is the width fitness value; k2 is the second preset coefficient; f(b) is the diameter fitness value; k3 is the third preset coefficient; f(c) is the radius fitness value; a is the influence of the central cylinder width parameter value on the maximum composite electric field strength on the insulator surface; b is the influence of the central cylinder diameter parameter value on the maximum composite electric field strength on the insulator surface; c is the influence of the connection fillet radius parameter value on the maximum composite electric field strength on the insulator surface.

[0018] In one embodiment, the improved grey wolf optimization algorithm is used to optimize the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter according to the structural optimization model and the fitness function, and the optimal structural parameter combination is obtained, including:

[0019] In the current round, the current gray wolf position of each gray wolf individual is determined; the current gray wolf position is a combination of structural parameters;

[0020] Calculating the individual fitness value corresponding to the current round of each gray wolf individual according to the structural optimization model, the fitness function and the current position of each gray wolf;

[0021] Based on the fitness values ​​of each individual in the current round, a target individual is determined among the gray wolf individuals; the target individual is the gray wolf individual with the lowest electric field intensity on the insulator surface;

[0022] If the individual fitness value of the target individual in the current round is better than the fitness value corresponding to the optimal position, the current gray wolf position of the target individual is used as the optimal position, and the individual fitness value of the target individual in the current round is used as the fitness value corresponding to the optimal position;

[0023] If it is determined that the preset iteration end condition is met, the optimal position is used as the optimal structural parameter combination, otherwise, the process proceeds to the next round.

[0024] In one embodiment, determining the current gray wolf position of each gray wolf individual in the current round includes:

[0025] If the current round is the first round, then according to the preset number N of gray wolf individuals, the current gray wolf positions of the N gray wolf individuals are generated;

[0026] If the current round is not the first round, the current gray wolf position of each gray wolf individual in the current round is determined according to the gray wolf position of each gray wolf individual in the previous round according to the preset position update strategy.

[0027] In one embodiment, the iteration termination condition is that the number of iterations is greater than or equal to a preset maximum number.

[0028] In one embodiment, the electric field simulation model includes a three-dimensional structural model of the three-pillar insulator, and the three-dimensional structural model is a three-dimensional axisymmetric model.

[0029] In a second aspect, an embodiment of the present application provides a structure optimization device for a three-pillar insulator, comprising:

[0030] A simulation model acquisition module is used to obtain an electric field simulation model of a three-pillar insulator; wherein the electric field simulation model is used to determine electric field distribution data of the three-pillar insulator based on a central cylinder width parameter, a central cylinder diameter parameter, and a connection fillet radius parameter of the three-pillar insulator;

[0031] A test combination generation module is used to generate multiple test structure parameter combinations based on a predetermined width value range, a diameter value range, and a fillet radius value range; wherein the test structure parameter combinations include a center cylinder width value, a center cylinder diameter value, and a connection fillet radius value;

[0032] An electric field simulation module, configured to perform simulation calculations on each group of test structure parameter combinations based on the electric field simulation model, and obtain test electric field distribution data corresponding to each group of test structure parameter combinations;

[0033] a structural optimization model acquisition module, configured to perform training on each group of test structural parameter combinations and the test electric field distribution data corresponding to each group of test structural parameter combinations to obtain a structural optimization model; the structural optimization model is configured to respectively calculate the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0034] An optimization module is used to optimize the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter based on the structural optimization model, with minimizing the electric field intensity on the insulator surface as the optimization goal, and obtain the optimal structural parameter combination.

[0035] In a third aspect, an embodiment of the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the structural optimization method of the three-pillar insulator described in any of the above embodiments.

[0036] In a fourth aspect, an embodiment of the present application provides a computer device, the computer device comprising: one or more processors, and a memory;

[0037] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the structural optimization method of the three-pillar insulator described in any of the above embodiments are executed.

