Multi-physics field optimization design method and system for high-capacity high-frequency transformer

Abstract

The invention provides a multi-physical field optimization design method and system for a high-capacity high-frequency transformer, and belongs to the technical field of high-frequency transformers, and the method comprises the steps: constructing optimization targets of the cost, power density and efficiency of the high-frequency transformer according to the application demands of the high-frequency transformer; based on the optimization targets of the cost, the power density and the efficiency of the high-frequency transformer, a calculation model of iron core loss and winding loss is introduced, and the loss amount of the high-frequency transformer is obtained; based on the maximum feasible size of the high-frequency transformer iron core and thermal and electromagnetic constraints, introducing iron core size constraints; and based on the constraint of the size of the iron core, carrying out optimization design on the high-frequency transformer by using a Taguchi method, and based on a selective non-dominated genetic algorithm, carrying out Pareto frontier relation analysis on the high-frequency transformer subjected to the optimization design to obtain an optimal design scheme of the high-frequency transformer. And comprehensive optimization requirements of multiple physical fields can be effectively met.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-frequency transformers, and in particular relates to a multi-physical field optimization design method and system for a large-capacity high-frequency transformer. Background Art

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

[0003] With the rapid development of power electronics technology, high-frequency transformers (HFT) are increasingly used in power transmission, renewable energy generation (such as wind and solar energy), and electric vehicle charging. In order to meet the needs of modern power systems for high frequency, light weight, and high efficiency, high-frequency transformer design has gradually evolved from traditional 50 / 60Hz AC systems to high frequency, small size, and low loss. How to achieve high power density and high efficiency while controlling costs has become a key issue in high-frequency transformer design.

[0004] Under high-frequency conditions, the performance of high-frequency transformers is affected by the mutual coupling of multiple physical fields such as electric field, magnetic field, thermal field and mechanical field. Electromagnetic loss, heat accumulation, temperature field distribution and other factors are closely related, which puts high demands on the heat dissipation performance and structural stability of high-frequency transformers. Especially in large-capacity and high-frequency applications, electromagnetic stress, temperature rise and vibration effects significantly affect the long-term operating reliability of the equipment. Therefore, it is necessary to ensure the efficient and stable operation of high-frequency transformers under complex working conditions through comprehensive optimization design of multiple physical fields.

[0005] The choice of core material plays a key role in the efficiency of high-frequency transformers. At high frequencies, the loss of core materials, including hysteresis loss and eddy current loss, increases significantly, and traditional silicon steel can no longer meet the demand. Therefore, nanocrystalline and amorphous alloy materials have become the mainstream choice due to their low loss and high saturation flux density characteristics. However, as the frequency increases, the temperature rise caused by core loss cannot be ignored. How to effectively reduce core loss and manage temperature has become an important challenge in the design.

[0006] Winding design also needs to take high-frequency effects into consideration. In high-frequency and high-power applications, skin effect and proximity effect significantly increase the AC loss of the winding. To reduce this loss, Litz wire winding is usually used. This winding is made of multiple strands of insulated wire twisted together, which can effectively reduce the resistance at high frequencies. Through multi-physics simulation and optimization, precise design of winding and core materials, electrical parameters, etc. can be achieved, thereby improving the power density and efficiency of high-frequency transformers and controlling overall costs.

[0007] At present, the traditional design of high-frequency transformers has significantly increased electromagnetic losses under high-frequency conditions, making it difficult to achieve high efficiency, and the temperature rise problem of the core and winding is difficult to effectively solve, affecting the long-term stability of high-frequency transformers. Due to the mutual coupling of multiple physical fields of electricity, magnetism, heat and force, traditional design methods cannot effectively meet the comprehensive optimization needs of multiple physical fields. Summary of the invention

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a multi-physical field optimization design method and system for a large-capacity high-frequency transformer. Through the coupling analysis of multiple physical fields, the influence of the four physical fields of electricity, magnetism, heat and force are comprehensively considered to optimize the design of the high-frequency transformer to ensure its efficient and stable operation under high-frequency and high-capacity working conditions.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] The first aspect of the present invention provides a multi-physics field optimization design method for a large-capacity high-frequency transformer;

