Shell type transformer parameter design method and system

Abstract

The invention discloses a method and a system for designing parameters of a shell type transformer, and relates to the technical field of electrical engineering, and the method focuses on three core targets of minimizing total loss, maximizing efficiency and minimizing volume. Key design parameters such as the thickness of an iron core lamination, the diameter of an iron core column, the number of turns of a high-voltage winding, the number of turns of a low-voltage winding, the sectional area of a high-voltage wire and the sectional area of a low-voltage wire can cover the requirements for operation economy, energy conversion efficiency and installation space of the shell type transformer, and multi-target balance of loss, efficiency and size is achieved; secondly, optimizing is achieved based on an NSGA-II algorithm, the diversity and reliability of an optimal solution can be guaranteed, and the problem that traditional leakage magnetic field calculation precision is insufficient is solved; the finite element method FEM of the shell-type transformer is established based on the finite element method, a leakage magnetic field simulation calculation result during normal operation can be obtained, and theoretical support and engineering reference are provided for subsequent high-performance design of the shell-type transformer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical engineering, and particularly relates to a shell type transformer parameter design method and system. BACKGROUND

[0002] The shell type transformer has been widely applied in large power engineering, industrial field and rail transit scene due to its compact structure, strong short-circuit resistance, high mechanical strength and other significant advantages. However, the shell type transformer still faces many challenges in the design process. On the one hand, the complex structure leads to difficulty in accurately grasping the electromagnetic distribution law, and unreasonable electromagnetic design may cause local overheating, increased loss and other problems, and even seriously affect the service life of the equipment. On the other hand, as an important electromagnetic phenomenon in the operation of the transformer, the leakage magnetic field not only produces additional loss, but also may cause mechanical force impact, which poses a potential threat to the safe and stable operation of the transformer.

[0003] The electromagnetic design of the transformer is a complex optimization problem with multiple objectives and multiple constraints, involving multiple disciplines such as electromagnetic field theory, heat transfer, material science, etc. The leakage magnetic field calculation is a key link in the design of the transformer, and its calculation accuracy directly affects the evaluation of additional loss and mechanical force. However, the traditional leakage magnetic field calculation method is mainly based on analytical method, such as mirror method, separation of variables method, etc. These methods are only suitable for transformers with simple structure, and it is difficult to obtain ideal calculation results for shell type transformers with complex structure. Moreover, current electromagnetic optimization mainly focuses on single objective, and the balance of multiple objectives such as loss, efficiency and volume has not been achieved, resulting in insufficient calculation accuracy and difficulty in providing theoretical support and engineering reference for subsequent high-performance design of shell type transformers. SUMMARY

[0004] The present application provides a shell type transformer parameter design method and system, which solves the technical problem that current electromagnetic design of shell type transformers mainly focuses on single objective, and the balance of multiple objectives such as loss, efficiency and volume has not been achieved, and the calculation accuracy of leakage magnetic field is insufficient.

[0005] The shell type transformer parameter design method provided in the first aspect of the present application comprises: determining multiple design objectives and multiple key design parameters of the shell type transformer; the multiple design objectives include minimizing total loss, maximizing efficiency and minimizing volume, and the key design parameters at least affect the realization of one of the design objectives;

[0006] A multi-objective genetic NSGA-II algorithm is used to optimize and solve the multiple key design parameters, and at least one group of optimal solutions is obtained; the optimal solution is the parameter value corresponding to the situation that the multiple key design parameters jointly satisfy the multiple design objectives;

[0007] build a virtual shell transformer finite element model based on the optimal solution and a finite element method (FEM), and output a leakage magnetic field simulation calculation result of the shell transformer finite element model;

[0008] manufacture a real shell transformer physical prototype based on the optimal solution, and output a leakage magnetic field measurement calculation result of the shell transformer physical prototype;

[0009] Compare the leakage magnetic field simulation calculation result and the leakage magnetic field measurement calculation result. If the comparison result meets a preset condition, the optimal solution is determined as a target solution.

[0010] Optionally, the NSGA-II algorithm is implemented through a fast non-dominated sorting strategy, a crowded distance calculation strategy and an elite strategy.

[0011] The step of using a multi-objective genetic NSGA-II algorithm to optimize and solve a plurality of the key design parameters to obtain at least one group of optimal solutions includes:

[0012] determine the correlation between the target function and a plurality of the key design parameters, initialize the population size, the maximum evolution generation number, and set the current evolution generation number Gen = 1; wherein the target function includes the total loss, the efficiency and the material volume of the shell transformer, and the maximum evolution generation number is the iteration number corresponding to the convergence of the target function;

[0013] If the first generation of offspring population has not been generated, generate the first generation of offspring population through the initial population obtained by initialization, and set the Gen = 2;

[0014] If the first generation of offspring population has been generated, take the initial population as the first generation of parent population, merge the parent population and the offspring population into a new population, calculate the target function result of each individual in the new population, generate a new generation of parent population through the fast non-dominated sorting strategy, the crowded distance calculation strategy and the elite strategy, generate a new generation of offspring population through the new generation of parent population; determine whether the Gen is equal to the maximum evolution generation number. If not, set the Gen = Gen + 1, and return to the step of merging the parent population and the offspring population into a new population. If yes, the operation is ended, and at least one group of optimal solutions is output.

[0015] Optionally, the step of building a virtual shell transformer finite element model based on the optimal solution and a finite element method (FEM), and outputting a leakage magnetic field simulation calculation result of the shell transformer finite element model includes:

[0016] Based on the optimal solution and preset modeling parameters, a finite element model of the shell type transformer is built using a finite element method; the modeling parameters include at least one of the following parameters: core material and structure, lamination coefficient, size and winding mode of high-voltage winding wire, size and winding mode of low-voltage winding wire, and circuit connection mode.

[0017] The shell type transformer finite element model is meshed, and the spatial distribution of the leakage magnetic field of the shell type transformer finite element model during normal operation at multiple time nodes is analyzed to output the leakage magnetic field simulation calculation result under each input voltage.

