Transformer insulation structure optimization method and system based on multi-physics field coupling

Abstract

The invention provides a transformer insulation structure optimization method and system based on multi-physics field coupling, and the method comprises the steps: building a transformer finite element simulation model according to the structure and electrical parameters of a transformer; according to an electromagnetic field-temperature field-fluid field coupling relation, an electromagnetic heat flow multi-physical field coupling model is established on the basis of the transformer finite element simulation model; taking the maximum electric field intensity as an optimization target, performing a simulation test by using an electromagnetic heat flow multi-physics field coupling model, determining structural parameters of which the influence on the electric field intensity is greater than a set value, and setting a change interval of the corresponding structural parameters according to size and structural limitation; constructing a response surface function based on the radial basis function; and solving an optimal solution of the response surface function by using a genetic algorithm in the change interval of each structure parameter to obtain a globally optimal structure parameter variable and the maximum electric field intensity. According to the method, the optimization effect can be improved, and the calculation amount and the test frequency are effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of transformer structure optimization, and particularly relates to a method and system for optimizing the insulation structure of a transformer based on multi-physical field coupling. Background Art

[0002] The statements in this part merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] As one of the most critical devices in the power system, the operating state of a transformer directly affects the safety and stability of power supply. The insulation structure of a transformer determines the distribution of the electric field, but is also affected by the temperature field and the flow field. The temperature field is also affected by the electric field and the flow field. Therefore, in the actual operating environment, the transformer is subjected to the coupling action of multiple physical fields of electromagnetic heat flow, and they are coupled and interact with each other in the device operation space-time, forming an inseparable multi-physical coupling field. When optimizing the design of the transformer structure, it is necessary to consider the mutual influence between the fields, especially the influence of the temperature field and the flow field on the electric field.

[0004] As a mathematical and statistical method for optimization, the response surface method can make the implicit objective function explicit when dealing with optimization problems, and is widely used because the establishment of the approximate model is independent. When establishing a response surface model, there are many types of response surface functions to choose from, but traditional response surface models usually use quadratic polynomial expressions with cross terms, making the model relatively simplified and reducing the optimization accuracy.

[0005] However, as the inventor understands, when using the response surface optimization method currently, it is generally directly applied and has certain disadvantages. For example, in Chinese invention patent CN 116151068 A, "A Method for Optimizing the Insulation Structure of a Multi-Winding High-Frequency Transformer", a method combining sensitivity analysis and response surface analysis is used to optimize the insulation structure of a high-frequency transformer. After selecting the optimal structure parameters by sensitivity analysis, the response surface analysis is directly carried out, which is difficult to ensure the optimization accuracy, and a traditional quadratic polynomial expression with cross terms is used for modeling during the response surface analysis, resulting in relatively low efficiency. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a method and system for optimizing the insulation structure of a transformer based on multi-physical field coupling. The present invention can improve the optimization effect and effectively reduce the calculation amount and the number of tests.

[0007] According to some embodiments, the present invention adopts the following technical solutions:

[0008] A method for optimizing the insulation structure of a transformer based on multi-physical field coupling, comprising the following steps:

[0009] Construct a finite element simulation model of the transformer according to the structure and electrical parameters of the transformer;

[0010] Based on the finite element simulation model of the transformer, establish a multi-physics coupling model of electromagnetic-thermal-fluid according to the coupling relationship between the electromagnetic field, temperature field and fluid field;

[0011] Taking the maximum electric field strength as the optimization goal, use the multi-physics coupling model of electromagnetic-thermal-fluid to conduct simulation tests, determine the structural parameters whose influence on the electric field strength is greater than the set value, and set the change range of the corresponding structural parameters according to the size and structure limitations;

[0012] Construct a response surface function based on the radial basis function;

[0013] Within the change range of each structural parameter, use the genetic algorithm to obtain the optimal solution of the response surface function, and obtain the globally optimal structural parameter variables and the maximum electric field strength.

[0014] As an alternative implementation, the specific process of constructing the finite element simulation model of the transformer includes: establishing a finite element simulation model according to the structure and electrical parameters of the transformer, as well as the characteristic parameters of the iron core, winding and transformer oil.