[0011] A multi-physics field optimization design method for a large-capacity high-frequency transformer, comprising:

[0012] According to the application requirements of high-frequency transformers, the optimization targets of high-frequency transformer cost, power density and efficiency are established respectively;

[0013] Based on the optimization objectives of high-frequency transformer cost, power density and efficiency, the calculation model of core loss and winding loss is introduced to obtain the loss of high-frequency transformer;

[0014] Based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints, the core size constraint is introduced;

[0015] Based on the constraint of core size, the Taguchi method is used to optimize the design of the high-frequency transformer. Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the optimized high-frequency transformer is carried out to obtain the optimal design scheme of the high-frequency transformer.

[0016] As a further technical solution, the optimization target based on power density is:

[0017]

[0018] Where P d is the power density; P 0 is the rated power of the high-frequency transformer, V c is the volume of the core, V w is the volume of the Litz wire winding;

[0019] Cost C t for:

[0020] C t =MLT×(N p +N s )C w +V c C c

[0021] Where MLT is the average turn length of the winding, N p 、N s is the number of turns of the primary and secondary windings, C w , C c are the cost coefficients of Litz wire and nanocrystalline core respectively;

[0022] The efficiency η is:

[0023]

[0024] Among them, P t is the total loss of the high frequency transformer.

[0025] As a further technical solution, the core loss is modeled using the modified generalized Steinmetz equation, where:

[0026] The improved generalized Steinmetz equation is as follows:

[0027]

[0028] In the formula, is the core loss; T is the period; B is the magnetic flux density; α, β, k are the core parameters used for loss calculation, which depend on the type of core used.

[0029] Since the core loss depends on the flux density and the operating frequency, the optimal flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as:

[0030]

[0031] In the formula, B op The optimal flux density required to minimize the power loss of the high-frequency transformer; H c is the coercivity of the core material; k a , K c , k w is the coefficient of the core material used; ΔT a is the temperature change; ρ cu is the resistivity of copper; k u is the window utilization coefficient; k c is a constant related to the eddy current loss of the core material; f s a f sa to the power of a, a is the Steinmetz coefficient, which is determined by the core material; K v is the type of voltage waveform applied to the HFT; f is the operating frequency of the high-frequency transformer; k f is the core stacking factor; ∑VA is the total rated power; A p is the product of the core window area and the core cross-sectional area of ​​the frequency transformer; f s is the switching frequency of the high-frequency transformer; K f is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; K t is the correction factor related to heat conduction.

[0032] As a further technical solution, the modeling process of the winding loss is specifically as follows:

[0033] Calculate the AC resistance of the winding:

[0034]

[0035] in is the diameter of the Litz wire; l is the number of winding layers; n s is the packing factor; ξ is the number of strands in each Litz wire conductor; d cu is the diameter of the copper conductor, δ s is the skin effect; N is the number of winding turns.

[0036] Maintain the ratio of the AC to DC resistance of the winding at a specific value, as shown in the following equation:

[0037]

[0038] In the formula, r o is the radius of the circular conductor, frequency f s Skin effect under s It is expressed as:

[0039]

[0040] The power loss in the primary and secondary windings is expressed as:

[0041]

[0042] in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively;

[0043]

[0044] Where P winding is the winding loss.

[0045] As a further technical solution, the core size constraint is:

[0046]

[0047] In the formula, h c is the core height; d c is the width of the core yoke; t c is the core thickness; l c N is the core width. s,p is the number of turns of the secondary winding and the number of turns of the primary winding; B sat is the saturation magnetic flux density; h max , l max ,t max d max are the maximum height, maximum width, maximum thickness and maximum yoke width allowed by the core; T is the operating temperature of the high-frequency transformer; T max N is the maximum allowable temperature rise of the high-frequency transformer; min and N max Refers to the minimum number of winding turns and the maximum number of winding turns respectively.