[0018] Optionally, the step of manufacturing a real shell type transformer physical prototype based on the optimal solution and outputting a leakage magnetic field measured calculation result of the shell type transformer physical prototype comprises:

[0019] Based on the modeling parameters corresponding to the shell type transformer finite element model and the optimal solution, the shell type transformer physical prototype is manufactured.

[0020] The leakage magnetic field simulation calculation result of the shell type transformer physical prototype under each input voltage is measured through the built experimental platform; the input voltage corresponding to the leakage magnetic field simulation calculation result and the leakage magnetic field measured calculation result is consistent.

[0021] Optionally, if multiple groups of optimal solutions are obtained, after obtaining the multiple groups of optimal solutions and before building the shell type transformer finite element model and manufacturing the shell type transformer physical prototype, the method further comprises:

[0022] One group of optimal solution with the best comprehensive result is selected from the multiple groups of optimal solutions as the optimal solution for building the shell type transformer finite element model and manufacturing the shell type transformer physical prototype; the best comprehensive result means that the shell type transformer corresponding to the optimal solution is most likely to achieve multiple design targets and / or has the lowest cost.

[0023] Optionally, the key design parameters at least include one of the following parameters: core lamination thickness, core column diameter, high-voltage winding number of turns, low-voltage winding number of turns, high-voltage wire cross-sectional area, and low-voltage wire cross-sectional area.

[0024] The second aspect of the present application provides a shell type transformer parameter design system, comprising:

[0025] A determination module is configured to determine multiple design targets of a shell type transformer and multiple key design parameters; the multiple design targets include minimizing total loss, maximizing efficiency, and minimizing volume, and the key design parameters at least affect the realization of one of the design targets.

[0026] A solution module is configured to use a multi-objective genetic NSGA-II algorithm to perform optimization and solution on the plurality of key design parameters to obtain at least one set of optimal solutions, wherein the optimal solutions are parameter values corresponding to a case where the plurality of key design parameters collectively satisfy the plurality of design objectives.

[0027] A virtual simulation module is configured to build a virtual shell transformer finite element model based on the optimal solutions and a finite element method, and output a leakage magnetic field simulation calculation result of the shell transformer finite element model.

[0028] A physical test module is configured to manufacture a real shell transformer physical prototype based on the optimal solutions, and output a leakage magnetic field actual measurement calculation result of the shell transformer physical prototype.

[0029] A comparison module is configured to compare the leakage magnetic field simulation calculation result and the leakage magnetic field actual measurement calculation result, and if a comparison result satisfies a preset condition, determine the optimal solutions as target solutions.

[0030] The third aspect of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the shell transformer parameter design method.

[0031] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the shell transformer parameter design method.

[0032] The fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the shell transformer parameter design method.

[0033] From the above technical solutions, the present application has the following advantages:

[0034] The present application focuses on three core targets of minimizing total loss, maximizing efficiency and minimizing volume, and multiple key design parameters, which can cover the operation economy, energy conversion efficiency and installation space requirement of the shell type transformer, and realize the multi-target balance of loss, efficiency and volume; secondly, the optimization is realized based on the NSGA-II algorithm, which can guarantee the diversity and reliability of the optimal solution, so as to solve the problem of insufficient calculation precision of the traditional leakage magnetic field; in addition, the finite element model of the shell type transformer is established based on the finite element method (FEM), so that the simulation calculation result of the leakage magnetic field in normal operation can be obtained, thereby providing theoretical support and engineering reference for subsequent high-performance design of the shell type transformer. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0036] Figure 1 A step flow chart of a shell type transformer parameter design method provided by the embodiment of the present application is shown in the figure.

[0037] Figure 2 A shell type transformer three-phase five-column core lamination diagram provided by the embodiment of the present application is shown in the figure.

[0038] Figure 3 An equivalent magnetic circuit diagram of the shell type transformer provided by the embodiment of the present application is shown in the figure.

[0039] Figure 4 An NSGA-II algorithm flow chart provided by the embodiment of the present application is shown in the figure.

[0040] Figure 5 A Pareto optimal front diagram provided by the embodiment of the present application is shown in the figure, when the iteration is to the 80th generation, the target function converges stably.

[0041] Figure 6 A shell type transformer three-dimensional finite element model established according to the optimal parameters obtained by the NSGA-II algorithm provided by the embodiment of the present application is shown in the figure.

[0042] Figure 7 A B-H curve of a type 23QG090 oriented electrical steel silicon steel sheet provided by the embodiment of the present application is shown in the figure.

[0043] Figure 8 A calculated shell type transformer voltage time domain waveform provided by the embodiment of the present application is shown in the figure.

[0044] Figure 9 The calculated shell type transformer current time domain waveform provided by the embodiment of the present application;

[0045] Figure 10 The structural block diagram of the shell type transformer parameter design system provided by the embodiment of the present application;

[0046] Figure 11 The structural block diagram of the computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0048] It should be noted that, in the optional embodiments of the present application, the object information and other related data involved need to be authorized or agreed by the object when the embodiments of the present application are applied to specific products or technologies, and the collection, use and processing of the related data need to comply with the relevant laws, regulations and standards of the country and region. That is to say, if the embodiments of the present application involve data related to the object, the data need to be obtained under the condition that the object authorizes and agrees, the relevant department authorizes and agrees, and the relevant laws, regulations and standards of the country and region are met. If the embodiments involve personal information, the consent of the individual needs to be obtained for the acquisition of all personal information, and the separate consent of the information subject needs to be obtained for the acquisition of sensitive information, and the embodiments also need to be implemented under the condition that the object authorizes and agrees.

[0049] Please refer to Figure 1 , Figure 1 The step flowchart of the shell type transformer parameter design method provided by the first embodiment of the present application.

[0050] The shell type transformer parameter design method provided by the present application comprises:

[0051] Step 101, determining a plurality of design targets and a plurality of key design parameters of the shell type transformer;

[0052] In the embodiments of the present application, the plurality of design targets includes minimizing total loss, maximizing efficiency and minimizing volume, and the key design parameters at least affect the realization of one design target.