[0015] As an alternative implementation, when establishing the multi-physics coupling model of electromagnetic-thermal-fluid, consider the influence of temperature change on material properties, the influence of temperature gradient on the convection field, the influence of Joule heat loss of materials under electric field on the temperature field, and then on the fluid field, and the influence of transformer oil flow on temperature, and then on the electric field.

[0016] As an alternative implementation, the boundary condition setting for establishing the multi-physics coupling model of electromagnetic-thermal-fluid includes setting the momentum boundary condition as a no-slip boundary condition, and only considering convective heat transfer between the transformer and the outside air.

[0017] As an alternative implementation, select the curvature radius of the static ring, the distance from the static ring to the pressing plate, the thickness of the static ring insulation layer, the length of the upper flat plate of the static ring, the distance from the angle ring to the winding end, and the thickness of the angle ring as the structural parameters to be determined, determine their change ranges, use the multi-physics coupling model of electromagnetic-thermal-fluid to conduct simulation experiments, change the values of the structural parameters to be determined, and determine their influence on the electric field strength.

[0018] As an alternative implementation, the specific process of constructing the response surface function based on the radial basis function includes: the response surface function is:

[0019]

[0020] where the basis function is composed of N radial functions H(||X - X i ||) associated with the sample points in the function space, and cj is the weight coefficient;

[0021] Adopt multi-quadratic function as the radial basis function, where r is the Euclidean norm; d i is the distance between the sample point at the i-th iteration and its nearest sample point, and the sample point is the value taken by the determined structural parameter.

[0022] As an alternative implementation, the determined structural parameters include the radius of curvature of the electrostatic ring, the distance from the electrostatic ring to the pressure plate, the thickness of the electrostatic ring insulation layer, and the length of the upper flat plate of the electrostatic ring.

[0023] As an alternative implementation, during the solution process, in each iteration, the optimal point of the response surface function obtained based on the genetic algorithm before is used as the new sample point, and together with the previous initial sample points, a new response surface function is constructed again. Within the variation range of each structural parameter, the minimum value of the new response surface is obtained by using the genetic algorithm, that is, the optimal point of this iteration. The newly generated optimal points are successively added to the sample points of the previous iteration, the response surface function is reconstructed, and the minimum value is solved again.

[0024] As a further implementation, during the solution process, the convergence condition of the iteration is set as the change amount of each optimization parameter not exceeding the preset threshold. After meeting the convergence condition, the iteration is completed, and the optimal solution at this time is the global optimal solution.

[0025] A transformer insulation structure optimization system based on multi-physical field coupling, comprising:

[0026] A simulation model construction module, configured to construct a finite element simulation model of the transformer according to the structure and electrical parameters of the transformer;

[0027] Based on the electromagnetic field-temperature field-fluid field coupling relationship, on the basis of the transformer finite element simulation model, an electromagnetic-thermal-fluid multi-physical field coupling model is established;

[0028] Taking the maximum electric field strength as the optimization goal, using the electromagnetic-thermal-fluid multi-physical field coupling model for simulation tests, determining the structural parameters that have an impact on the electric field strength greater than the set value, and setting the change range of the corresponding structural parameters according to the size and structure limitations;

[0029] Based on the radial basis function, construct a response surface function;

[0030] Within the change range of each structural parameter, use the genetic algorithm to obtain the optimal solution of the response surface function, and obtain the globally optimal structural parameter variables and the maximum electric field strength.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] (1) The optimal design method for the transformer insulation structure provided by the present invention effectively improves the problem in the existing method of separately optimizing each key variable and insufficient consideration except for the electric field. While ensuring the optimization efficiency, the coupled influence of each field is considered.

[0033] (2) When the present invention applies the response surface method to optimize the transformer structure, the structural parameters of the static ring and the structural parameters of the angle ring are considered simultaneously, and a response surface function is established based on the radial basis function, which improves the optimization accuracy and ensures the optimization effect.