[0038] In the structural optimization methods and related devices for three-post insulators provided in some embodiments of the present application, the parameter values ​​of key structural parameters of the three-post insulator can be repeatedly varied to obtain multiple sets of test structural parameter combinations, and test electric field distribution data corresponding to each test structural parameter combination can be generated. By training the multiple sets of test structural parameter combinations and the test electric field distribution data, a structural optimization model can be generated. The structural optimization model can then be used to extract the influence factors of each key structural parameter on the maximum composite electric field strength on the surface of the three-post insulator. Based on this, the structure of the three-post insulator is optimized to minimize the surface electric field strength of the three-post insulator and obtain the optimal structural parameter combination. In this way, the optimized three-post insulator can solve the problem of uneven surface electric field distribution and improve the electric field distribution on the structure surface, thereby enhancing the structural stability of the three-post insulator and reducing the probability of discharge failures. This can increase the safety margin of the GIL during operation and ensure the safe and stable operation of the GIL. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 1 is a flow chart of a method for optimizing the structure of a three-pillar insulator in one embodiment;

[0041] Figure 2 Schematic diagram of the central cylinder width, central cylinder diameter, and connection fillet radius of a three-pillar insulator in one embodiment;

[0042] Figure 3 Schematic diagram of the steps of parameter optimization using the improved grey wolf optimization algorithm in one embodiment;

[0043] Figure 4 FIG1 is a surface electric field distribution cloud diagram of a three-pillar insulator before optimization in one embodiment;

[0044] Figure 5 FIG1 is a surface electric field distribution cloud diagram of an optimized three-pillar insulator in one embodiment;

[0045] Figure 6 Schematic diagram of the structure of a three-pillar insulator structure optimization device in one embodiment;

[0046] Figure 7 FIG. 1 is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] In one embodiment, Figure 1 As shown, the present application provides a structural optimization method for a three-pillar insulator, comprising the following steps:

[0049] S102: Obtain an electric field simulation model of a three-pillar insulator.

[0050] The electric field simulation model is used to determine the electric field distribution data for a three-post insulator based on its central cylinder width, central cylinder diameter, and connection fillet radius parameters, all of which are key structural parameters of the three-post insulator.

[0051] The three-post insulator mentioned herein may include a three-post insulator body, which may include a central cylinder and three legs. The central cylinder may be a structure located at the center of the three-post insulator and used to accommodate a central guide rod. Each leg may be connected to the central cylinder and extend in a direction away from the center of the central cylinder. In one example, the three-post insulator body may be made of epoxy resin.

[0052] The width of the center cylinder, the diameter of the center cylinder and the radius of the fillet at the connection can be as follows Figure 2 As shown. The center cylinder diameter D can be the diameter of the center cylinder, and the connection fillet radius R can be the fillet radius of the connection between the column leg and the center cylinder. In an example, please combine Figure 2 The width H of the central cylinder can be the minimum width of two adjacent column legs on the central cylinder.

[0053] Furthermore, in addition to the three-leg insulator body, the three-leg insulator described in this application may also include a center barrel, a metal insert, and a metal shell. The metal insert is provided at the end of each leg away from the central cylinder, and the metal shell can be used to encase the three-leg insulator body. In one example, both the center barrel and the metal shell are made of aluminum alloy.

[0054] It can be understood that in this application, the electric field simulation model of the three-pillar insulator can be determined based on actual conditions. This article does not impose any specific restrictions on this. It only requires that the electric field simulation model can determine the electric field distribution data of the three-pillar insulator based on the specific values ​​of H, D and R.

[0055] In one example, the electric field simulation model includes a three-dimensional structural model of a three-pillar insulator. This three-dimensional structural model is a three-dimensional axisymmetric model. For example, the three-dimensional structural model can be constructed using a three-dimensional parametric equation model. By adjusting the specific values ​​of H, D, and / or R, a corresponding three-dimensional structural model can be generated, facilitating subsequent electric field simulation of the three-dimensional structural model.

[0056] S104: Generate multiple groups of test structure parameter combinations according to the predetermined width value range, diameter value range, and fillet radius value range.

[0057] The test structure parameter combination includes the center cylinder width value, the center cylinder diameter value, and the connection fillet radius value. When the parameter values ​​of the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter are determined, the structure of the three-pillar insulator is also determined.

[0058] In this step, the parameter values ​​of the center cylinder width parameter, the center cylinder diameter parameter, and / or the connection fillet radius parameter may be varied multiple times based on the width value range, the diameter value range, and the fillet radius value range, thereby generating multiple sets of test structure data. Each set of test structure data includes the parameter values ​​of the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter. Therefore, each set of test structure data can represent a three-pillar insulator structure.