[0053] Among them, the total loss is minimized

[0054] (1)

[0055] Pfeis the iron loss

[0056] (2)

[0057] Pfeis the core material loss coefficient, Pfeis the frequency, a is the frequency coefficient, B is the magnetic induction, and β is the magnetic flux density coefficient, Veffis the effective volume of the core.

[0058] Pcu is the winding copper loss

[0059] (3)

[0060] Ih is the high-voltage winding rated current, R H Rh is the high-voltage winding DC resistance, I L Il is the low-voltage winding rated current, R L Rl is the low-voltage winding DC resistance.

[0061] Maximize efficiency η

[0062] (4)

[0063] S N is the rated capacity, and cosφ is the rated load power factor.

[0064] Minimize volume V total

[0065] (5)

[0066] V Fe is the core volume

[0067] (6)

[0068] D Fe is the core column diameter, H Fe is the core column height.

[0069] V Cu is the winding volume

[0070] (7)

[0071] N H is the high-voltage winding number of turns, N L is the low-voltage winding number of turns, S H is the high-voltage wire cross-sectional area, S LThis refers to the cross-sectional area of ​​the low-voltage conductor.

[0072] As explained above, the optimization design objective of a shell-type transformer is clearly a multi-objective optimization model that minimizes total loss, maximizes efficiency, and minimizes volume. This model analyzes the structural and electromagnetic design theory of shell-type transformers. For example, the core of a shell-type transformer can adopt a "core-wrapped winding" structure. Taking a 50kVA three-phase five-limb shell-type transformer as an example... Figure 2 As shown, the high-voltage winding adopts a disc-type insulation structure, with shaped insulating components inserted between the discs along the equipotential lines, resulting in high insulation reliability and low partial discharge. The high-voltage windings are connected in series and parallel, with the same winding direction. The high-voltage and low-voltage windings are arranged alternately and assembled into phase windings, with the low-voltage windings placed on the outermost side of the phase windings. Theoretical calculations show that the radial electromagnetic force of a shell-type transformer is very small. The axial electromagnetic force is large, but it can be significantly reduced when there are many leakage flux groups. The main magnetic circuit of a three-phase shell-type core transformer consists of four closed iron frames, with the two middle frames having a narrow branch in the middle. A detailed explanation of the equivalent magnetic circuit of a shell-type transformer can be found below.

[0073] Step 102: Use the multi-objective genetic NSGA-II algorithm to optimize multiple key design parameters and obtain at least one optimal solution;

[0074] The optimal solution mentioned above refers to the parameter values ​​that satisfy multiple design objectives when multiple key design parameters work together.

[0075] Furthermore, the NSGA-II algorithm is implemented through a fast non-dominated sorting strategy, a crowding distance calculation strategy, and an elitist strategy, such as... Figure 4 As shown, step 102 may include the following sub-steps:

[0076] S21. Determine the relationship between the objective function and multiple key design parameters, initialize the population size and maximum number of generations, and set the current generation Gen=1; where the objective function includes the total loss, efficiency, and material volume of the shell transformer, and the maximum number of generations is the number of iterations corresponding to the convergence of the objective function;

[0077] S22. If the first generation offspring population is not generated, generate the first generation offspring population using the initial population obtained from initialization, and set Gen=2.

[0078] S23, if the first generation of offspring population has been generated, the initial population is taken as the first generation of father population, the father population and the offspring population are combined to form a new population, the objective function results of individuals in the new population are calculated, a new generation of father population is generated through fast non-dominated sorting strategy, crowded distance calculation strategy and elite strategy, a new generation of offspring population is generated through the new generation of father population; it is judged whether Gen is equal to the maximum evolution generation, if not, Gen=Gen+1, and the step of combining the father population and the offspring population to form a new population is returned; if yes, the operation is ended, and at least one set of optimal solution is output.

[0079] The NSGA-II algorithm is used for optimization. Specifically, the "fast non-dominated sorting" is used to divide the "front" (preferentially reserve the first front non-dominated solution), the "crowded distance calculation" is used to ensure the diversity of the solution, and the "elite strategy" is used to combine the father population and the offspring population to avoid losing excellent individuals. The NSGA-II algorithm is configured and initialized to have a population size of 50-100 individuals, as shown in FIG. 2. Figure 5 As shown in FIG. 2, the maximum evolution generation is set to 80 generations because the objective function converges stably at the 80th iteration. Binary crossover is used to generate offspring, and polynomial mutation is used because the offspring will be mutated. The search range is adjusted by the mutation distribution index and the mutation step length to balance the global and local optimization capabilities. As shown in FIG. 2, the specific optimization process of the NSGA-II algorithm is as follows: Figure 4

[0080] First step: initial population and set evolution generation Gen=1.

[0081] Second step: judge whether the first generation of offspring population has been generated, if yes, set the evolution generation Gen=2, otherwise, perform non-dominated sorting and selection, binary crossover and mutation on the initial population to generate the first generation of offspring population and make the evolution generation Gen=2.

[0082] Third step: combine the father population and the offspring population to form a new population.

[0083] Fourth step: judge whether the new father population has been generated, if not, calculate the objective function of individuals in the new population, and perform fast non-dominated sorting, crowded distance calculation, elite strategy and other operations to generate the new father population; otherwise, go to the fifth step.

[0084] Fifth step: perform selection, crossover and mutation operations on the generated father population to generate the offspring population.

[0085] Sixth step: judge whether Gen is equal to the maximum evolution generation, if not, set the evolution generation Gen=Gen+1 and return to the third step; otherwise, the algorithm is ended, and the Pareto optimal solution set, i.e., at least one set of optimal solution, is output.

[0086] ​It can be specifically understood that the NSGA-II multi-objective optimization model is constructed, the mathematical relationship between the objective function and the design variable is derived based on electromagnetic theory, the population size (such as 50-100 individuals) and the maximum evolution number (such as 80 generations) of the NSGA-II algorithm are initialized, and the target function converges stably when the iteration is 80 generations. Figure 5 Binary crossover (formula 12-13) is adopted, and gene recombination is controlled through a crossover factor; polynomial mutation (formula 14-15) is adopted, and the search range is adjusted through a mutation step length to balance the global and local optimization capabilities. The non-dominated genetic (Non-dominated Sorting Genetic Algorithms, NSGA) algorithm is based on genetic algorithm and based on the Pareto optimal concept. The NSGA-II algorithm is an improvement based on the optimization effect and operation time of NSGA, and is an excellent multi-objective optimization algorithm suitable for the multi-objective optimization design of the transformer parameters of the application. The NSGA-II realizes efficient multi-objective optimization through three key mechanisms of fast non-dominated sorting, crowded distance calculation and elite strategy reservation.