[0034] (3) In the present invention, the genetic algorithm is used to iteratively solve the response surface function. Although it has no advantage in the optimization speed, it can effectively improve the optimization accuracy and can well solve the structural optimization problems of the transformer static ring and angle ring, etc.

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0036] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0037] Figure 1 It is a flow chart of the optimal design method for the transformer insulation structure based on multi-physical field coupling and the response surface method;

[0038] Figure 2 It is a schematic diagram of the transformer structure;

[0039] Figure 3 It is a flow chart of using the genetic algorithm to solve the response surface function;

[0040] Figure 4 It is the electric field distribution diagram after the transformer is optimized. Detailed Embodiments

[0041] The present invention will be further described below in conjunction with the drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are all illustrative and are intended to provide a further description of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] Example 1

[0045] Transformer insulation structure optimization method based on multi-physics field coupling and response surface method, such as Figure 1 As shown, the following steps are included:

[0046] Step 1: Construct a finite element simulation model of the transformer based on the structure and electrical parameters of the transformer;

[0047] Step 2: Based on the electromagnetic field-temperature field-fluid field coupling relationship and the transformer finite element simulation model, an electromagnetic heat flow multi-physics field coupling model is established;

[0048] Step 3: Taking the maximum electric field intensity as the optimization goal, a simulation test is conducted using the electromagnetic heat flow multi-physics field coupling model to determine the structural parameters whose impact on the electric field intensity is greater than the set value, and the variation range of the corresponding structural parameters is set according to the size and structural limitations;

[0049] Step 4: Based on the radial basis function, construct the response surface function;

[0050] Step 5: Within the variation range of each structural parameter, the optimal solution of the response surface function is obtained by using a genetic algorithm to obtain the global optimal structural parameter variables and the maximum electric field intensity.

[0051] In this embodiment, in step 1, a finite element simulation model of the transformer is preliminarily constructed based on the structural and electrical parameters of the transformer electrostatic ring and corner ring, specifically including:

[0052] In COMSOL simulation software, a finite element simulation model of the transformer is initially established based on the structure and electrical parameters of the transformer and other characteristic parameters of the core, winding, transformer oil, etc. The established finite element simulation model is as follows: Figure 2 Some parameters are shown in Table 1.

[0053] Table 1 Part of the parameters of the input simulation

[0054]

[0055] In this embodiment, step 2 considers the coupling relationship between the electromagnetic field, temperature field, and fluid field, and loads this coupling relationship into the finite element model from step 1 to establish an electromagnetic heat flow multi-physics coupling model. This model takes into account the effects of temperature changes on material properties, the impact of temperature gradients on the flow field, the influence of Joule heat loss in materials under an electric field on the temperature field, and thus on the fluid field, and the influence of temperature changes caused by transformer oil flow on the electric field. Through coupling, meshing, heterogeneous mesh mapping, and boundary condition setting, a pre-optimized transformer electromagnetic heat flow multi-field coupling simulation model is established to obtain the pre-optimized transformer multi-physics field.

[0056] Different physical fields of electromagnetic heat flow have different requirements for meshing that affect the calculation. Even for the same physical field, the requirements for meshing are different at different locations. For example, the electric field requires a higher mesh density than the temperature field. In addition, only refining the mesh in places with large gradients of physical quantities can effectively reduce the amount of calculation. For example, the electric field uses a triangular mesh at the electrostatic ring position, while reducing the meshing density inside the winding and oil flow channel.

[0057] Since the meshing conditions between different physical fields are not consistent, direct data mapping between different physical field meshes cannot be achieved. For example, the energy loss calculated in the electromagnetic field needs to be transferred to the temperature field through the mesh, which requires the use of heterogeneous mesh mapping methods. In this embodiment, in order to reduce calculation time and improve efficiency, a fast mapping method is used to achieve data transfer between heterogeneous meshes. The interpolation function is as follows:

[0058] A sD =N i A fi +N j A fj +N m A fm

[0059] Among them, A sD Substitute the nodal physical quantity in the unit, N i 、N j 、N m is the shape function of the mesh element with i, j, and m as nodes, A fi , A fj , A fm is the physical quantity value of each node of the triangle unit of the known physical field.