[0059] It is understood that the width value range, diameter value range and rounded corner value range can be determined according to actual conditions and are not specifically limited herein. In one example, the width value range, diameter value range and rounded corner value range can be as shown in Table 1.

[0060] Table 1

[0061]

[0062] S106: performing simulation calculations on each test structure parameter combination based on the electric field simulation model, and obtaining test electric field distribution data corresponding to each test structure parameter combination.

[0063] In this step, when multiple groups of test structure parameter combinations are obtained, the electric field simulation model can be used to perform electric field calculation and analysis on the three-pillar insulator structure represented by each group of test structure parameter combinations, and the test electric field distribution data of each three-pillar insulation structure can be obtained.

[0064] In one example, during the electric field simulation, boundary conditions can be applied to the metal insert and center tube structure of a three-post insulator. The electric field simulation model is then used to calculate the surface electric field strength of the three-post insulator, thereby generating test electric field distribution data. In one example, the applied simulation voltage can be a lightning impulse voltage of 1675 kV, and the metal insert can be grounded, meaning that the surface voltage of the metal insert is zero.

[0065] S108: Performing learning and training on each set of test structural parameter combinations and the test electric field distribution data corresponding to each set of test structural parameter combinations to obtain a structural optimization model.

[0066] Among them, the structural optimization model is used to calculate the influence of the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface.

[0067] In this step, after obtaining each group of test structure parameter combinations and the test electric field distribution data corresponding to each group of test structure parameter combinations, they can be studied and trained to learn the influence of the central cylinder width parameter on the maximum composite electric field strength on the surface of the three-pillar insulator, the influence of the central cylinder diameter parameter on the maximum composite electric field strength on the surface of the three-pillar insulator, and the influence of the connection fillet radius parameter on the maximum composite electric field strength on the surface of the three-pillar insulator, and establish a structural optimization model.

[0068] In one example, the structural optimization model can be used to analyze the electric field distribution law of a three-pillar insulator when the center cylinder width parameter, center cylinder diameter parameter and connection fillet radius parameter are changed, thereby determining the degree of influence of the center cylinder width parameter, center cylinder diameter parameter and connection fillet radius parameter on the maximum composite electric field strength on the insulator surface.

[0069] S110: Taking minimizing the electric field intensity on the insulator surface as the optimization goal, according to the structural optimization model, the parameters of the center cylinder width, center cylinder diameter, and connection fillet radius are optimized to obtain the optimal structural parameter combination.

[0070] In this step, the minimum electric field intensity on the surface of the three-pillar insulator is taken as the optimization goal. According to the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter on the electric field on the surface of the insulator, the structure of the three-pillar insulator is optimized to obtain the parameter value that can minimize the surface electric field intensity of the three-pillar insulator, that is, to obtain the optimal structural parameter combination.

[0071] It is understood that the optimal structural parameter combination may include parameter values ​​corresponding to the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter. Compared to three-post insulators represented by other structural parameter combinations, the three-post insulator represented by the optimal structural parameter combination has the smallest surface electric field strength.

[0072] In the present application, the parameter values ​​of the key structural parameters of the three-pillar insulator can be changed multiple times to obtain multiple groups of test structural parameter combinations, and test electric field distribution data corresponding to each group of test structural parameter combinations can be generated. By learning and training multiple groups of test structural parameter combinations and each test electric field distribution data, a structural optimization model can be generated. The structural optimization model can then be used to extract the influence factors of each key structural parameter on the maximum composite electric field strength on the surface of the three-pillar insulator. Based on this, the structure of the three-pillar insulator is optimized to minimize the surface electric field strength of the three-pillar insulator and obtain the optimal structural parameter combination. In this way, the optimized three-pillar insulator can solve the problem of uneven surface electric field distribution and improve the electric field distribution on the surface of the structure, thereby improving the structural stability of the three-pillar insulator and reducing the probability of discharge failure. This can increase the safety margin of the GIL during operation and ensure the safe and stable operation of the GIL.