[0087] Among them, the fast non-dominated sorting method reduces the calculation complexity of the algorithm, so that the calculation time of the algorithm is greatly reduced; for two solutions x i and x j , if x i dominates x j (referred to as x i x j ), it needs to meet: x i is not worse than x j in all objective functions; x i is strictly better than x j in at least one objective function. It is expressed as:

[0088] (8)

[0089] Among them, is the objective function, and k is the target number.

[0090] The non-dominated sorting divides the population into different “fronts”, the first front contains solutions that are not dominated by any solution, the second front contains solutions that are only dominated by the solutions in the first front, and so on.

[0091] The crowded distance calculation method replaces the fitness sharing strategy that needs to specify the shared radius, and serves as a standard for selecting excellent individuals among individuals of the same level, ensuring the diversity of individuals in the population, which is beneficial to the selection, crossover and mutation of individuals in the entire interval. For each objective function f k (x), the solutions in the front are sorted according to f k(x) sorted in ascending order, resulting in x(1), x(2), …, x(l), where l is the size of the front. The crowdedness of the boundary solutions (the solutions at the beginning and end of the sorted list) is set to:

[0092]

[0093] The crowdedness of the intermediate solutions x(i) is:

[0094] (10)

[0095] where and are the maximum and minimum values of objective k in the current front. The sum of the crowdedness of all objectives is:

[0096] (11)

[0097] The elitist strategy is adopted to reserve the parent individuals. After the parent individuals and offspring individuals are combined, the non-dominated sorting is performed, so that the search space is enlarged. When generating the next generation of parent population, the individuals with higher priority are selected in order, and the crowdedness is used to select among individuals of the same level, so that the excellent individuals can have a greater probability of being reserved. Simulated binary crossover is used to generate offspring. For the parent , , the offspring , satisfy

[0098] (12)

[0099] (13)

[0100] wherein, is a crossover factor, is a random number between 0 and 1, is a crossover distribution index. Since the offspring will be mutated, the value of x after mutation is

[0101] (14)

[0102] (15)

[0103] wherein, is a mutation step size, is a mutation distribution index, and are the upper and lower bounds of the variable.

[0104] It should be noted that the equivalent magnetic circuit of the shell-type transformer can be as shown in Figure 3As shown. The magnetic flux distribution in the iron core will vary depending on the connection group number. To simplify the analysis and considering that most actual connections are Y connections, the analysis will focus on the case of Y-connected windings.

[0105] Figure 3 The two magnetic circuits are connected by a circle with a double arrowhead, indicating that the two magnetic circuits are acted upon by the same magnetomotive force. Magnetic reluctance In the formula, L represents the magnetic path length, μ represents the permeability, and S represents the cross-sectional area. A complex quantity is introduced. Let the magnetic flux of each segment be Ф1~Ф6 respectively. Under symmetrical three-phase line voltage, we have: With the three phases having the same number of turns, according to the law of electromagnetic induction, the induced voltage is proportional to the magnetic flux of the magnetic circuit, which leads to the following...

[0106] (16)

[0107] After sorting

[0108] (17)

[0109] We can write equations for loops I and II to obtain...

[0110] (18)

[0111] Similarly, equations for loops III, IV, V, and VI can be written, and after transformation and calculation, Ф2~Ф6 can be obtained. By performing vector decomposition on Ф2~Ф6, they can be divided into real and imaginary parts, further simplifying the equations. Since there are many equations and unknowns, direct solution is quite complex and tedious. Therefore, computer software programming can be used for assisted solution. Based on the magnetic circuit length L, permeability μ, and cross-sectional area S, two relative magnetic reluctances R0 and R1 are obtained, which can then be input into the computer for solution.

[0112] By analyzing the structural characteristics and core electromagnetic design theories of shell-type transformers, six parameters that significantly affect them can be selected as key design parameters, i.e., optimization variables. These include the core lamination thickness, core column diameter, number of turns in the high-voltage conductor winding, number of turns in the low-voltage conductor winding, cross-sectional area of ​​the high-voltage conductor, and cross-sectional area of ​​the low-voltage conductor. Of course, these six parameters can also be selected as key design parameters for other reasons, such as meeting cost requirements. In practice, the range of variable values ​​can be set based on the engineering design specifications and material properties of shell-type transformers with structures such as three-phase five-limb transformers.

[0113] Therefore, based on the above theoretical analysis, when performing multi-objective optimization, the transformer leakage magnetic field and material cost can be taken as optimization objectives. Six factors that have a significant impact on the optimization objectives can be selected as optimization variables (i.e., key design parameters), including core thickness (2-200mm), core column diameter (10-200mm), high-voltage winding turns (30-300), low-voltage winding turns (10-100), and high-voltage conductor cross-sectional area (1-50mm²). 2 Low-voltage derivative cross-sectional area (5-100mm²) 2 ).

[0114] In summary, the specific process of performing the NSGA-II algorithm for optimization can be described as follows: Figure 4 As shown: Initialize the population, perform non-dominated sorting on individuals (divide the "frontier", the first frontier is the current optimal solution), calculate the crowding distance of individuals (measure the uniformity of solution distribution and avoid clustering), merge the parent and offspring populations through an elite strategy, select high-quality individuals to generate new parents, repeat crossover and mutation operations to generate offspring populations, iterate to the maximum number of generations, and output the Pareto optimal solution set, which contains only one set of optimal solutions.

[0115] Furthermore, after step 102 and before steps 103 and 104, the method of this application embodiment may also include the following screening step: selecting the group with the best comprehensive result from multiple optimal solutions as the optimal solution for building the finite element model of the shell transformer and manufacturing the shell transformer prototype; the optimal comprehensive result means that the shell transformer corresponding to the optimal solution is most likely to meet multiple design goals and / or has the lowest cost.

