Insulator parameter determination method and device, storage medium and electronic equipment

Abstract

The invention discloses an insulator parameter determination method and device, a storage medium and electronic equipment, and relates to the technical field of composite insulator design, and the method comprises the steps: carrying out the simulation model construction based on a preset physical structure of an insulator to be subjected to parameter determination, and obtaining a two-dimensional axial symmetry simulation model of the insulator; function construction is carried out based on a two-dimensional axial symmetry simulation model by taking minimization of the maximum electric field intensity of the insulator predetermined area as a target, an objective function used for determining insulator structure parameters is obtained, and the objective function comprises to-be-optimized structure parameters; constructing constraint conditions; and based on the constraint condition, optimizing the target function by adopting a preset optimization algorithm to obtain a target parameter value corresponding to the to-be-optimized structural parameter of the insulator. According to the composite insulator designed and obtained by determining the optimal structural parameters through the method, the electric field intensity of the surface of the umbrella skirt is remarkably reduced, the electrical performance of the composite insulator is greatly improved, and the composite insulator has higher application performance.

Description

Technical Field

[0001] The present invention relates to the field of composite insulator design, and in particular to an insulator parameter determination method, device, storage medium and electronic equipment. Background Art

[0002] Composite insulators are widely used due to their hydrophobic surface, light weight, good flashover performance, easy installation, low cost, and convenient operation and maintenance. The short-term and long-term performance of composite insulators is mainly affected by the electric field intensity distribution. In the electric field intensity distribution of high-voltage composite insulators, high electric field intensity is concentrated near its terminals. Reducing the electric field near the terminals is of great significance to improving the performance of insulators.

[0003] At present, the research on the design of composite insulators only considers the use of grading rings or zinc oxide microvaristors to reduce the maximum electric field strength of the composite insulators, but the electrical performance of the designed composite insulators is low. Summary of the invention

[0004] In view of this, the present invention provides a method, device, storage medium and electronic device for determining insulator parameters, the main purpose of which is to solve the problem of low electrical performance of composite insulators obtained by considering a single factor in the design of composite insulators.

[0005] In order to solve the above problems, the present application provides a method for determining insulator parameters, comprising:

[0006] A simulation model is constructed based on a preset physical structure of the insulator to be determined by parameters, so as to obtain a two-dimensional axisymmetric simulation model of the insulator;

[0007] Based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, to obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0008] Construct constraints;

[0009] Based on the constraint conditions, the objective function is optimized using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0010] Optionally, constructing a simulation model based on a preset physical structure of the insulator to be determined by parameters to obtain a two-dimensional axisymmetric simulation model of the insulator specifically includes:

[0011] Based on the preset physical structure, obtaining initial structural parameters, the number of sheds and predetermined electrical parameters of the insulator;

[0012] A simulation model is constructed based on the initial structural parameters, the number of sheds and the predetermined electrical parameters using a preset finite element analysis method to obtain a two-dimensional axisymmetric simulation model of the insulator.

[0013] Optionally, the structural parameters to be optimized include: the radius of the equalizing ring tube, the radius of the equalizing ring, the first equalizing ring height, the second equalizing ring height, the first umbrella skirt radius, the second umbrella skirt radius, the umbrella skirt spacing, the first microvaristor layer width, the first microvaristor layer height, the second microvaristor layer width and the second microvaristor layer height.

[0014] Optionally, the construction constraints specifically include:

[0015] Constructing constraint conditions based on boundary thresholds corresponding to the structural parameters to be optimized to obtain preset boundary constraint conditions;

[0016] Constraint conditions are constructed based on the first shed radius, the second shed radius and the preset shed distance minimum difference in the structural parameters to be optimized to obtain the preset shed radius constraint conditions.

[0017] Optionally, based on the constraint condition, optimizing the objective function using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator specifically includes:

[0018] Initialize the parameters in the optimization algorithm;

[0019] Determining a search boundary value of the preset optimization algorithm based on the constraint condition;

[0020] Based on the preset parameter values ​​of the parameters and the search boundary value, randomly initializing the initial position of each individual in the population;

[0021] Based on each of the initial positions, calculating an initial fitness value corresponding to each of the individuals;

[0022] Screening each of the initial fitness values ​​to obtain an initial target fitness value to obtain an initial optimal individual;

[0023] Determining the parameter value corresponding to the initial position of the initial optimal individual as the initial optimal parameter value;

[0024] Based on the initial optimal parameter value, each individual in the population is updated in a loop iteration manner to re-determine the current optimal individual based on the current position of each updated individual, until the number of loop iterations reaches a preset iteration number threshold, thereby obtaining the target parameter value corresponding to the structural parameter to be optimized.