[0048] As a further technical solution, based on the constraints of core size, the process of optimizing the design of high-frequency transformer using Taguchi method is as follows:

[0049] The core width, height, thickness and yoke width are used as the design variables of the core size, and the level number is set to describe the range of variation;

[0050] In combination with the characteristics of the four-variable mixed level, an orthogonal table is selected to determine the test points as the design scheme; the test points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table;

[0051] The target performance is simulated and evaluated based on the parameter combination, and the influence of each design variable is evaluated by using range analysis and variance analysis methods.

[0052] As a further technical solution, based on the selective non-dominated genetic algorithm, the Pareto frontier relationship analysis is performed on the high-frequency transformer after the optimized design, and the process of obtaining the optimal design solution of the high-frequency transformer is as follows:

[0053] Perform fitness evaluation on the high-frequency transformer after optimization design, wherein the fitness is evaluated with cost, power density and efficiency as objective functions;

[0054] Set the stop criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, stop running;

[0055] The width, height, thickness and yoke width of the core are used as design variables, and the design variables that meet the multi-objective optimization criteria are selected;

[0056] By selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

[0057] As a further technical solution, it also includes performing multi-physical field bidirectional coupling simulation analysis on the scheme, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

[0058] A second aspect of the present invention provides a multi-physics field optimization design system for large-capacity high-frequency transformers.

[0059] A multi-physics field optimization design system for large-capacity high-frequency transformers, comprising:

[0060] The optimization target building module is configured to: build optimization targets of high-frequency transformer cost, power density and efficiency respectively according to application requirements of the high-frequency transformer;

[0061] The loss acquisition module is configured to: based on the optimization objectives of high-frequency transformer cost, power density and efficiency, introduce a calculation model of core loss and winding loss to acquire the loss amount of the high-frequency transformer;

[0062] The constraint establishment module is configured to: introduce a core size constraint based on a maximum feasible size of a high frequency transformer core and thermal and electromagnetic constraints;

[0063] The optimal design scheme acquisition module is configured as follows: based on the constraint of core size, the Taguchi method is used to optimize the design of the high-frequency transformer, and based on the selective non-dominated genetic algorithm, the Pareto frontier relationship analysis is performed on the high-frequency transformer after the optimized design to obtain the optimal design scheme of the high-frequency transformer.

[0064] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer as described in the first aspect of the present invention.

[0065] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer are implemented as described in the first aspect of the present invention.

[0066] One or more of the above technical solutions have the following beneficial effects:

[0067] (1) The present invention introduces a multi-objective optimization algorithm to achieve a multi-faceted balance in the design of high-frequency transformers. By comprehensively considering multiple factors such as efficiency, power density, and cost, it ensures that production costs are effectively controlled while meeting high performance. This optimization method can minimize material usage and production costs without sacrificing equipment performance, and is particularly suitable for market demand in large-scale production.

[0068] (2) The present invention optimizes the core size and material, adjusts its magnetic properties and geometric dimensions, and reduces eddy current loss and hysteresis loss caused by high-frequency effects, thereby effectively achieving high power density design. This optimization method significantly improves the electromagnetic efficiency of high-frequency transformers and provides a more reliable solution for high-frequency and high-power applications.

[0069] (3) The present invention adopts a multi-physics field coupling analysis method, and solves the common electromagnetic loss and thermal management problems in high-frequency transformers by comprehensive simulation and optimization of electric field, magnetic field, thermal field and mechanical field. On the basis of electromagnetic coupling optimization, by accurately simulating temperature rise and stress distribution, the loss and temperature rise are significantly reduced, thereby improving the working efficiency and long-term operation reliability of the high-frequency transformer. This method can achieve more accurate thermal management and electromagnetic optimization, and provides a scientific basis for the design of high-efficiency high-frequency transformers.

[0070] (4) The present invention optimizes the geometric structure of the high-frequency transformer through mechanical field analysis, and improves its ability to resist vibration and impact. Under the combined action of electromagnetic force and mechanical stress, the structure of the high-frequency transformer may vibrate, deform, and affect its working performance. Through precise mechanical simulation and optimized design, the present invention effectively avoids deformation and damage caused by mechanical stress, and ensures the stability and reliability of the high-frequency transformer under long-term and high-load conditions.