[0116] Specifically, the optimal parameter combination with the best overall performance can be selected from the Pareto optimal solution set. For example, an optimal solution might include: core thickness 111mm, core column diameter 95mm, high-voltage turns 108, low-voltage turns 36, high-voltage conductor cross-sectional area 9.157mm², and low-voltage conductor cross-sectional area 26.52mm². This optimal parameter combination selected from the Pareto optimal solution set can serve as the design benchmark for subsequent transformer simulation models and physical prototypes.

[0117] Step 103: Based on the optimal solution and the finite element method, build a virtual shell transformer finite element model and output the simulation calculation results of the leakage magnetic field of the shell transformer finite element model.

[0118] Specifically, virtual simulation products can be built using finite element software and the aforementioned optimal solutions, i.e., constructing... Figure 6The shell-type transformer finite element model (or three-dimensional finite element model, etc.) is shown. The finite element model is meshed, the windings are connected in the circuit, the material properties are set and the magnetic field is calculated. The three-dimensional leakage magnetic field of the shell-type transformer is calculated and analyzed.

[0119] Furthermore, step 103 may include the following sub-steps:

[0120] S31. Based on the optimal solution and preset modeling parameters, a finite element model of a shell transformer is built using the finite element method.

[0121] The modeling parameters include at least one of the following: core material and structure, lamination factor, size and winding method of high-voltage winding conductors, size and winding method of low-voltage winding conductors, and circuit connection method.

[0122] S32. Mesh the shell transformer finite element model and analyze the spatial distribution of the leakage magnetic field at multiple time points during normal operation of the shell transformer finite element model, so as to output the simulation calculation results of the leakage magnetic field under each input voltage.

[0123] like Figure 6 As shown, the core material can be selected from 23QG090 oriented electrical steel (refer to its BH curve as shown). Figure 7 As shown), the windings are arranged in a pie-shaped staggered pattern (the low-voltage winding is placed on the outermost side), and the circuit connection is set to the Dyn11 connection group. The high-voltage and low-voltage windings are staggered along the axial direction of the transformer body. The input rated parameters are (such as 50kVA capacity, 380V / 220V voltage, and 50Hz frequency).

[0124] Subsequently, the three-dimensional leakage magnetic field of the shell-type transformer can be calculated using finite element software to verify the accuracy of the model. A finite element model is established and meshed, and the windings are connected in circuit. The spatial magnetic flux density and leakage magnetic field distribution during normal operation are calculated using the finite element method. Four time nodes (0.02s, 0.025s, 0.03s, and 0.035s) are selected to output the time-domain distribution of the axial leakage magnetic field By component and the radial leakage magnetic field Bx and Bz components, clarifying the leakage magnetic field distribution pattern of "high at both ends of the axial direction and high radial gap".

[0125] The specific operating steps for S32 are as follows:

[0126] Step 1: Determine the basic parameters and construct a three-dimensional geometric model. Model the model in the order of iron core → winding → oil tank → air domain. The iron core adopts a three-phase five-column structure. The winding is arranged in a disc pattern with the low-voltage winding placed on the outside. The oil tank is designed to fit the body of the transformer. The air domain needs to completely wrap the oil tank to avoid magnetic field boundary reflection.

[0127] Step 2: Specify the electromagnetic parameters (also known as modeling parameters) for each component. The core is customized as 23QG090 silicon steel sheet, and the BH curve is imported, setting the lamination factor to 0.96. Copper is used for the windings, structural steel for the tank, and the air domain uses the default air properties. Of course, the specific values ​​of the above modeling parameters can be customized, such as using other core types.

[0128] Step 3: Add a "Magnetic Field" physical interface and select the transient solution type. Apply winding time-domain current excitation according to the Dyn11 connection group, set magnetic insulation on the outer wall of the tank, set a radiation boundary on the outer boundary of the air domain, and maintain the default continuity of the contact surface between the iron core and the winding.

[0129] Step 4: Use physical control mesh to densify the mesh in areas with concentrated leakage magnetic flux, such as the winding coil, iron core and winding gap, and sparse the mesh in the oil tank and air domain far away from the transformer body. Check to ensure that the minimum mass of the unit is ≥0.3.

[0130] Step 5: Select a transient solver and adopt a fully coupled method. Set the time and convergence conditions, with the start time set to 0s and the end time to 0.04s. Force output results at four key time nodes, set the relative tolerance to 1e-6, the maximum number of iterations to 500, select the MUMPS linear solver, and submit the calculation.

[0131] Step 6: Observe the spatial distribution of the leakage magnetic field at different time points using 3D cloud maps, plot the time-domain curve of the leakage magnetic field at the winding disc using dot plots, and analyze the distribution pattern of "high at both ends of the axial direction and high in the radial gap". Export the simulated values ​​of the leakage magnetic field under each input voltage.

[0132] Depend on Figure 8 and Figure 9 It can be seen that the primary side rated voltage is 533V, the primary side rated current is 34.2A, the secondary side rated voltage is 173V, and the secondary side rated current is 174A. The shell-type transformer adopts a Dyn11 connection, with 108 turns in one winding and 36 turns in the secondary winding. Since the specified rated values ​​are all RMS values, the waveform peak value is √2 times the RMS value. After star-angle transformation calculation, the primary side phase voltage amplitude is 537V, the primary side phase current amplitude is 35.8A, the secondary side phase voltage amplitude is 179V, and the secondary side phase current amplitude is 185A.