[0025] Optionally, based on the initial optimal parameter value, each individual in the population is updated in a loop iteration manner, so as to re-determine the current optimal individual based on the current position of each updated individual, until the number of loop iterations reaches a preset iteration number threshold, and the target parameter value corresponding to the structural parameter to be optimized is obtained, specifically including:

[0026] Step 1, randomly matching a follower individual for each individual;

[0027] Step 2: Based on the random probability of each individual matching the following individual and the predetermined perception probability, the position of each individual is updated to obtain the current position corresponding to each individual;

[0028] Step 3: Calculate based on each current position to obtain a current fitness value corresponding to each individual;

[0029] Step 4: Screening based on each of the current fitness values ​​to obtain a current target fitness value;

[0030] Step 5: determining the current optimal individual position of the current iteration round based on the current target fitness value and the initial target fitness value to obtain the current optimal parameter value corresponding to the structural parameter to be optimized;

[0031] Step 6: Determine whether the current iteration round is equal to the preset round threshold and obtain a determination result;

[0032] Step 7: When the current iteration round is less than the preset round threshold, repeat steps 1 to 6 until the current iteration round is equal to the preset round threshold, and then determine the updated current optimal parameter value as the target parameter value corresponding to the structural parameter to be optimized.

[0033] Step eight: when the current iteration round is equal to the preset round threshold, the current optimal parameter value is determined as the target parameter value corresponding to the structural parameter to be optimized.

[0034] Optionally, the updating of the position of each individual based on the random probability of each individual matching the following individual and the predetermined perception probability to obtain the current position corresponding to each individual specifically includes:

[0035] When the random probability that the same individual moves toward the following individual corresponding to the same individual is greater than the predetermined perception probability, the current position of the same individual is determined using the first displacement function;

[0036] When the random probability that the same individual moves toward the following individual corresponding to the same individual is less than or equal to the predetermined perception probability, the current position corresponding to the same individual is randomly determined using a second displacement function.

[0037] In order to solve the above problems, the present application provides an insulator parameter determination device, comprising:

[0038] A simulation model building module, used to build a simulation model based on a preset physical structure of the insulator to be determined by parameters, and obtain a two-dimensional axisymmetric simulation model of the insulator;

[0039] A determination module, configured to construct a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, and obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0040] Constraint building module, used to build constraints;

[0041] The optimization module is used to optimize the objective function based on the constraint conditions by using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0042] In order to solve the above problem, the present application provides a storage medium, characterized in that the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned insulator parameter determination method are implemented.

[0043] To solve the above problem, the present application provides an electronic device, characterized in that it comprises at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned insulator parameter determination method when executing the computer program in the memory.

[0044] Beneficial effects of the present application: The present application constructs a simulation model based on the preset physical structure of the insulator to be determined by the parameters, and obtains a two-dimensional axisymmetric simulation model of the insulator; based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field strength in the predetermined area of ​​the insulator, and an objective function for determining the structural parameters of the insulator is obtained, and the objective function includes the structural parameters to be optimized; constructs constraints; based on the constraints, the objective function is optimized using a preset optimization algorithm to obtain the target parameter values ​​corresponding to the structural parameters to be optimized of the insulator. The present application simultaneously considers the dual grading rings and the zinc oxide microvaristor layer and optimizes the objective function using a preset optimization algorithm. The designed composite insulator significantly reduces the electric field strength on the surface of the shed, greatly improves the electrical performance of the composite insulator, and has stronger applicability.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 This is a flow chart of a method for determining insulator parameters according to an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a preset physical structure of an insulator provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of a two-dimensional axisymmetric simulation model of an insulator provided in an embodiment of the present application;

[0050] Figure 4 A flow chart of a method for determining insulator parameters provided in yet another embodiment of the present application;

[0051] Figure 5 This is a structural block diagram of an insulator parameter determination device according to another embodiment of the present application;

[0052] Figure 6 This is a schematic diagram of the electric field intensity distribution on the surface of the insulator before and after optimization of the embodiment of the present application;

[0053] Figure 7 This is a schematic diagram of the electric field intensity distribution on the surface of the insulator in two cases with and without a microvaristor layer after optimization of the embodiment of the present application. DETAILED DESCRIPTION

[0054] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0055] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0056] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0057] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0058] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0059] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0060] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0061] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0062] The present application embodiment provides a method for determining insulator parameters, such as Figure 1 As shown, including:

[0063] Step S101: constructing a simulation model based on a preset physical structure of an insulator to be determined by parameters, and obtaining a two-dimensional axisymmetric simulation model of the insulator;

[0064] In the specific implementation process of this step, the initial structural parameters, the number of sheds and the predetermined electrical parameters of the insulator are obtained based on the preset physical structure; the structural schematic diagram of the preset physical structure of the insulator is as follows: Figure 2As shown, the insulator includes a first metal end, a second metal end, a first equalizing ring, a second equalizing ring, a plurality of first umbrella skirts, a plurality of second umbrella skirts, a first microvaristor layer, a second microvaristor layer, a core rod and other components. The initial structural parameters are initial values ​​corresponding to the structural parameters to be optimized corresponding to the insulator. The first microvaristor layer and the second microvaristor layer can be zinc oxide microvaristor layers; the number of umbrella skirts includes the number of large umbrella skirts and the number of small umbrella skirts, and the large umbrella skirts and the small umbrella skirts are cross-spaced in the preset physical structure; the predetermined electrical parameters include parameters such as the relative node constant and conductivity of the material used for the insulator. Based on the initial structural parameters, the number of umbrella skirts and the predetermined electrical parameters, a preset finite element analysis method is used to construct a simulation model to obtain a two-dimensional axisymmetric simulation model of the insulator. As shown Figure 3 Shown is a schematic diagram of a two-dimensional axisymmetric simulation model of the insulator.