[0071] (5) The multi-physical field coupling optimization design adopted by the present invention is not limited to electromagnetic performance, but also covers multiple aspects such as thermal management and mechanical structure stability. Through the joint optimization of multiple physical fields, it is possible to ensure high efficiency and high power density while taking into account the thermal stability, structural strength and electromagnetic compatibility of the high-frequency transformer, thus avoiding the performance imbalance caused by a single optimization. It provides a more comprehensive solution for the design of high-frequency transformers, which is particularly suitable for high-power and high-frequency application scenarios, and can provide efficient and stable performance in actual use.

[0072] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0074] Figure 1 This is a flow chart of the method of the first embodiment.

[0075] Figure 2 Schematic diagram of the design variables of the core size in the first embodiment.

[0076] Figure 3 It is a schematic diagram of the optimization results of the non-dominated sorting genetic algorithm in the first embodiment.

[0077] Figure 4 This is a schematic diagram of the analysis results between the design variables and the objective function of the first embodiment.

[0078] Figure 5 It is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0079] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0080] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0081] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0082] Embodiment 1

[0083] This embodiment discloses a multi-physics field optimization design method for a large-capacity high-frequency transformer;

[0084] like Figure 1 As shown, a multi-physics field optimization design method for a large-capacity high-frequency transformer includes:

[0085] Step S1, constructing optimization targets for high-frequency transformer cost, power density and efficiency respectively according to application requirements of the high-frequency transformer;

[0086] Wherein, the optimization target based on power density is:

[0087]

[0088] Where P d is the power density; P 0is the rated power of the high-frequency transformer, V c is the volume of the core, V w is the volume of the Litz wire winding;

[0089] Cost C t for:

[0090] C t =MLT×(N p +N s )C w +V c C c

[0091] Where MLT is the average turn length of the winding, N p 、N s is the number of turns of the primary and secondary windings, C w , C c are the cost coefficients of Litz wire and nanocrystalline core respectively;

[0092] The efficiency η is:

[0093]

[0094] Among them, P t is the total loss of the high frequency transformer.

[0095] Step S2, based on the optimization goals of high-frequency transformer cost, power density and efficiency, introduce the calculation model of core loss and winding loss to obtain the loss of the high-frequency transformer; further obtain the optimization target efficiency of the high-frequency transformer, and select a suitable design scheme based on the optimization goal.

[0096] The significance of core loss depends mainly on the characteristics of the selected core material. In order to accurately estimate the core loss in high-frequency transformers, the modified generalized Steinmetz equation is used as follows:

[0097]

[0098] In the formula, is the core loss; T is the period; B is the magnetic flux density; α, β, k are the core parameters used for loss calculation, which depend on the type of core used.

[0099] Since the core power loss depends on the magnetic flux density and the operating frequency, the optimal magnetic flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as follows:

[0100]

[0101]

[0102] In the formula, B opThe optimal flux density required to minimize the power loss of the high-frequency transformer; H c is the coercivity of the core material; k a , K c , k w is the coefficient of the core material used; ΔT a is the temperature change; ρ cu is the resistivity of copper; k u is the window utilization coefficient; k c is a constant related to the eddy current loss of the core material; f s a to the power of a, a is the Steinmetz coefficient, which is determined by the core material; K v is the type of voltage waveform applied to the HFT; f is the operating frequency of the high-frequency transformer; k f is the core stacking factor; ∑VA is the total rated power; A p is the product of the core window area and the core cross-sectional area of ​​the frequency transformer; f s is the switching frequency of the high-frequency transformer; K f is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; K t is the correction factor related to heat conduction.

[0103] Furthermore, in the process of modeling winding losses, the power loss in the high-frequency transformer winding depends on the winding resistance and the effective value of the current flowing through the winding. Considering the influence of high frequency on the winding resistance, the winding AC resistance value can be calculated by the following formula:

[0104]

[0105] in is the diameter of the Litz wire; l is the number of winding layers; n s is the packing factor; ξ is the number of strands in each Litz wire conductor; d cu is the diameter of the copper conductor, δ s is the skin effect; N is the number of winding turns, N = N s =N p .