[0133] The spatial magnetic flux density and leakage magnetic field distribution during normal operation were calculated using the finite element method. The spatial magnetic flux density variation during normal operation of the shell-type transformer was presented at four time points: 0.02s, 0.025s, 0.03s, and 0.035s. The magnetic flux density was lowest at 0.02s when the current crossed zero, ranging from 0 to 0.409T. At 0.025s, during the peak of the positive half-cycle of the current, it rapidly increased to 0 to 2.01T. At 0.03s, the current fell back to zero, and due to the hysteresis effect of the iron core, the magnetic flux density remained in a stable peak range of 0 to 2.04T. At 0.035s, the peak of the negative half-cycle of the current slightly decreased to 0 to 1.96T, with overall high magnetic flux density fluctuations within ±0.08T. This change not only conforms to the law of "synchronous change of current and magnetic field" in alternating current, but also reflects the magnetic shielding advantage of the shell structure with "high magnetic density in the main magnetic circuit of the iron core and low magnetic density in the leakage magnetic region". At the same time, it verifies the rationality of the selection of iron core material and magnetic circuit design, and reflects the stable operation of the transformer without magnetic circuit abnormalities.

[0134] In addition, the time-domain distribution of the output axial leakage magnetic field By component and the radial leakage magnetic field Bx and Bz components can be analyzed. The spatial magnetic flux density of the shell transformer changes periodically with the current, with the lowest value of 0.409T at 0.02s and the highest value of 2.04T at 0.03s. The leakage magnetic field shows a distribution pattern of "high at both ends of the axial direction and high in the radial gap". The peak value of the axial By component at the end reaches 0.125T, and the radial Bx component has the largest amplitude at the gap between the high and low voltage windings.

[0135] Step 104: Based on the optimal solution, manufacture a real shell-type transformer prototype and output the measured calculation results of the leakage magnetic field of the shell-type transformer prototype.

[0136] In practice, the simulation results and the measured results are compared to verify the accuracy of the design. Similarly, a shell transformer prototype can be manufactured based on the optimal solution optimized by the NSGA-II algorithm, such as a 50kVA three-phase five-limb shell transformer prototype.

[0137] In addition, an experimental platform can be built consisting of a voltage regulator (adjusting the input voltage by 0.5pu-1pu), an isolation transformer (for safety isolation), an oscilloscope (for monitoring voltage), and a TM6160B AC / DC Tesla meter (for measuring leakage magnetic field). The TM6160B AC / DC Tesla meter is used to measure the leakage magnetic field of the shell-type transformer prototype, which is then compared with the simulation results to measure the error and verify its practical feasibility.

[0138] Furthermore, step 104 may include the following sub-steps:

[0139] S41. Based on the modeling parameters and optimal solution that correspond to the finite element model of the shell transformer, manufacture a physical prototype of the shell transformer;

[0140] S42. Using the constructed experimental platform, the simulation calculation results of the leakage magnetic field of the shell-type transformer prototype under various input voltages were measured; the input voltages corresponding to the simulation calculation results of the leakage magnetic field and the actual measured calculation results of the leakage magnetic field were consistent.

[0141] A prototype shell-type transformer was manufactured using the globally optimal solution obtained from the NSGA-II algorithm. Table 1 shows the design parameters required during the design process of the shell-type transformer.

[0142] Table 1 Design parameters for shell-type transformers

[0143]

[0144] For the prototype 50kVA / 380V / 220V shell-type transformer, the core is made of 23QG090 Shougang grain-oriented electrical steel, with core specifications of CN-window length 150mm × window width 55mm × core stack 111mm × core height 240mm. The windings use paper-insulated flat conductors. The low-voltage winding conductor size is 1.8mm × 9.3mm, with an insulation thickness of 0.3mm, wound in pairs (36 turns for low voltage); the high-voltage winding conductor size is 1.62mm × 7.4mm, with an insulation thickness of 0.3mm, and 108 turns for high voltage. The front and rear clamps of the core are connected using U-shaped stainless steel plates and screws to ensure a secure overall structure. The leakage magnetic field of the shell-type transformer prototype was measured using a TM6160B AC / DC Tesla meter, which can be used to measure alternating magnetic fields in space. During the measurement process, the input voltage of the shell-type transformer was set to 1 pu, 0.9 pu, 0.8 pu, 0.7 pu, 0.6 pu, and 0.5 pu using a voltage regulating transformer, and the magnitude of the axial leakage magnetic field of the winding was measured. An experimental platform for the leakage magnetic field of the shell-type transformer was constructed. The experimental platform consisted of a voltage regulator, an isolation transformer, an oscilloscope, the shell-type transformer, and a TM6160B AC / DC Tesla meter. The voltage regulating transformer was used to adjust the voltage input to the power supply, converting and regulating it to output the rated voltage of 1 pu, 0.9 pu, 0.8 pu, 0.7 pu, 0.6 pu, and 0.5 pu on the primary side. The isolation transformer isolated the power supply from the load, preventing damage to the load caused by power leakage or short circuits, and protecting personnel safety. The oscilloscope measured the input voltage of the shell-type transformer; the coil induction probe was placed at the axial winding plate, with the coil induction probe perpendicular to the leakage magnetic field direction, and the axial leakage magnetic field of each winding plate was measured. Comparison of simulated and measured values ​​shows that the error between the simulated and measured values ​​of the leakage magnetic field of the prototype is less than 3.9%. Furthermore, the total loss of the prototype is reduced by 12.3% after optimization, and the efficiency is increased to 98.72%. All performance indicators meet the requirements of the national standard GB / T6451-2023, verifying the accuracy and engineering application value of the electromagnetic optimization model and leakage magnetic field calculation method of this invention.

[0145] Step 105: Compare the simulation calculation results of the leakage magnetic field with the measured calculation results of the leakage magnetic field. If the comparison results meet the preset conditions, the optimal solution is determined as the target solution.

[0146] The aforementioned preset condition can be that the error of the comparison result is less than a certain threshold, which can be set according to the actual situation and is not limited here. For example, since the calculated values ​​are phase voltage and phase current, the overall calculation results are the same as the specified results, with an error of less than 5%, verifying the accuracy of the electromagnetic scheme of the finite element model established in this embodiment.

[0147] In comparison, the method in this application generates the Pareto optimal solution using the NSGA-II algorithm, solving the performance imbalance problem of traditional single-objective design and achieving a balance between total loss, efficiency, and volume. After optimization, the total loss of the prototype is reduced by 12.3%, and the efficiency is increased to 98.72%. Combining the finite element method and the nonlinear characteristics of the core BH curve, and considering the leakage flux concentration effect at the winding ends, the simulation and measurement error is ≤3.9%, providing a precise basis for winding structure optimization and loss suppression. Among them, the optimization variables (i.e., key design parameters) and parameter ranges can be set based on the actual shell-type transformer design specifications. The prototype development and experimental verification stages are aligned with the industrial production process and can be directly applied to the research and manufacturing of shell-type transformers in scenarios such as power systems and rail transit.