[0065] Step S102: constructing a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, to obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0066] In the specific implementation of this step, the structural parameters to be optimized include the radius of the grading ring tube, the radius of the grading ring, the first grading ring height, the second grading ring height, the first shed radius, the second shed radius, the shed spacing, the first microvaristor layer width, the first microvaristor layer height, the second microvaristor layer width and the second microvaristor layer height. The predetermined area can be the shed surface area of ​​the insulator.

[0067] Step S103: construct constraint conditions;

[0068] In the specific implementation process of this step, constraint conditions are constructed based on boundary thresholds corresponding to the structural parameters to be optimized to obtain preset boundary constraint conditions; constraint conditions are constructed based on the first shed radius, the second shed radius and the preset shed distance threshold in the structural parameters to be optimized to obtain preset shed radius constraint conditions.

[0069] Step S104: Based on the constraint conditions, a preset optimization algorithm is used to optimize the objective function to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0070] In the specific implementation process of this step, each parameter in the optimization algorithm is initialized; each of the parameters includes parameters such as population size, maximum number of iterations, and search dimension. Based on the constraints, the search boundary value of the preset optimization algorithm is determined; based on the preset parameter value of each parameter and the search boundary value, the initial position of each individual in the population is randomly initialized; based on each initial position, the initial fitness value corresponding to each individual is calculated; each initial fitness value is screened to obtain the initial target fitness value to obtain the initial optimal individual; the parameter value corresponding to the initial position of the initial optimal individual is determined as the initial optimal parameter value; based on the initial optimal parameter value, each individual in the population is updated in a cyclic iteration manner to re-determine the current optimal individual based on the current position of each individual after the update, until the number of cyclic iterations reaches the preset iteration number threshold, and the target parameter value corresponding to the structural parameter to be optimized is obtained.

[0071] This application constructs a simulation model based on the preset physical structure of the insulator to be determined by parameters, and obtains a two-dimensional axisymmetric simulation model of the insulator; based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field strength in the predetermined area of ​​the insulator, and an objective function for determining the structural parameters of the insulator is obtained, and the objective function includes the structural parameters to be optimized; constructs constraints; based on the constraints, the objective function is optimized using a preset optimization algorithm to obtain the target parameter value corresponding to the structural parameters to be optimized of the insulator. This application simultaneously considers the dual grading ring and the zinc oxide microvaristor layer and optimizes the objective function using a preset optimization algorithm. The designed composite insulator significantly reduces the electric field strength on the surface of the shed, greatly improves the electrical performance of the composite insulator, and has stronger applicability.

[0072] Another embodiment of the present application provides another method for determining insulator parameters, such as Figure 4 As shown, including:

[0073] Step S201: constructing a simulation model based on a preset physical structure of the insulator to be determined by parameters, and obtaining a two-dimensional axisymmetric simulation model of the insulator;

[0074] In the specific implementation process of this step, based on the preset physical structure, the initial structural parameters, the number of sheds and the predetermined electrical parameters of the insulator are obtained; Figure 2As shown, the insulator includes a first metal end ①, a second metal end ⑨, a first grading ring ③, a second grading ring ⑧, a plurality of first sheds ⑤, a plurality of second sheds ⑥, a first microvaristor layer ④, a second microvaristor layer ⑦ and a core rod ② and other components. The initial structural parameters are initial values ​​of the structural parameters to be optimized corresponding to the insulator. The initial values ​​of the structural parameters to be optimized can be manually configured or automatically randomly generated. Table 1 is a data table of initial values ​​of the structural parameters to be optimized:

[0075] Main parameters Value(mm) Radius of pressure equalizing ring 15 Radius of pressure equalizing ring 130 Upper pressure ring height 150 Height of lower pressure equalizing ring 150 Large shed radius 82.5 Small skirt radius 62.5 Umbrella skirt spacing 45

[0076] Table 1

[0077] The first microvaristor layer and the second microvaristor layer can be zinc oxide microvaristor layers; the number of umbrella skirts includes the number of large umbrella skirts and the number of small umbrella skirts, and the large umbrella skirts and the small umbrella skirts in the preset physical structure are arranged in parallel with the voltage-equalizing ring in a cross-interval; for example: the total number of umbrella skirts can be configured as 97, of which the number of small umbrella skirts is 49 and the number of large umbrella skirts is 48. In the specific implementation process, the number of umbrella skirts can be set according to actual needs. The predetermined electrical parameters include parameters such as the relative dielectric constant and conductivity of the material used for the insulator. Table 2 is a data table of the predetermined electrical parameters:

[0078] Material <![CDATA[Relative permittivity (ε r )]]> Conductivity (σ(S / m)) Umbrella skirt 4.3 <![CDATA[1×10 -13 ]]> Air 1 <![CDATA[1×10 -13 ]]> Micro varistor layer 12 σ(E) Mandrel (FRP) 7.1 <![CDATA[1×10 -13 ]]>

[0079] Table 2

[0080] A simulation model is constructed based on the initial structural parameters, the number of sheds and the predetermined electrical parameters using a preset finite element analysis method to obtain a two-dimensional axisymmetric simulation model of the insulator.