[0106] To minimize proximity and winding losses, the ratio of the AC / DC resistance of the winding should be around 2. This ratio can be estimated using the following formula,

[0107]

[0108] In the formula, r o is the radius of the circular conductor, frequency f s Skin effect under s It is expressed as:

[0109]

[0110] The power losses in the primary and secondary windings can be estimated by:

[0111]

[0112] in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively.

[0113]

[0114] Where P winding is the winding loss.

[0115] The optimized target efficiency of the high-frequency transformer can be calculated by the core loss and winding loss, and the appropriate solution can be selected by optimizing the target.

[0116] Step S3, introducing core size constraints based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints;

[0117] In step S3, electrical constraints (such as voltage, current, frequency, etc.) will put forward specific requirements for the core size. For example, high voltage applications require a larger insulation distance, which will affect the size and structural design of the core. Size optimization needs to consider these electrical constraints to ensure that the transformer can operate safely and reliably under various working conditions. For example, in high-frequency applications, the skin effect and proximity effect will cause the current to concentrate on the surface of the conductor, and it is necessary to increase the cross-sectional area of ​​the conductor or adopt a special structure, which will also affect the size design of the core. Thermal constraints (temperature rise limits) will affect the choice of core size. A larger core can provide better heat dissipation performance, but it will increase the volume and cost of the transformer. Electromagnetic constraints (such as electromagnetic interference, magnetic induction intensity, etc.) will affect the core size and structural design. For example, in order to reduce electromagnetic interference, special core materials may be required, which will also affect the size and cost of the transformer. Size optimization needs to consider electromagnetic constraints in a coordinated manner to ensure that the transformer can work properly in various electromagnetic environments.

[0118] Electrical constraints are mainly used to ensure that the designed equipment can meet the electrical performance requirements, avoid system failure due to unsatisfactory electrical conditions, and ensure that the high-voltage transformer can operate safely and reliably under various working conditions.

[0119] Common electrical constraints include:

[0120] Maximum current I max: Limits the maximum current flowing through a transformer or inductor to prevent overload.

[0121] Maximum flux density L f,max : Ensure that the flux density does not exceed the allowable range to avoid core saturation.

[0122] Minimum voltage V min and the maximum voltage V max : Limit the operating voltage range to ensure that the device can operate within a safe range.

[0123] Efficiency Constraint η min : Ensure that the conversion efficiency of the equipment is higher than the minimum requirement.

[0124] Thermal constraints mainly consider the heat generated by the equipment during operation and its heat dissipation capacity. By limiting the temperature rise, it ensures that the high-frequency transformer will not overheat under the rated load, thereby preventing insulation material aging, winding short circuit and other faults. Further good heat dissipation performance can extend the service life of the high-frequency transformer and improve its reliability and stability. Common thermal constraints include:

[0125] Maximum temperature rise T max : Limits the temperature rise of the equipment, usually determined by material properties or operating environment conditions.

[0126] Heat conduction and heat dissipation capabilities: Through thermal field analysis, ensure that the designed heat dissipation path and cooling method can effectively reduce the temperature of the equipment.

[0127] Electromagnetic constraints are to ensure that the electromagnetic performance of the equipment meets the design requirements, avoid excessive electromagnetic stress or field strength, and effectively reduce electromagnetic interference. By setting electromagnetic interference and electromagnetic compatibility constraints, it is ensured that the transformer can work normally in various electromagnetic environments and does not interfere with other equipment. It includes the following aspects:

[0128] Magnetic induction intensity B max :Limit the magnetic induction intensity of the core to prevent the core from entering a saturation state,

[0129] Electromagnetic compatibility constraints: Ensure that the operation of the equipment does not generate excessive electromagnetic interference and meets the standard electromagnetic compatibility requirements.

[0130] Electromagnetic field boundary condition check: Through finite element analysis, the electromagnetic field distribution is accurately calculated to verify the rationality of the electromagnetic field at the boundary.