[0148] In summary, the method of this application can solve the problems of high loss and local overheating caused by concentrated leakage magnetic field in the traditional design of shell-type transformers. It is applicable to the optimization of electromagnetic parameters, performance improvement and accurate analysis of leakage magnetic field of typical shell-type power transformers such as three-phase five-limb shell-type transformers. It can be widely used in the research and development and manufacturing of high-performance shell-type transformers in scenarios such as power system transmission and distribution, industrial power supply and rail transit power supply. Specifically, the method of this application first constructs a multi-objective optimization model with the goals of "minimizing total loss, maximizing efficiency, and minimizing volume," selects key design parameters such as core lamination thickness and winding turns, and uses the NSGA-II multi-objective genetic algorithm to find the optimal solution. Second, based on the optimal solution and the finite element method (FEM), a finite element model of a shell-type transformer is established, and the calculated simulation results of voltage, current, spatial magnetic flux density, and leakage magnetic field distribution during normal operation are output. Finally, a 50kVA three-phase five-limb shell-type transformer prototype is manufactured based on the optimal solution, and leakage magnetic field experiments are conducted to verify the results. The results show that the total loss of the optimized transformer is reduced by 12.3%, the efficiency is increased to 98.72%, and the error between the simulated and measured leakage magnetic field values ​​is ≤3.9%. Therefore, it can be shown that this method can provide theoretical support and engineering reference for the high-performance design of shell-type transformers.

[0149] Please see Figure 10 , Figure 10 The present invention provides a structural block diagram of a shell-type transformer parameter design system.

[0150] This invention provides a shell-type transformer parameter design system, comprising:

[0151] Module 1001 is used to determine multiple design objectives and multiple key design parameters of the shell-type transformer; the multiple design objectives include minimizing total loss, maximizing efficiency, and minimizing volume, and the key design parameters affect the achievement of at least one design objective;

[0152] The solver module 1002 is used to optimize multiple key design parameters using the multi-objective genetic NSGA-II algorithm to obtain at least one set of optimal solutions; the optimal solution is the parameter value corresponding to the common satisfaction of multiple design objectives by multiple key design parameters.

[0153] The virtual simulation module 1003 is used to build a virtual shell transformer finite element model based on the optimal solution and the finite element method, and output the simulation calculation results of the leakage magnetic field of the shell transformer finite element model.

[0154] The physical testing module 1004 is used to manufacture a real shell-type transformer prototype based on the optimal solution and output the measured calculation results of the leakage magnetic field of the shell-type transformer prototype.

[0155] The comparison module 1005 is used to compare the simulation calculation results of the leakage magnetic field with the measured calculation results of the leakage magnetic field. If the comparison results meet the preset conditions, the optimal solution is determined as the target solution.

[0156] Furthermore, the NSGA-II algorithm is implemented through a fast non-dominated sorting strategy, a crowding distance calculation strategy, and an elitist strategy; the solution module 1002 can perform the following steps:

[0157] Determine the relationship between the objective function and multiple key design parameters, initialize the population size and maximum number of generations, and set the current generation Gen=1; where the objective function includes the total loss, efficiency, and material volume of the shell transformer, and the maximum number of generations is the number of iterations corresponding to the convergence of the objective function;

[0158] If the first generation of offspring population is not generated, the first generation of offspring population is generated using the initial population obtained from initialization, and Gen=2 is set.

[0159] If the first generation of offspring has been generated, the initial population is used as the first generation of parent population. The parent and offspring populations are merged into a new population. The objective function of individuals in the new population is calculated. A new generation of parent population is generated using a fast non-dominated sorting strategy, a crowding distance calculation strategy, and an elite strategy. A new generation of offspring population is generated from the new generation of parent population. It is then determined whether Gen is equal to the maximum number of generations. If not, Gen = Gen + 1, and the step of merging the parent and offspring populations into a new population is returned. If yes, the operation ends, and at least one optimal solution is output.

[0160] Furthermore, the virtual simulation module 1003 can perform the following steps:

[0161] Based on the optimal solution and preset modeling parameters, a shell transformer finite element model is built using the finite element method. The modeling parameters include at least one of the following: core material and structure, lamination factor, size and winding method of high voltage winding conductors, size and winding method of low voltage winding conductors, and circuit connection method.

[0162] Mesh the shell transformer finite element model and analyze the spatial distribution of leakage magnetic field at multiple time points during normal operation of the shell transformer finite element model, so as to output the simulation calculation results of leakage magnetic field under each input voltage.

[0163] Furthermore, the entity testing module 1004 can perform the following steps:

[0164] Based on the modeling parameters and optimal solution that correspond to the finite element model of the shell transformer, a physical prototype of the shell transformer is manufactured.

[0165] The simulated leakage magnetic field of the shell-type transformer prototype was measured under various input voltages using the experimental platform. The simulated leakage magnetic field results and the measured leakage magnetic field results corresponded to the same input voltages.

[0166] Furthermore, the solver module 1002 can also perform the following steps:

[0167] The optimal solution is selected from multiple optimal solutions to obtain the best overall result. This optimal solution is used to build the finite element model of the shell transformer and manufacture the physical prototype of the shell transformer. The optimal overall result means that the shell transformer corresponding to the optimal solution is most likely to meet multiple design objectives and / or has the lowest cost.

[0168] Furthermore, the key design parameters shall include at least one of the following parameters: core lamination thickness, core column diameter, number of turns in the high-voltage winding, number of turns in the low-voltage winding, cross-sectional area of ​​the high-voltage conductor, and cross-sectional area of ​​the low-voltage conductor.