[0081] Step S202: constructing a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, to obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0082] In the specific implementation of this step, the predetermined area is the surface area of ​​the shed of the insulator. The mathematical expression of the objective function can be expressed as the following formula (1):

[0083] F=min(E max ) (1)

[0084] Among them, E max is the maximum value of the electric field strengths E in the surface area of ​​the shed. The structural parameters to be optimized include: the radius of the pressure equalizing ring tube r1, the radius of the pressure equalizing ring R1, the height of the first pressure equalizing ring H1, the height of the second pressure equalizing ring H2, the radius of the first shed R s1, the second shed radius R s2 、Parcel spacing D d , a first microvaristor layer width X1, a first microvaristor layer height Y1, a second microvaristor layer width X2, and a second microvaristor layer height Y2.

[0085] Step S203: constructing constraint conditions based on boundary thresholds corresponding to the structural parameters to be optimized to obtain preset boundary constraint conditions;

[0086] In the specific implementation process of this step, the constraint condition is constructed based on the maximum boundary threshold corresponding to the structural parameter to be optimized and the minimum boundary threshold corresponding to the structural parameter to be optimized to obtain the preset boundary constraint condition. As shown in Table 3, it is a data table of the maximum boundary threshold and the minimum boundary threshold corresponding to the structural parameter to be optimized:

[0087] Structural parameters to be optimized Minimum Maximum <![CDATA[r1(mnmn)]]> 10 30 <![CDATA[R1(mm)]]> 110 300 <![CDATA[H1(mmn)]]> 50 150 <![CDATA[H2(mm)]]> 50 150 <![CDATA[R s1 (mm)]]> 40 82.5 <![CDATA[R s2 (mm)]]> 30 72.5 <![CDATA[D d (mm)]]> 20 45 <![CDATA[X1(mm)]]> 2 6 <![CDATA[Y1(mm)]]> 60 1500 <![CDATA[X2(mm)]]> 2 6 <![CDATA[Y2(mm)]]> 60 1500

[0088] Table 3.

[0089] Step S204: constructing constraint conditions based on the first shed radius, the second shed radius and the preset shed distance minimum difference in the structural parameters to be optimized, and obtaining preset shed radius constraint conditions;

[0090] In the specific implementation process of this step, the mathematical expression of the preset shed radius constraint condition can be expressed as the following formula (2):

[0091] R s1 ≥R s2 +l (2)

[0092] Among them, R s1 is the radius of the first shed skirt, R s2 is the second shed radius, l is the preset shed distance minimum difference threshold, the preset shed distance minimum difference l can be 10 mm, and the value of the preset shed distance minimum difference l can be set according to actual needs.

[0093] Step S205: Initialize various parameters in the optimization algorithm;

[0094] In the specific implementation process of this step, the optimization algorithm can be a variable step crow search algorithm (VariableStep Crow Search Algorithm, referred to as VSCSA); each of the parameters includes the population size N, the maximum number of iterations Iter max And parameters such as search dimension d.

[0095] Step S206: determining a search boundary value of the preset optimization algorithm based on the constraint condition;

[0096] In the specific implementation process of this step, based on the constraint conditions, the search upper boundary ub of the preset optimization algorithm is determined. i (i=1,…,N; based on the constraint condition, determine the search lower boundary lb of the preset optimization algorithm i (i=1,…,N); the individual i at the iterth iteration in the search space based on the search upper boundary and the search lower boundary is represented by the vector x i,iter (i=1,…,N; iter=1,…,iter max ) represents; vector x i,iter The elements in correspond to the structural parameters to be optimized.

[0097] Step S207: based on the preset parameter values ​​of the parameters and the search boundary value, randomly initializing the initial position of each individual in the population;

[0098] In the specific implementation process of this step, based on each of the parameters and the search boundary, a population is randomly generated within the search boundary value range, and an initial position is randomly matched for each individual in the population.

[0099] Step S208: Based on each of the initial positions, calculating an initial fitness value corresponding to each of the individuals;

[0100] In the specific implementation process of this step, calculation processing is performed based on the initial parameter values ​​corresponding to the structural parameters to be optimized at each initial position to obtain the initial fitness value corresponding to each individual. The initial fitness value is the electric field strength value calculated using the initial parameter value.

[0101] Step S209: screening each of the initial fitness values ​​to obtain an initial target fitness value to obtain an initial optimal individual;

[0102] In the specific implementation process of this step, the initial fitness values ​​are screened to obtain the initial target fitness value. Specifically, the maximum initial fitness value among the initial fitness values ​​is determined as the target fitness value, and the individual corresponding to the target fitness value is determined as the initial optimal individual. The initial position of the initial optimal individual is determined as the initial target position of the hidden food.