[0131] Furthermore, electrical performance can be ensured by constraining the core size. By limiting the core size, it is ensured that the transformer can provide the required electrical performance such as inductance and flux density under specific voltage, current and frequency; optimize volume and cost: while meeting the electrical performance, the transformer can minimize its volume and cost through size optimization.

[0132] Combination Figure 2 , based on the above constraints, the multi-objective optimization problem of high-frequency transformer is expressed as:

[0133] (l c ,h c ,t c ,d c ,N p ,N s )=min(P t ,C t ,-P d )

[0134] In the formula, h c is the core height; d c is the width of the core yoke; t c is the core thickness; l c N is the core width. p 、N s is the number of turns of the primary and secondary windings. In the multi-objective optimization process, multiple objectives may conflict with each other. Optimizing one objective may lead to the degradation of the performance of other objectives. Therefore, it is necessary to find a balance point between the objectives and use the Pareto optimal solution to describe this balance relationship.

[0135] The search space for the optimal values ​​of the high-frequency transformer core parameters and windings is limited based on the maximum feasible core size and the constraints of the power electronic converter used. p 、N s , which will ensure a reasonable value for the leakage inductance without any serious impact on the efficiency of the high-frequency transformer. The maximum permissible temperature rise and the saturation flux density of the core are used to set thermal and electromagnetic constraints during the optimization process. In addition, the core size (h c , l c ,t c d c )The constraints are as follows:

[0136]

[0137] Where N s,p is the number of turns of the secondary winding and the number of turns of the primary winding; B sat is the saturation magnetic flux density; h max , l max ,t max d max are the maximum height, maximum width, maximum thickness and maximum yoke width allowed by the core; T is the operating temperature of the high-frequency transformer; T maxIt is the maximum allowable temperature rise of the high frequency transformer. ; N min and N max Refers to the minimum number of winding turns and the maximum number of winding turns respectively.

[0138] Step S4, based on the constraint of the core size, the high-frequency transformer is optimized by using the Taguchi method, and the Pareto front relationship analysis is performed on the optimized high-frequency transformer based on the selective non-dominated genetic algorithm to obtain the optimal design scheme of the high-frequency transformer.

[0139] In the multi-objective optimization design of high-frequency transformers, in order to reduce the consumption of computing resources and time and cover different combinations of the design space, this embodiment adopts the Taguchi method to design a four-variable mixed level orthogonal test for the core size. The design variables include core width, height, thickness and yoke width, and reasonable levels are set to describe their range of variation. For example, the core width and height are set to 3 levels (small, medium, and large), and the thickness and yoke width are set to 2 levels (thin and thick). Combined with the characteristics of the four-variable mixed level, the L18 (33×22) orthogonal table is selected to determine 18 test points as the design scheme. These test points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table, which not only covers the main change trends of the variable levels, but also significantly reduces the 3×3×2×2=36 full factorial tests that are theoretically required. The core size combination corresponding to each design point directly affects the flux density and loss of the transformer, so the target performance (such as loss, efficiency, etc.) is simulated and evaluated through these combinations. Finally, methods such as range analysis and variance analysis are used to evaluate the influence of each design variable to ensure the accuracy of the optimization results and the test efficiency, providing a scientific basis for core optimization.

[0140] In the performance evaluation phase, the study will quantitatively analyze the objective function of each design point. Specifically, the improvement of efficiency will help reduce energy consumption, and the temperature control below 75°C can ensure the stability of the high-frequency transformer; the cost optimization is aimed at manufacturing and material costs to ensure the best performance within the budget, and the improvement of power density can reach 1×10 7 W / m 3 .