[0060] In the boundary condition settings, the momentum boundary condition is set to a no-slip boundary condition:

[0061] U fs =0

[0062] Wherein, subscript f refers to fluid and s refers to solid.

[0063] The heat exchange between the transformer and the outside air is only considered through convection:

[0064]

[0065] Where k s is the thermal conductivity of the solid; T s is the solid thermodynamic temperature; k f is the thermal conductivity of the fluid; T f is the solid-fluid thermodynamic temperature.

[0066] In this embodiment, in step 3, the experimental design is performed with the maximum electric field intensity as the optimization target, the structural parameters that have a greater impact on the electric field intensity are selected, and the variation range is set according to the size and structural limitations, specifically including:

[0067] The initial optimization parameters were selected: the curvature radius R of the electrostatic ring, the distance h from the electrostatic ring to the pressure plate, the thickness s1 of the electrostatic ring insulation layer, the length l of the upper plate of the electrostatic ring, the distance d from the corner ring to the winding end, and the thickness s2 of the corner ring. Their variation ranges were determined based on the spatial structure of the transformer to prevent the occurrence of unreasonable structures that would result in an inability to simulate the electric field. During the optimization process, the optimized parameters were loaded into the transformer electromagnetic heat flow multi-field coupling simulation model by setting the parameter sizes for further analysis.

[0068] After determination, it was found that the four structural parameters of the electrostatic ring curvature radius, the distance from the electrostatic ring to the pressure plate, the thickness of the electrostatic ring insulation layer, and the length of the electrostatic ring upper plate had a significant impact on the maximum field strength, while the distance from the corner ring to the winding end and the thickness of the corner ring had relatively little impact on the maximum field strength. These four structural parameters were selected as the structural parameters to be optimized: the electrostatic ring curvature radius, the distance from the electrostatic ring to the pressure plate, the thickness of the electrostatic ring insulation layer, and the length of the electrostatic ring upper plate.

[0069] In this embodiment, if Figure 3 As shown, in step 4, when performing optimization design based on a response surface, a response surface function needs to be constructed. The radial basis function can achieve efficient data fitting by mapping the input space to the output space. Its construction algorithm is simple and easy to implement on a computer. It performs well in high-dimensional nonlinear systems and has the advantages of fast training speed and the ability to quickly process large amounts of data. Therefore, in step 4, the radial basis function is used to construct the response surface model.

[0070] for The response surface function expression based on the radial basis function is:

[0071]

[0072] Where f is a function, R refers to any set of real numbers, and the basis function is the radial function H(||XX) associated with N sample points in the function space and X. i||), c j is the weight coefficient.

[0073] The multiquadric function is used as the radial basis function, where r is the Euclidean norm; d i is the distance between the sample point at the i-th iteration and its nearest sample point.

[0074] In step 5, when obtaining the optimal solution of the aforementioned response surface model, the genetic algorithm is selected. The genetic algorithm simulates the biological evolution process in nature. All possible solutions are regarded as individual organisms in nature. In the solution set, relatively non-optimal solutions are eliminated through simulating the genetic selection process, and finally the optimal solution under the limited conditions is obtained. That is, in the optimization process, first, an objective fitness function is defined (the objective fitness function in this embodiment is the response surface function established in step four). This function is used to evaluate the performance of each individual. Then, according to this fitness function, each individual in the problem set is carefully evaluated to determine whether it meets the preset optimization goal. Following the evolutionary rules of "survival of the fittest" and "survival of the superior and elimination of the inferior", those individuals with better performance are continuously selected. These individuals will form an evolving population, and this population will gradually tend to be better. In this embodiment, reducing the maximum field strength is used as the optimization goal, and the established response surface function is used as the objective fitness function. The maximum field strength is taken as the individual fitness, and the individuals that do not meet the optimization goal are removed, so that the individual fitness can reflect the electric field optimization situation. In the embodiment, three methods (existing methods can be selected) are used to generate new population individuals. According to the individual fitness, some individuals are selected and retained in the new population by finding the optimal ones. Two individuals are randomly selected for crossover and inserted into the new population. One individual is randomly selected for mutation and inserted into the new population. Then, two random numbers are used to form a proportionality factor to control the proportion, but ensure that the total number of individuals remains unchanged. To further improve the search efficiency, a global parallel search method is adopted. This means that in the entire problem set, multiple individuals will perform search and optimization simultaneously. This parallel search method can not only improve the search speed, but also ensure a greater possibility of finding the optimal solution in the entire population. In this way, continuous optimization of the entire population can be achieved, and the optimal solution can be gradually approached.