[0103] Step S210: determining the parameter value corresponding to the initial position of the initial optimal individual as the initial optimal parameter value;

[0104] During the specific implementation of this step, the parameter value corresponding to the initial position of the initial optimal individual is determined as the initial optimal parameter value; specifically, the parameter value corresponding to the initial position of the initial optimal individual is determined as the initial optimal parameter value corresponding to the structural parameter to be optimized in the initial iteration round.

[0105] Step S211: Based on the initial optimal parameter value, each individual in the population is updated in a loop iteration manner to re-determine the current optimal individual based on the current position of each updated individual, until the number of loop iterations reaches a preset iteration number threshold, and the target parameter value corresponding to the structural parameter to be optimized is obtained.

[0106] This step includes the following steps in the specific implementation process:

[0107] Step 1: Randomly match each individual i with a follower individual j;

[0108] Step 2: Based on the random probability of each individual matching the following individual and the predetermined perception probability, the position of each individual is updated to obtain the current position corresponding to each individual; specifically, when the random probability of the same individual moving to the following individual corresponding to the same individual is greater than the predetermined perception probability, that is, when r j . iter >AP j.iter When the first displacement function is used to determine the current position of the same individual; r j.iter is the random probability, AP j.iter is the predetermined perception probability. The mathematical expression of the first displacement function can be expressed as follows:

[0109] x i,iter+1 =x i，iter +|cos(r i )|×(m j，iter -x i，iter ) (3)

[0110] Among them, x i,iter+1 is the current parameter value of the structural parameter to be optimized corresponding to the current position; x i,iter is the initial parameter value of the structural parameter to be optimized corresponding to the initial position; r i is a random number in the range [0,1]; m j,iter is the initial parameter value of the structural parameter to be optimized corresponding to the initial target position. When the random probability of the same individual moving to the following individual corresponding to the same individual is less than or equal to the predetermined perception probability, the second displacement function is used to randomly determine the current position corresponding to the same individual. That is, when r j.iter ≤AP j.iter When the second displacement function is used to randomly determine the current position corresponding to the same individual, the mathematical expression of the second displacement function can be expressed as follows:

[0111] x i,iter+1 =0.5×(ub i ×rs； +m j，ter ) (4)

[0112] Among them, rs i is a random number in the range [-1,1].

[0113] Step 3: Calculate based on each current position to obtain a current fitness value corresponding to each individual;

[0114] Based on the preset constraint conditions, the current positions are filtered to obtain the current positions that meet the preset constraint conditions, so as to obtain the individuals that meet the preset constraint conditions. Based on the current parameter values ​​corresponding to the structural parameters to be optimized at the current positions, calculation processing is performed to obtain the current fitness values ​​corresponding to the individuals. The current fitness value is the electric field strength value calculated using the current parameter values.

[0115] Step 4: Screening based on each of the current fitness values ​​to obtain a current target fitness value;

[0116] The current fitness values ​​are screened to obtain a current target fitness value. Specifically, the maximum current fitness value among the current fitness values ​​is determined as the current target fitness value, and the individual corresponding to the current target fitness value is determined as the current optimal individual.

[0117] Step 5: determining the current optimal individual position of the current iteration round based on the current target fitness value and the initial target fitness value to obtain the current optimal parameter value corresponding to the structural parameter to be optimized;

[0118] Specifically, based on the comparison between the current target fitness value and the initial target fitness value, a comparison result is obtained; when the comparison result is that the current target fitness value is greater than or equal to the initial target fitness value, the initial target fitness value is determined as the optimal position of the hidden object in the current iteration round; the initial structural parameter value corresponding to the structural parameter to be optimized corresponding to the position of the initial target fitness value is determined as the current optimal parameter value. When the comparison result is that the current target fitness value is less than the initial target fitness value, the current position corresponding to the current target fitness value is determined as the optimal position of the hidden object in the current iteration round; the current structural parameter value corresponding to the structural parameter to be optimized corresponding to the position of the current target fitness value is determined as the current optimal parameter value.

[0119] Step 6: Determine whether the current iteration round is equal to the preset round threshold and obtain a determination result;

[0120] Step 7: When the current iteration round is less than the preset round threshold, repeat steps 1 to 6 until the current iteration round is equal to the preset round threshold, and then determine the updated current optimal parameter value as the target parameter value corresponding to the structural parameter to be optimized.