[0141] Furthermore, based on the selective non-dominated genetic algorithm, the Pareto frontier relationship analysis is performed on the high-frequency transformer after the optimized design to obtain the optimal design scheme of the high-frequency transformer. In the above process, the fitness of the high-frequency transformer after the optimized design is first evaluated, and the fitness is evaluated with cost, power density and efficiency as the objective function;

[0142] Set the stop criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, stop running;

[0143] The width, height, thickness and yoke width of the core are used as design variables, and design variables with good performance on multiple objectives are selected. Specifically, in the process of Pareto frontier relationship analysis of high-frequency transformers based on selective non-dominated genetic algorithm, "better performing" design variables refer to variables that meet the specific quantitative standards of multi-objective optimization. These standards include: in terms of cost objectives, design variables must make the manufacturing cost of high-frequency transformers lower than the set budget threshold (for example, less than 200 yuan); in terms of power density objectives, design variables must make the power density reach or exceed the set lower limit (for example, not less than 50W / cm 3 ); in terms of efficiency, the design variables must ensure that the efficiency is not less than the set minimum value (for example, not less than 95%). In addition, the selected design variables must meet the conditions of the Pareto optimal solution, that is, in terms of the optimization objectives, relative to other design variables, they must show "non-dominated" characteristics: better than other design variables in at least one objective, and not inferior to any design variables in other objectives. Therefore, "better performance" specifically refers to design variables that meet the constraints of the above multi-objective optimization and are in the Pareto frontier set.

[0144] Furthermore, by selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

[0145] In the selection process, a selective dominance mechanism is introduced. For two chromosomes X1 and X2, if X1 is not inferior to X2 in all objectives and is superior to X2 in at least one objective, then X1 is said to dominate X2. The chromosome with stronger dominance is selected to enter the next generation.

[0146] Among them, genetic operations include crossover operations and mutation operations. Through the crossover operation, those design variables that have an advantage in the dominance relationship are selected for crossover, thereby transmitting advantageous gene information. The crossover operation generates new daughter chromosomes by combining the genes of two parent chromosomes to increase the diversity of the population. One or more crossover points are randomly selected. The parts of the two parent chromosomes after the crossover point are exchanged to generate two daughter chromosomes. For example, the core thickness t of chromosome X1 and chromosome X2 c and core height h c exchange.

[0147] Through mutation operations, those design variables that perform poorly but have potential will be mutated first, thereby improving the diversity and exploration ability of the population. Mutation operations increase the diversity of the population and prevent the algorithm from falling into local optimality by randomly changing certain genes in the chromosome. For example, changing a design variable in a chromosome can generate a new chromosome.

[0148] Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the optimized high-frequency transformer is carried out to obtain the optimal design scheme of the high-frequency transformer.

[0149] Furthermore, it also includes multi-physical field bidirectional coupling simulation analysis of the optimal design scheme of the high-frequency transformer, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

[0150] The heat sources of high-frequency transformers include heat losses in the windings and eddy currents:

[0151]

[0152] Where A, v, J e ,σ,ω are magnetic vector potential, magnetic reluctance, eddy current density, material conductivity and frequency, respectively, and the unit is rad / s.

[0153]

[0154] Among them, ρ v , C p , k and T a are bulk density, specific heat capacity, thermal conductivity and ambient temperature respectively.

[0155]

[0156] Where Q p , u are the point heat source and velocity vector respectively.

[0157]

[0158] Among them, ρ f 、u f , F, and p are the fluid density, velocity, volume force, and pressure, respectively.

[0159] The steady-state multi-physics model can be used to comprehensively analyze the temperature field, electromagnetic field, and stress field inside the high-frequency transformer. The distribution of the temperature field directly affects the magnetic permeability of the core material and the resistance of the winding, so multiple rounds of iterative calculations between the electromagnetic and thermal fields are required to ensure the accuracy of the results.

[0160] Furthermore, in this embodiment, the design optimization of a 1MVA, 10kHz high-frequency transformer is studied as an example.

[0161] Multi-physics simulation and optimization were performed using finite element analysis software. The detailed technical specifications of the high-frequency transformer are shown in Table 1(a). The selected core has a high saturation flux density (1.56T), reasonable core loss (W / m3), and is relatively low cost compared to nanocrystalline cores used in high-frequency power converter magnetic applications. The cost optimization factors are shown in Table 1(b).