[0073] In one embodiment, S110 includes:

[0074] Step A1: performing data fitting on each set of test structural parameter combinations and the test electric field distribution data corresponding to each set of test structural parameter combinations, and obtaining a fitness function; wherein the fitness function is used to calculate a fitness value based on the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0075] Step A3: Taking minimizing the electric field intensity on the insulator surface as the optimization goal, according to the structural optimization model and fitness function, the improved Grey Wolf optimization algorithm is used to optimize the central cylinder width parameter, central cylinder diameter parameter, and connection fillet radius parameter, and the optimal structural parameter combination is obtained.

[0076] In this embodiment, during the structural optimization of the three-pillar insulator, the functional expression of the fitness function can be fitted based on each group of test structural parameter combinations and each group of test electric field distribution data, so that the fitness function can determine the fitness value based on the degree of influence of the three key structural parameters on the maximum synthetic field strength on the insulator surface.

[0077] Once the fitness function is fitted, the center cylinder width, center cylinder diameter, and connection fillet radius parameters can be used as decision variables, with minimizing the electric field intensity on the insulator surface as the optimization objective. The improved Grey Wolf optimization algorithm is then used to optimize the structural parameters of the three-pillar insulator. This approach balances solution accuracy and convergence speed, shortening the optimization time for the three-pillar insulator structure and obtaining the optimal structural parameter combination that minimizes the surface electric field intensity.

[0078] In one embodiment, the fitness function is:

[0079] f=k1×f(a)+k2×f(b)+k3×f(c)

[0080]

[0081] Wherein, f is the individual fitness value; k1 is the first preset coefficient; f(a) is the width fitness value; k2 is the second preset coefficient; f(b) is the diameter fitness value; k3 is the third preset coefficient; f(c) is the radius fitness value; a is the influence of the central cylinder width parameter value on the maximum composite electric field strength on the insulator surface; b is the influence of the central cylinder diameter parameter value on the maximum composite electric field strength on the insulator surface; c is the influence of the connection fillet radius parameter value on the maximum composite electric field strength on the insulator surface.

[0082] In this embodiment, during the structural optimization process based on the improved Grey Wolf optimization algorithm, if the parameter values ​​of the three key structural parameters are N1, N2, and N3, respectively, then the degree of influence a of the central cylinder width parameter with the value of N1 on the maximum composite electric field strength on the surface of the three-pillar insulator can be determined based on the structural optimization model. Similarly, the degree of influence b of the central cylinder diameter parameter with the value of N2 on the maximum composite electric field strength on the surface of the three-pillar insulator, and the degree of influence c of the connection fillet radius parameter with the value of N3 on the maximum composite electric field strength on the surface of the three-pillar insulator can also be determined based on the structural optimization model.

[0083] After determining the values ​​of a, b, and c, the present application can calculate the values ​​of f(a), f(b), and f(c) based on the above expressions, and then obtain the individual fitness value corresponding to the structural parameter combination (N1, N2, N3). This individual fitness value can reflect the surface electric field of the three-pillar insulator structure represented by (N1, N2, N3).

[0084] It can be understood that the specific values ​​of the first preset coefficient, the second preset coefficient and the third preset coefficient can be determined according to actual conditions, and no specific limitation is imposed herein.

[0085] In this embodiment, by using the fitness function, the error between the calculated optimal structural parameter combination and the actual size value of the three-post insulator can be minimized. The actual size value can be the size value of a typical three-post insulator on the market.

[0086] In one embodiment, please combine Figure 3 According to the structural optimization model and fitness function, the improved Grey Wolf optimization algorithm is used to optimize the central cylinder width parameters, central cylinder diameter parameters and connection fillet radius parameters, and the optimal structural parameter combination is obtained, including:

[0087] Step A31: in the current round, determining the current gray wolf position of each gray wolf individual; the current gray wolf position is a combination of structural parameters;

[0088] Step A33: Calculate the individual fitness value of each individual gray wolf in the current round based on the structural optimization model, the fitness function, and the current position of each gray wolf;

[0089] Step A35: Based on the fitness values ​​of each individual in the current round, a target individual is determined among the gray wolf individuals; the target individual is the gray wolf individual with the lowest electric field intensity on the insulator surface;

[0090] Step A37: If the individual fitness value of the target individual in the current round is better than the fitness value corresponding to the optimal position, the current gray wolf position of the target individual is used as the optimal position, and the individual fitness value of the target individual in the current round is used as the fitness value corresponding to the optimal position;

[0091] Step A39: If it is determined that the preset iteration end condition is met, the optimal position is used as the optimal structural parameter combination; otherwise, the process proceeds to the next round.