[0121] Step 8: When the current iteration round is equal to the preset round threshold, the current optimal parameter value is determined as the target parameter value corresponding to the structural parameter to be optimized. For example, the target parameter value corresponding to the structural parameter to be optimized determined by the method of the present application is shown in Table 4:

[0122] Structural parameters to be optimized Target parameter value <![CDATA[r1(mm)]]> 26.29 <![CDATA[R1(mm)]]> 140.63 <![CDATA[H1(mnm)]]> 133.07 <![CDATA[H2(mm)]]> 132.99 <![CDATA[R s1 (mm)]]> 80.95 <![CDATA[R s2 (mm)]]> 59.68 D(mm) 37.55 <![CDATA[X1(mm)]]> 4.033 <![CDATA[Y1(mm)]]> 1190.7 <![CDATA[X2(mm)]]> 4.017 <![CDATA[Y2(mm)]]> 1234.4

[0123] Table 4

[0124] like Figure 6 The figure shows the distribution diagram of the electric field intensity on the surface of the insulator before and after optimization; the variable step-length crow search algorithm of the present application significantly reduces the distribution of the electric field intensity on the surface of the composite insulator and improves the electrical performance of the composite insulator. Figure 7 The figure shows the distribution diagram of the electric field intensity along the surface of the insulator in two cases after optimization, with and without the micro-varistor layer. In the present application, the introduction of the micro-varistor layer under the condition of double equalizing rings can further reduce the maximum electric field intensity and make the electric field more uniform.

[0125] The present application constructs a simulation model based on a preset physical structure of an insulator to be determined by parameters, thereby obtaining a two-dimensional axisymmetric simulation model of the insulator; constructs a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, thereby obtaining an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized; constructs constraint conditions based on boundary thresholds corresponding to the structural parameters to be optimized, thereby obtaining preset boundary constraint conditions; constructs constraint conditions based on a first shed radius, a second shed radius, and a preset shed distance minimum difference in the structural parameters to be optimized, thereby obtaining a preset shed radius constraint condition; initializes each parameter in the optimization algorithm; Parameters: Based on the preset parameter values ​​of each parameter and the search boundary value, the initial position of each individual in the population is randomly initialized; based on each initial position, the initial fitness value corresponding to each individual is calculated; each initial fitness value is screened to obtain an initial target fitness value to obtain an initial optimal individual; the parameter value corresponding to the initial position of the initial optimal individual is determined as the initial optimal parameter value; based on the initial optimal parameter value, each individual in the population is updated in a cyclic iteration manner to re-determine the current optimal individual based on the current position of each individual after the update, until the number of cyclic iterations reaches the preset iteration number threshold, and the target parameter value corresponding to the structural parameter to be optimized is obtained. The present application also considers the use of double grading rings and zinc oxide microvaristor layers to design composite insulators, and uses a variable step-size crow search algorithm to optimize the structural parameters of the composite insulator to obtain the optimal size of each structural parameter of the composite insulator, which significantly reduces the maximum electric field strength of the composite insulator, improves the performance of the composite insulator, and has stronger applicability.

[0126] Another embodiment of the present application provides an insulator parameter determination device, such as Figure 5 As shown, including:

[0127] A simulation model building module 1 is used to build a simulation model based on a preset physical structure of the insulator to be determined by parameters, so as to obtain a two-dimensional axisymmetric simulation model of the insulator;

[0128] Determination module 2, for constructing a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in the predetermined area of ​​the insulator, to obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0129] A constraint condition building module 3, used for building constraint conditions;

[0130] The optimization module 4 is used to optimize the objective function based on the constraint conditions by using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0131] In the specific implementation process, the simulation model construction module 1 is specifically used to: based on the preset physical structure, obtain the initial structural parameters, the number of sheds and the predetermined electrical parameters of the insulator; based on the initial structural parameters, the number of sheds and the predetermined electrical parameters, use a preset finite element analysis method to construct a simulation model to obtain a two-dimensional axisymmetric simulation model of the insulator.

[0132] During the specific implementation process, the determination module 2 is specifically used for: the structural parameters to be optimized include: the radius of the equalizing ring tube, the radius of the equalizing ring, the first equalizing ring height, the second equalizing ring height, the first umbrella skirt radius, the second umbrella skirt radius, the umbrella skirt spacing, the first microvaristor layer width, the first microvaristor layer height, the second microvaristor layer width and the second microvaristor layer height.

[0133] In the specific implementation process, the constraint condition construction module 3 is specifically used to: construct constraint conditions based on the boundary threshold corresponding to the structural parameter to be optimized to obtain preset boundary constraint conditions; construct constraint conditions based on the first umbrella skirt radius, the second umbrella skirt radius and the preset umbrella skirt distance minimum difference in the structural parameter to be optimized to obtain preset umbrella skirt radius constraint conditions.

[0134] In the specific implementation process, the optimization module 4 is specifically used to: initialize each parameter in the optimization algorithm; determine the search boundary value of the preset optimization algorithm based on the constraint conditions; randomly initialize the initial position of each individual in the population based on the preset parameter value of each parameter and the search boundary value; based on each initial position, calculate the initial fitness value corresponding to each individual; screen each initial fitness value to obtain an initial target fitness value to obtain an initial optimal individual; determine the parameter value corresponding to the initial position of the initial optimal individual as the initial optimal parameter value; based on the initial optimal parameter value, use a loop iteration method to update each individual in the population to re-determine the current optimal individual based on the current position of each individual after the update, until the number of loop iterations reaches a preset iteration number threshold, and the target parameter value corresponding to the structural parameter to be optimized is obtained.