[0075] The optimal point of the response surface function obtained based on the genetic algorithm is used as a new sample point. Together with the previous initial sample points, a new response surface function is constructed again. Within the variation range of the determined parameters, the above operation of using the genetic algorithm to solve the optimal point is repeated continuously to obtain the minimum value of the new response surface, that is, the optimal point of this iteration. The newly generated optimal points are successively added to the sample points of the previous iteration, the response surface function is reconstructed, and the minimum value is solved again.

[0076] The initial sample points can be selected as the set values of the structural parameters commonly used for this type of transformer. Alternatively, based on the experimental results, the initial set values of each structural parameter can be determined.

[0077] In this embodiment, the set convergence condition is that the change amount of each optimization parameter does not exceed 0.1 mm. After meeting the convergence condition, the iteration is completed, and the optimal solution at this time is the global optimal solution.

[0078] In other embodiments, the selection and set values of the above parameters can be adjusted according to specific circumstances.

[0079] The optimized electric field distribution is as Figure 4 shown. It can be found that the optimized field strength distribution is significantly improved, and the field strength is reduced, which greatly reduces potential electric field strength problems. It should be noted that although the maximum field strength still appears at the two corners above the primary side static ring, its value has been significantly reduced. This improvement effectively suppresses the phenomenon of excessive local field strength, thus avoiding potential problems of equipment damage or performance degradation. The verification results prove the effectiveness of the optimized structural parameters using this optimization method in improving the electric field distribution of the transformer, providing strong support for improving the stability and lifespan of the equipment.

[0080] Embodiment 2

[0081] A transformer insulation structure optimization system based on multi-physical field coupling, comprising:

[0082] A simulation model construction module configured to construct a finite element simulation model of the transformer according to the structural and electrical parameters of the transformer;

[0083] Based on the electromagnetic field - temperature field - fluid field coupling relationship, an electromagnetic-thermal-fluid multi-physical field coupling model is established on the basis of the transformer finite element simulation model;

[0084] Taking the maximum electric field strength as the optimization target, using the electromagnetic-thermal-fluid multi-physical field coupling model for simulation tests, determining the structural parameters whose influence on the electric field strength is greater than the set value, and setting the change range of the corresponding structural parameters according to size and structural limitations;

[0085] Construct a response surface function based on radial basis functions;

[0086] Within the change range of each structural parameter, use the genetic algorithm to obtain the optimal solution of the response surface function, and obtain the globally optimal structural parameter variables and the maximum electric field strength.

[0087] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0088] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0091] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative efforts within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization method for the insulation structure of a transformer based on multi-physical field coupling, characterized in that, Including the following steps: Construct a finite element simulation model of the transformer according to the structure and electrical parameters of the transformer; Based on the coupling relationship of electromagnetic field - temperature field - fluid field, establish an electromagnetic - thermal - fluid multi - physical - field coupling model on the basis of the transformer finite element simulation model; Taking the maximum electric field strength as the optimization objective, use the electromagnetic - thermal - fluid multi - physical - field coupling model to conduct simulation tests, determine the structural parameters whose influence on the electric field strength is greater than the set value, and set the change range of the corresponding structural parameters according to size and structure limitations; Construct a response surface function based on the radial basis function; Within the change range of each structural parameter, use the genetic algorithm to obtain the optimal solution of the response surface function, and obtain the globally optimal structural parameter variables and the maximum electric field strength.