[0092] In this embodiment, during structural optimization using the improved Gray Wolf Optimization Algorithm, three key structural parameters can be used as decision variables, and these decision variables constitute the Gray Wolf individuals in the population. The improved Gray Wolf Optimization Algorithm aims to reduce the maximum composite electric field strength on the surface of a three-post insulator by adjusting the values ​​of these three key structural parameters. Furthermore, in one example, the maximum composite electric field strength of the three-post insulator does not exceed 12 kV / mm.

[0093] The improved gray wolf optimization algorithm can determine the optimal combination of structural parameters through multiple rounds of iteration. In each round, the position of each gray wolf individual in the current round can be first determined, that is, the current gray wolf position of each gray wolf individual. It can be understood that since this application uses three key structural parameters as decision variables, the gray wolf position can be understood as the structural parameter combination described above, for example (N1, N2, N3), where N1 is the value of the central cylinder width parameter, N2 is the value of the central cylinder diameter parameter, and N3 is the value of the joint fillet radius parameter.

[0094] After determining the current location of each individual gray wolf, the individual fitness value of each individual gray wolf in the current round can be calculated based on the aforementioned fitness function. A target individual can be identified based on this fitness value, which can be considered the local optimum for this round. For example, this application can sort the individual gray wolves / current locations based on their individual fitness values ​​in the current round to identify the specific location where the electric field intensity reaches the lowest value, and record the individual fitness value corresponding to that location.

[0095] The present application can determine whether the local optimal value generated in this round is better than the optimal position as the global optimal value based on the individual fitness value corresponding to the target individual and the individual fitness value corresponding to the determined optimal position. If so, the local optimal value can be used to replace the global optimal value, so that the optimal position after replacement is the current gray wolf position of the target individual. Furthermore, if the current round is the first round, the optimal position and its corresponding individual fitness value can be preset initial values, or the current gray wolf position of the target individual and the individual fitness value corresponding to the target individual can be directly recorded as the optimal position and its individual fitness value, respectively, and subsequent iterative rounds are executed accordingly.

[0096] After the local optimum and the global optimum are compared, it is determined whether the pre-set conditions for the iteration end are met. If so, the final optimal position is output as the optimal structural parameter combination. Otherwise, the next round is entered and steps A31 to A39 are repeated.

[0097] It is understood that the condition for ending the iteration can be determined based on actual conditions, and this document does not impose specific restrictions on this. In one example, the condition for ending the iteration is that the number of iterations is greater than or equal to a preset maximum number T. In other words, the improved Gray Wolf Optimization Algorithm needs to perform T rounds of iterative optimization to determine the optimal structural parameter combination. Among them, the preset maximum number can be determined based on actual conditions, so that engineers can obtain the optimal structural parameter combination that meets their needs by adjusting the value of the preset maximum number. In this way, the flexibility of structural optimization can be improved.

[0098] In one embodiment, please combine Figure 3, in the current round, determine the current gray wolf position of each gray wolf individual, including:

[0099] If the current round is the first round, then the current gray wolf positions of N gray wolf individuals are generated according to the preset number of gray wolf individuals N;

[0100] If the current round is not the first round, the current gray wolf position of each gray wolf individual in the current round is determined according to the gray wolf position of each gray wolf individual in the previous round according to the preset position update strategy.

[0101] In this embodiment, if the current round is the first round, the application determines appropriate initial values ​​based on the three key structural parameters, randomly generates N gray wolf individuals, and determines the initial gray wolf position corresponding to each gray wolf individual. N is the preset number of gray wolf individuals, which can be set according to actual circumstances and is not specifically limited herein.

[0102] If the current round is not the first round, that is, the current round is the Mth round, and M is a positive integer greater than 1, then according to the preset position update strategy, the current gray wolf position of each gray wolf individual in the Mth round is determined according to the gray wolf position of each gray wolf individual in the (M-1)th round.