[0135] In the specific implementation process, the optimization module 4 is also used for: step 1, randomly matching a follower individual for each individual; step 2, updating the position of each individual based on the random probability of each individual matching the follower individual and the predetermined perception probability, and obtaining the current position corresponding to each individual; step 3, calculating based on each current position to obtain the current fitness value corresponding to each individual; step 4, screening based on each current fitness value to obtain the current target fitness value; step 5, determining the current optimal individual position of the current iteration round based on the current target fitness value and the initial target fitness value to obtain the current optimal parameter value corresponding to the structural parameter to be optimized; step 6, judging whether the current iteration round is equal to the preset round threshold, and obtaining the judgment result; step 7, when the current iteration round is less than the preset round threshold, repeating steps 1 to 6 until the current iteration round is equal to the preset round threshold, and the updated current optimal parameter value is determined as the target parameter value corresponding to the structural parameter to be optimized. Step eight: when the current iteration round is equal to the preset round threshold, the current optimal parameter value is determined as the target parameter value corresponding to the structural parameter to be optimized.

[0136] During the specific implementation process, the optimization module 4 is also used to: when the random probability of the same individual moving towards the following individual corresponding to the same individual is greater than the predetermined perception probability, use the first displacement function to determine the current position of the same individual; when the random probability of the same individual moving towards the following individual corresponding to the same individual is less than or equal to the predetermined perception probability, use the second displacement function to randomly determine the current position corresponding to the same individual.

[0137] This application constructs a simulation model based on the preset physical structure of the insulator to be determined by parameters, and obtains a two-dimensional axisymmetric simulation model of the insulator; based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field strength in the predetermined area of ​​the insulator, and an objective function for determining the structural parameters of the insulator is obtained, and the objective function includes the structural parameters to be optimized; constructs constraints; based on the constraints, the objective function is optimized using a preset optimization algorithm to obtain the target parameter value corresponding to the structural parameters to be optimized of the insulator. This application simultaneously considers the dual grading ring and the zinc oxide microvaristor layer and optimizes the objective function using a preset optimization algorithm. The designed composite insulator significantly reduces the electric field strength on the surface of the shed, greatly improves the electrical performance of the composite insulator, and has stronger applicability.

[0138] Another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following method steps are implemented:

[0139] Step 1: constructing a simulation model based on a preset physical structure of the insulator to be determined by parameters, and obtaining a two-dimensional axisymmetric simulation model of the insulator;

[0140] Step 2: constructing a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, and obtaining an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0141] Step 3: Construct constraints;

[0142] Step 4: Based on the constraint conditions, a preset optimization algorithm is used to optimize the objective function to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0143] The specific implementation process of the above method steps can refer to the embodiments of any of the above insulator parameter determination methods, which will not be repeated in this embodiment.

[0144] This application constructs a simulation model based on the preset physical structure of the insulator to be determined by parameters, and obtains a two-dimensional axisymmetric simulation model of the insulator; based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field strength in the predetermined area of ​​the insulator, and an objective function for determining the structural parameters of the insulator is obtained, and the objective function includes the structural parameters to be optimized; constructs constraints; based on the constraints, the objective function is optimized using a preset optimization algorithm to obtain the target parameter value corresponding to the structural parameters to be optimized of the insulator. This application simultaneously considers the dual grading ring and the zinc oxide microvaristor layer and optimizes the objective function using a preset optimization algorithm. The designed composite insulator significantly reduces the electric field strength on the surface of the shed, greatly improves the electrical performance of the composite insulator, and has stronger applicability.

[0145] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0146] Step 1: constructing a simulation model based on a preset physical structure of the insulator to be determined by parameters, and obtaining a two-dimensional axisymmetric simulation model of the insulator;

[0147] Step 2: constructing a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, and obtaining an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized;

[0148] Step 3: Construct constraints;

[0149] Step 4: Based on the constraint conditions, a preset optimization algorithm is used to optimize the objective function to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

[0150] The specific implementation process of the above method steps can refer to the embodiments of any of the above insulator parameter determination methods, which will not be repeated in this embodiment.

[0151] This application constructs a simulation model based on the preset physical structure of the insulator to be determined by parameters, and obtains a two-dimensional axisymmetric simulation model of the insulator; based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field strength in the predetermined area of ​​the insulator, and an objective function for determining the structural parameters of the insulator is obtained, and the objective function includes the structural parameters to be optimized; constructs constraints; based on the constraints, the objective function is optimized using a preset optimization algorithm to obtain the target parameter value corresponding to the structural parameters to be optimized of the insulator. This application simultaneously considers the dual grading ring and the zinc oxide microvaristor layer and optimizes the objective function using a preset optimization algorithm. The designed composite insulator significantly reduces the electric field strength on the surface of the shed, greatly improves the electrical performance of the composite insulator, and has stronger applicability.