2. The optimization method of the transformer insulation structure based on multi-physical field coupling according to claim 1, characterized in that The specific process of constructing the transformer finite element simulation model includes: establishing a finite element simulation model according to the structure and electrical parameters of the transformer, as well as the characteristic parameters of the iron core, winding, and transformer oil.

3. A method for optimizing the insulation structure of a transformer based on multi-physical field coupling as described in claim 1, characterized in that, When establishing the electromagnetic - thermal - fluid multi - physical - field coupling model, consider the influence of temperature change on material properties, the influence of temperature gradient on the flow field, the influence of Joule heat loss of the material under the electric field on the temperature field and then on the fluid field, and the influence of the flow of transformer oil on temperature and then on the electric field.

4. The optimization method for the transformer insulation structure based on multi-physical field coupling according to claim 1 is characterized in that a The boundary condition setting of the electromagnetic - thermal - fluid multi - physical - field coupling model includes setting the momentum boundary condition as the no - slip boundary condition, and only considering convective heat transfer between the transformer and the outside air.

5. The optimization method for the transformer insulation structure based on multi-physical field coupling according to claim 1, characterized in that Select the curvature radius of the static ring, the distance from the static ring to the pressure plate, the thickness of the static ring insulation layer, the length of the upper - segment flat plate of the static ring, the distance from the angle ring to the winding end, and the thickness of the angle ring as the structural parameters to be determined, determine their change ranges, use the electromagnetic - thermal - fluid multi - physical - field coupling model to conduct simulation experiments, change the values of the structural parameters to be determined, and determine their influence on the electric field strength.

6. The optimization method for the transformer insulation structure based on multi-physical field coupling according to claim 1, wherein, The specific process of constructing the response surface function based on the radial basis function includes: the response surface function is: Among them, the basis function is composed of N radial functions H(||X - X i ||) associated with the sample points in the function space and X, and c j is the weight coefficient; Using multi-quadratic functions as the radial basis function, where r is the Euclidean norm; d i is the distance between the sample point at the i-th iteration and its nearest sample point, and the sample point is the value taken by the determined structural parameter.

7. The optimization method of a transformer insulation structure based on multi-physical-field coupling according to claim 1, characterized in that The determined structural parameters include the curvature radius of the static ring, the distance from the static ring to the pressure plate, the thickness of the static ring insulation layer, and the length of the upper - segment flat plate of the static ring.

8. The optimization method for the transformer insulation structure based on multi-physical-field coupling according to claim 1, wherein, During the solution, in each iteration process, use the previously obtained optimal point of the response surface function based on the genetic algorithm as a new sample point, and together with the previous initial sample points, construct a new response surface function again. Within the change range of each structural parameter, use the genetic algorithm to obtain the minimum value of the new response surface, that is, the optimal point of this iteration. Successively add the newly generated optimal points to the sample points of the previous iteration, reconstruct the response surface function, and solve for the minimum value again.

9. The optimization method of the transformer insulation structure based on multi-physical-field coupling according to claim 8, characterized in that, During the solution, set the convergence condition of the iteration as the change amount of each optimization parameter not exceeding the preset threshold. After meeting the convergence condition, the iteration is completed, and the optimal solution at this time is the global optimal solution.

10. An optimization system for the insulation structure of a transformer based on multi-physical field coupling, characterized in that, Including: A simulation model construction module, configured to construct a finite element simulation model of the transformer according to the structure and electrical parameters of the transformer; Based on the coupling relationship of electromagnetic field - temperature field - fluid field, establish an electromagnetic - thermal - fluid multi - physical - field coupling model on the basis of the transformer finite element simulation model; Taking the maximum electric field strength as the optimization objective, a simulation test is carried out using the electromagnetic-thermal-fluid multi-physics coupling model to determine the structural parameters whose influence on the electric field strength is greater than the set value, and the variation range of the corresponding structural parameters is set according to the size and structural limitations; Based on the radial basis function, a response surface function is constructed; Within the variation range of each structural parameter, the genetic algorithm is used to obtain the optimal solution of the response surface function, and the globally optimal structural parameter variables and the maximum electric field strength are obtained.

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