[0103] In one example, the present application can calculate the threshold a' based on the population search situation in the (M-1)th round, and calculate the coefficients A1, C1, A2, C2, A3 and C3 respectively, and determine the Alpha, Beta and Delta of the three leading wolf individuals in the (M-1)th round respectively. When updating the current gray wolf position of a gray wolf individual in the Mth round, the distance between the gray wolf individual and Alpha, the distance between the gray wolf individual and Beta, and the distance between the gray wolf individual and Delta can be calculated respectively based on the gray wolf position of the gray wolf individual in the (M-1)th round and the gray wolf positions of the three leading wolf individuals in the (M-1)th round. The gray wolf position of the gray wolf individual is updated based on the three distances, and the gray wolf position of the gray wolf individual in the Mth round can be determined.

[0104] In one example, the present application is described in conjunction with the structural optimization of a 500kV GIL three-post insulator. Figure 4 The surface electric field distribution cloud diagram of the three-pillar insulator under lightning impulse voltage before optimization is shown. It can be seen that the maximum composite field strength on the surface of the three-pillar insulator before optimization is 14.12kV / mm. After optimization using the structural optimization method of the three-pillar insulator provided by this application, the surface electric field distribution cloud diagram of the three-pillar insulator under lightning impulse voltage is shown as follows: Figure 5 It can be seen that after optimization, the maximum composite electric field strength on the surface of the three-pillar insulator is 10.25 kV / mm, which is lower than that before optimization.

[0105] The structural optimization device for a three-pillar insulator provided in an embodiment of the present application is described below. The structural optimization device for a three-pillar insulator described below and the structural optimization method for a three-pillar insulator described above can be referenced to each other.

[0106] In one embodiment, Figure 6 As shown, the present application provides a structure optimization device 600 for a three-pillar insulator, comprising:

[0107] A simulation model acquisition module 602 is configured to acquire an electric field simulation model of a three-pillar insulator; wherein the electric field simulation model is configured to determine electric field distribution data of the three-pillar insulator based on a central cylinder width parameter, a central cylinder diameter parameter, and a connection fillet radius parameter of the three-pillar insulator;

[0108] A test combination generating module 604 is configured to generate multiple test structure parameter combinations based on predetermined width value ranges, diameter value ranges, and fillet radius value ranges; wherein the test structure parameter combinations include a center cylinder width value, a center cylinder diameter value, and a connection fillet radius value;

[0109] An electric field simulation module 606 is configured to perform simulation calculations on each group of the test structure parameter combinations based on the electric field simulation model, and obtain test electric field distribution data corresponding to each group of the test structure parameter combinations;

[0110] The structural optimization model acquisition module 608 is used to learn and train each group of the test structural parameter combinations and the test electric field distribution data corresponding to each group of the test structural parameter combinations to obtain a structural optimization model; the structural optimization model is used to respectively calculate the influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0111] The optimization module 610 is used to optimize the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter according to the structural optimization model, with minimizing the electric field strength on the insulator surface as the optimization goal, and obtain the optimal structural parameter combination.

[0112] In one embodiment, the optimization module 610 of the present application includes:

[0113] a data fitting unit, configured to perform data fitting on each group of the test structure parameter combinations and the test electric field distribution data corresponding to each group of the test structure parameter combinations, and obtain a fitness function; wherein the fitness function is configured to calculate a fitness value based on the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface;

[0114] An optimization unit is used to optimize the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter using an improved gray wolf optimization algorithm based on the structural optimization model and the fitness function, with minimizing the electric field intensity on the insulator surface as the optimization goal, and obtain the optimal structural parameter combination.

[0115] In one embodiment, the fitness function is:

[0116] f=k1×f(a)+k2×f(b)+k3×f(c)

[0117]

[0118] Wherein, f is the individual fitness value; k1 is the first preset coefficient; f(a) is the width fitness value; k2 is the second preset coefficient; f(b) is the diameter fitness value; k3 is the third preset coefficient; f(c) is the radius fitness value; a is the influence of the central cylinder width parameter value on the maximum composite electric field strength on the insulator surface; b is the influence of the central cylinder diameter parameter value on the maximum composite electric field strength on the insulator surface; c is the influence of the connection fillet radius parameter value on the maximum composite electric field strength on the insulator surface.