[0152] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for determining insulator parameters, characterized in that: include: A simulation model is constructed based on a preset physical structure of the insulator to be determined by parameters, so as to obtain a two-dimensional axisymmetric simulation model of the insulator; Based on the two-dimensional axisymmetric simulation model, a function is constructed with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, to obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized; Construct constraints; Based on the constraint conditions, the objective function is optimized using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

2. The method according to claim 1, characterized in that The simulation model is constructed based on the preset physical structure of the insulator to be determined by the parameters to obtain a two-dimensional axisymmetric simulation model of the insulator, specifically comprising: Based on the preset physical structure, obtaining initial structural parameters, the number of sheds and predetermined electrical parameters of the insulator; A simulation model is constructed based on the initial structural parameters, the number of sheds and the predetermined electrical parameters using a preset finite element analysis method to obtain a two-dimensional axisymmetric simulation model of the insulator.

3. The method according to claim 1, characterized in that The structural parameters to be optimized include: the radius of the equalizing ring tube, the radius of the equalizing ring, the first equalizing ring height, the second equalizing ring height, the first umbrella skirt radius, the second umbrella skirt radius, the umbrella skirt spacing, the first microvaristor layer width, the first microvaristor layer height, the second microvaristor layer width and the second microvaristor layer height.

4. The method according to claim 1, characterized in that The construction constraints specifically include: Constructing constraint conditions based on boundary thresholds corresponding to the structural parameters to be optimized to obtain preset boundary constraint conditions; Constraint conditions are constructed based on the first shed radius, the second shed radius and the preset shed distance minimum difference in the structural parameters to be optimized to obtain the preset shed radius constraint conditions.

5. The method according to claim 1, characterized in that The optimizing the objective function based on the constraint condition by using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator specifically includes: Initialize the parameters in the optimization algorithm; Determining a search boundary value of the preset optimization algorithm based on the constraint condition; Based on the preset parameter values ​​of the parameters and the search boundary value, randomly initializing the initial position of each individual in the population; Based on each of the initial positions, calculating an initial fitness value corresponding to each of the individuals; Screening each of the initial fitness values ​​to obtain an initial target fitness value to obtain an initial optimal individual; Determining the parameter value corresponding to the initial position of the initial optimal individual as the initial optimal parameter value; Based on the initial optimal parameter value, each individual in the population is updated in a loop iteration manner to re-determine the current optimal individual based on the current position of each updated individual, until the number of loop iterations reaches a preset iteration number threshold, thereby obtaining the target parameter value corresponding to the structural parameter to be optimized.

6. The method according to claim 5, characterized in that The method of updating each individual in the population based on the initial optimal parameter value in a loop iteration manner, so as to re-determine the current optimal individual based on the current position of each individual after the update, until the number of loop iterations reaches a preset iteration number threshold, and obtaining the target parameter value corresponding to the structural parameter to be optimized, specifically includes: Step 1, randomly matching a follower individual for each individual; Step 2: Based on the random probability of each individual matching the following individual and the predetermined perception probability, the position of each individual is updated to obtain the current position corresponding to each individual; Step 3: Calculate based on each current position to obtain a current fitness value corresponding to each individual; Step 4: Screening based on each of the current fitness values ​​to obtain a current target fitness value; Step 5: determining the current optimal individual position of the current iteration round based on the current target fitness value and the initial target fitness value to obtain the current optimal parameter value corresponding to the structural parameter to be optimized; Step 6: Determine whether the current iteration round is equal to the preset round threshold and obtain a determination result; Step 7: When the current iteration round is less than the preset round threshold, repeat steps 1 to 6 until the current iteration round is equal to the preset round threshold, and then determine the updated current optimal parameter value as the target parameter value corresponding to the structural parameter to be optimized. Step eight: when the current iteration round is equal to the preset round threshold, the current optimal parameter value is determined as the target parameter value corresponding to the structural parameter to be optimized.

7. The method according to claim 6, characterized in that The updating of the position of each individual based on the random probability of each individual matching the following individual and the predetermined perception probability to obtain the current position corresponding to each individual specifically includes: When the random probability that the same individual moves toward the following individual corresponding to the same individual is greater than the predetermined perception probability, the current position of the same individual is determined using the first displacement function; When the random probability that the same individual moves toward the following individual corresponding to the same individual is less than or equal to the predetermined perception probability, the current position corresponding to the same individual is randomly determined using a second displacement function.

8. An insulator parameter determination device, characterized in that: include: A simulation model building module, used to build a simulation model based on a preset physical structure of the insulator to be determined by parameters, so as to obtain a two-dimensional axisymmetric simulation model of the insulator; A determination module, configured to construct a function based on the two-dimensional axisymmetric simulation model with the goal of minimizing the maximum electric field intensity in a predetermined area of ​​the insulator, and obtain an objective function for determining the structural parameters of the insulator, wherein the objective function includes the structural parameters to be optimized; Constraint building module, used to build constraints; The optimization module is used to optimize the objective function based on the constraint conditions by using a preset optimization algorithm to obtain a target parameter value corresponding to the structural parameter to be optimized of the insulator.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the insulator parameter determination method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The device at least comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the insulator parameter determination method according to any one of claims 1 to 7 when executing the computer program on the memory.

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