[0169] Please see Figure 11 , Figure 11 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0170] An electronic device according to an embodiment of the present invention includes: a memory 1101 and a processor 1102. The memory 1101 stores a computer program. When the computer program is executed by the processor 1102, the processor 1102 executes the shell transformer parameter design method as described in the above embodiment.

[0171] Memory 1101 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 1101 has storage space 1103 for program code 1104 for performing any of the method steps described above. For example, storage space 1103 for program code may include various program codes 1104 for implementing the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the shell transformer parameter design method described above.

[0172] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the shell transformer parameter design method as described in the above embodiments.

[0173] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the shell transformer parameter design method as described in the above embodiments.

[0174] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0178] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for designing parameters of a shell-type transformer, characterized in that, include: Determine multiple design objectives and key design parameters for shell-type transformers; The design objectives include minimizing total loss, maximizing efficiency, and minimizing volume, and the key design parameters affect the achievement of at least one of the design objectives; The multi-objective genetic NSGA-II algorithm is used to optimize multiple key design parameters and obtain at least one optimal solution; the optimal solution is the parameter value corresponding to the multiple key design parameters jointly satisfying multiple design objectives. Based on the optimal solution and the finite element method, a virtual shell transformer finite element model is built, and the leakage magnetic field simulation calculation results of the shell transformer finite element model are output. Based on the optimal solution, a real shell-type transformer prototype is manufactured, and the measured calculation results of the leakage magnetic field of the shell-type transformer prototype are output. The simulation results of the leakage magnetic field and the measured results of the leakage magnetic field are compared. If the comparison results meet the preset conditions, the optimal solution is determined as the target solution.

2. The shell-type transformer parameter design method according to claim 1, characterized in that, The NSGA-II algorithm is implemented through a fast non-dominated sorting strategy, a crowding distance calculation strategy, and an elite strategy. The step of using the multi-objective genetic NSGA-II algorithm to optimize multiple key design parameters and obtain at least one optimal solution includes: Determine the correlation between the objective function and multiple key design parameters, initialize the population size and maximum number of generations, and set the current generation Gen=1; wherein, the objective function includes the total loss, efficiency, and material volume of the shell transformer, and the maximum number of generations is the number of iterations corresponding to the convergence of the objective function; If the first generation offspring population is not generated, the first generation offspring population is generated using the initial population obtained from initialization, and Gen=2 is set. If the first generation of offspring population has been generated, the initial population is used as the first generation of parent population. The parent population and the offspring population are merged into a new population. The objective function result of the individuals in the new population is calculated. A new generation of parent population is generated through a fast non-dominated sorting strategy, a crowding distance calculation strategy, and an elite strategy. A new generation of offspring population is generated through the new generation of parent population. Determine whether Gen is equal to the maximum number of generations. If not, set Gen = Gen + 1 and return to the step of merging the parent population and the offspring population into a new population. If so, the calculation ends, and at least one optimal solution is output.

3. The shell-type transformer parameter design method according to claim 1, characterized in that, The steps of constructing a virtual shell-type transformer finite element model based on the optimal solution and the finite element method, and outputting the simulation calculation results of the leakage magnetic field of the shell-type transformer finite element model, include: Based on the optimal solution and the preset modeling parameters, the finite element model of the shell transformer is built using the finite element method; the modeling parameters include at least one of the following parameters: core material and structure, lamination factor, size and winding method of high voltage winding conductors, size and winding method of low voltage winding conductors, and circuit connection method. The shell-type transformer finite element model is meshed, and the spatial distribution of the leakage magnetic field at multiple time points during normal operation is analyzed to output the simulation calculation results of the leakage magnetic field under each input voltage.

4. The shell-type transformer parameter design method according to claim 1 or 3, characterized in that, The steps of manufacturing a real shell-type transformer prototype based on the optimal solution and outputting the measured calculation results of the leakage magnetic field of the shell-type transformer prototype include: Based on the modeling parameters consistent with the finite element model of the shell transformer and the optimal solution, a physical prototype of the shell transformer is manufactured. The simulated leakage magnetic field of the shell-type transformer prototype was measured under various input voltages using the constructed experimental platform. The simulated leakage magnetic field results and the measured leakage magnetic field results corresponded to the same input voltages.

5. The shell-type transformer parameter design method according to claim 1, characterized in that, If the obtained optimal solution is multiple sets, then after obtaining multiple sets of optimal solutions, and before building the finite element model of the shell transformer and manufacturing the physical prototype of the shell transformer, the method further includes: The optimal solution is selected from multiple optimal solutions to obtain the optimal solution for building the finite element model of the shell transformer and manufacturing the physical prototype of the shell transformer. The optimal comprehensive result means that the shell transformer corresponding to the optimal solution is most likely to meet multiple design objectives and / or has the lowest cost.

6. The shell-type transformer parameter design method according to claim 1, characterized in that, The key design parameters include at least one of the following: core lamination thickness, core column diameter, number of turns in the high-voltage winding, number of turns in the low-voltage winding, cross-sectional area of ​​the high-voltage conductor, and cross-sectional area of ​​the low-voltage conductor.

7. A shell-type transformer parameter design system, characterized in that, include: The determination module is used to determine multiple design objectives and key design parameters for shell-type transformers; The design objectives include minimizing total loss, maximizing efficiency, and minimizing volume, and the key design parameters affect the achievement of at least one of the design objectives; The solution module is used to optimize multiple key design parameters using the multi-objective genetic NSGA-II algorithm to obtain at least one optimal solution; the optimal solution is the parameter value corresponding to the multiple key design parameters jointly satisfying multiple design objectives; The virtual simulation module is used to build a virtual shell transformer finite element model based on the optimal solution and the finite element method, and output the simulation calculation results of the leakage magnetic field of the shell transformer finite element model. The physical testing module is used to manufacture a real shell-type transformer prototype based on the optimal solution and output the measured calculation results of the leakage magnetic field of the shell-type transformer prototype. The comparison module is used to compare the simulation calculation results of the leakage magnetic field with the measured calculation results of the leakage magnetic field. If the comparison results meet the preset conditions, the optimal solution is determined as the target solution.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the shell transformer parameter design method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the shell transformer parameter design method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the shell transformer parameter design method as described in any one of claims 1-6.

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