[0119] In one embodiment, the optimization unit of the present application includes:

[0120] A position determination unit, configured to determine the current gray wolf position of each gray wolf individual in the current round; the current gray wolf position is a combination of structural parameters;

[0121] An individual fitness calculation unit, configured to calculate the individual fitness value corresponding to the current round of each gray wolf individual according to the structural optimization model, the fitness function, and the current position of each gray wolf;

[0122] a target individual determining unit, configured to determine a target individual from among the gray wolf individuals based on the fitness values ​​of the individuals in the current round; the target individual being the gray wolf individual with the lowest electric field intensity on the insulator surface;

[0123] an optimal position determining unit, configured to, if the individual fitness value of the target individual in the current round is better than the fitness value corresponding to the optimal position, use the current gray wolf position of the target individual as the optimal position, and use the individual fitness value of the target individual in the current round as the fitness value corresponding to the optimal position;

[0124] The iteration judgment unit is used to use the optimal position as the optimal structural parameter combination if it is determined that the preset iteration end condition is met, otherwise, enter the next round.

[0125] In one embodiment, the position determination unit of the present application includes:

[0126] A first determining unit is configured to generate current gray wolf positions of N gray wolf individuals according to a preset number N of gray wolf individuals if the current round is the first round;

[0127] The second determination unit is used to determine the current gray wolf position of each gray wolf individual in the current round according to the gray wolf position of each gray wolf individual in the previous round according to a preset position update strategy if the current round is not the first round.

[0128] In one embodiment, the iteration termination condition is that the number of iterations is greater than or equal to a preset maximum number.

[0129] In one embodiment, the electric field simulation model includes a three-dimensional structural model of the three-pillar insulator, and the three-dimensional structural model is a three-dimensional axisymmetric model.

[0130] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the structural optimization method of the three-pillar insulator in any embodiment.

[0131] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the structural optimization method of the three-pillar insulator in any embodiment.

[0132] Schematically, Figure 7 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. In one example, the computer device may be a server. Figure 7Computer device 900 includes a processing component 902, which further includes one or more processors, and memory resources represented by memory 901 for storing instructions executable by processing component 902, such as application programs. The application programs stored in memory 901 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 902 is configured to execute the instructions to perform the steps of the three-post insulator structural optimization method described in any of the above-described embodiments.

[0133] The computer device 900 may further include a power supply component 903 configured to perform power management of the computer device 900, a wired or wireless network interface 904 configured to connect the computer device 900 to a network, and an input / output (I / O) interface 905. The computer device 900 may operate based on an operating system stored in the memory 901, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0134] Those skilled in the art will understand that the internal structure of the computer device shown in the present application is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0135] Finally, it should be noted that, in this article, relational terms such as first and second are merely used to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. Without further restriction, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element. Herein, "one," "said," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. A plurality refers to at least two, such as 2, 3, 5, or 8. "And / or" includes any and all combinations of the relevant listed items.

[0136] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0137] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A structural optimization method for a three-pillar insulator, characterized in that: include: Obtaining an electric field simulation model of a three-pillar insulator; wherein the electric field simulation model is used to determine electric field distribution data of the three-pillar insulator based on a central cylinder width parameter, a central cylinder diameter parameter, and a connection fillet radius parameter of the three-pillar insulator; Generate multiple sets of test structure parameter combinations based on a predetermined width value range, diameter value range, and fillet radius value range; wherein the test structure parameter combinations include a center cylinder width value, a center cylinder diameter value, and a connection fillet radius value; Performing simulation calculations on each group of test structure parameter combinations based on the electric field simulation model, and obtaining test electric field distribution data corresponding to each group of test structure parameter combinations; Performing learning and training on each group of test structure parameter combinations and the test electric field distribution data corresponding to each group of test structure parameter combinations to obtain a structure optimization model; the structure optimization model is used to respectively calculate the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface; Taking minimizing the electric field intensity on the surface of the insulator as the optimization goal, according to the structural optimization model, the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter are optimized to obtain the optimal structural parameter combination.

2. The method according to claim 1, characterized in that Taking minimizing the electric field intensity on the surface of the insulator as the optimization goal, according to the structural optimization model, the parameters of the central cylinder width parameter, the central cylinder diameter parameter and the fillet radius parameter of the connection are optimized to obtain the optimal structural parameter combination, including: Performing data fitting on each group of the test structure parameter combinations and the test electric field distribution data corresponding to each group of the test structure parameter combinations to obtain a fitness function; wherein the fitness function is used to calculate the fitness value based on the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface; Taking minimizing the electric field intensity on the surface of the insulator as the optimization goal, according to the structural optimization model and the fitness function, an improved grey wolf optimization algorithm is used to optimize the central cylinder width parameter, the central cylinder diameter parameter and the connection fillet radius parameter, and the optimal structural parameter combination is obtained.

3. The method according to claim 2, characterized in that The fitness function is: f=k1×f(a)+k2×f(b)+k3×f(c) Wherein, f is the individual fitness value; k1 is the first preset coefficient; f(a) is the width fitness value; k2 is the second preset coefficient; f(b) is the diameter fitness value; k3 is the third preset coefficient; f(c) is the radius fitness value; a is the influence of the central cylinder width parameter value on the maximum composite electric field strength on the insulator surface; b is the influence of the central cylinder diameter parameter value on the maximum composite electric field strength on the insulator surface; c is the influence of the connection fillet radius parameter value on the maximum composite electric field strength on the insulator surface.

4. The method according to claim 2 or 3, characterized in that The improved grey wolf optimization algorithm is used to optimize the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter according to the structural optimization model and the fitness function, and the optimal structural parameter combination is obtained, including: In the current round, the current gray wolf position of each gray wolf individual is determined; the current gray wolf position is a combination of structural parameters; Calculating the individual fitness value corresponding to the current round of each gray wolf individual according to the structural optimization model, the fitness function and the current position of each gray wolf; Based on the fitness values ​​of each individual in the current round, a target individual is determined among the gray wolf individuals; the target individual is the gray wolf individual with the lowest electric field intensity on the insulator surface; If the individual fitness value of the target individual in the current round is better than the fitness value corresponding to the optimal position, the current gray wolf position of the target individual is used as the optimal position, and the individual fitness value of the target individual in the current round is used as the fitness value corresponding to the optimal position; If it is determined that the preset iteration end condition is met, the optimal position is used as the optimal structural parameter combination, otherwise, the process proceeds to the next round.

5. The method according to claim 4, characterized in that In the current round, determining the current gray wolf position of each gray wolf individual includes: If the current round is the first round, then according to the preset number N of gray wolf individuals, the current gray wolf positions of the N gray wolf individuals are generated; If the current round is not the first round, the current gray wolf position of each gray wolf individual in the current round is determined according to the gray wolf position of each gray wolf individual in the previous round according to the preset position update strategy.

6. The method according to claim 4, characterized in that The iteration end condition is that the number of iterations is greater than or equal to a preset maximum number.

7. The method according to any one of claims 1 to 3, characterized in that The electric field simulation model includes a three-dimensional structural model of the three-pillar insulator, and the three-dimensional structural model is a three-dimensional axisymmetric model.

8. A structure optimization device for a three-pillar insulator, characterized in that: include: A simulation model acquisition module is used to obtain an electric field simulation model of a three-pillar insulator; wherein the electric field simulation model is used to determine electric field distribution data of the three-pillar insulator based on a central cylinder width parameter, a central cylinder diameter parameter, and a connection fillet radius parameter of the three-pillar insulator; A test combination generation module is used to generate multiple test structure parameter combinations based on a predetermined width value range, a diameter value range, and a fillet radius value range; wherein the test structure parameter combinations include a center cylinder width value, a center cylinder diameter value, and a connection fillet radius value; An electric field simulation module, configured to perform simulation calculations on each group of test structure parameter combinations based on the electric field simulation model, and obtain test electric field distribution data corresponding to each group of test structure parameter combinations; a structural optimization model acquisition module, configured to perform training on each group of test structural parameter combinations and the test electric field distribution data corresponding to each group of test structural parameter combinations to obtain a structural optimization model; the structural optimization model is configured to respectively calculate the degree of influence of the central cylinder width parameter, the central cylinder diameter parameter, and the connection fillet radius parameter on the maximum composite electric field strength on the insulator surface; An optimization module is used to optimize the center cylinder width parameter, the center cylinder diameter parameter, and the connection fillet radius parameter based on the structural optimization model, with minimizing the electric field intensity on the insulator surface as the optimization goal, and obtain the optimal structural parameter combination.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to perform the steps of the structural optimization method for a three-pillar insulator as claimed in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the structural optimization method for a three-pillar insulator according to any one of claims 1 to 7.

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