[0162] Table 1 (a) HFT system design parameters (design values)

[0163] parameter Numeric Capacity 1MVA <![CDATA[Operating frequency f s > 10kHz Core type U shape <![CDATA[Input / output voltage U i / U o > 5000 / 750V <![CDATA[Input / output current I p / I s > 211 / 235A

[0164] Table 1(b) Cost coefficients of HFT components

[0165] parameter Numeric <![CDATA[Cost coefficient C of nanocrystalline materials core > 500(￥ / kg) <![CDATA[Cost coefficient C of Litz wire Litz > 50(￥ / m)

[0166] In the multi-objective optimization design of high-frequency transformers, if each level of each variable is calculated, a large amount of computer resources and time will be consumed. It is necessary to perform appropriate sample screening. In this embodiment, the Taguchi method is used, and the width l, height h, thickness t, and yoke width d of the core are used as design variables. Through an orthogonal experimental design with four variables and mixed levels, 18 design points are finally determined to cover different combinations of the design space. The selection of each design point is based on the reasonable distribution of each design variable to ensure the representativeness of the test results. Specifically, the size change of the core will directly affect its magnetic flux density and loss.

[0167] In the performance evaluation phase, the study will quantitatively analyze the objective function of each design point. Specifically, the improvement of efficiency will help reduce energy consumption, and the temperature control below 75°C can ensure the stability of the high-frequency transformer; the cost optimization is aimed at manufacturing and material costs to ensure the best performance within the budget, and the improvement of power density can reach 1×10 7 W / m 3 .

[0168] Statistical analysis methods are used to analyze the test data in depth, evaluate the performance of each design point on the objective function, and identify the optimal design point. Finally, the multi-objective design optimization of the high-frequency transformer is achieved by comprehensively considering the mutual influence between the various objectives.

[0169] The design results of the proposed optimization algorithm are shown in Table 2. The simulation results give POS with 18 different core wire sizes, as well as their efficiency values, power density, total cost and number of turns. According to the defined objective function, the three-dimensional graph of POS is shown in Figure 3 shown.

[0170] Table 2 POS results of 1MVA, 10kHz high frequency transformer

[0171]

[0172] The POS data sheet (Table 2) shows the impact of the combination of design variables screened by the Taguchi method on the optimization target. Table 2 shows the significant impact of design variables on cost, which indicates that core size optimization is critical for cost control. However, the high power density design did not significantly increase the cost, indicating that a reasonable design can achieve a balance between power density and cost. The efficiency η is concentrated between 92.84% and 99.45%, and some high power density designs still maintain high efficiency, showing that optimization can maintain high efficiency while increasing power density. The average temperature Tavg is controlled between 51.88℃ and 79.87℃, and the low temperature design shows good thermal management, indicating that the heat load can be effectively controlled by optimizing variable selection.

[0173] The multi-objective optimization results generated by the non-dominated genetic algorithm show the Pareto front relationship of efficiency, power density and cost. Comprehensive analysis found the following rules: First, there is a trade-off between power density and cost. The increase in power density is generally accompanied by an increase in cost, but the design scheme on the Pareto front proves that low costs can still be maintained at some high power densities. Secondly, efficiency and power density can be optimized synergistically. The results of the selective non-dominated genetic algorithm show that the increase in power density will not significantly reduce efficiency, meeting the project's requirements for high efficiency and high power density. Finally, the heat load can be effectively controlled. The POS data shows that the temperature can be controlled within a reasonable range (not exceeding 73°C) under the optimized design, meeting the high heat load requirements. By reasonably selecting design variables such as core width and thickness, good thermal management can be achieved while improving power density.

[0174] The analysis based on POS data and the optimization results of the selective non-dominated genetic algorithm shows that multi-objective optimization of high-frequency transformers can be achieved by optimizing the combination of design variables. Under the requirements of high efficiency, high heat load and high power density, the Pareto frontier design scheme provides guidance for achieving a balance between performance and cost. In the optimization process, the scheme on the Pareto frontier can be given priority to improve power density and efficiency while controlling cost and temperature, providing a feasible solution for the actual design of high-performance high-frequency transformers.

[0175] Figure 4 The left figure is the Pearson correlation coefficient r between the design variables (P1: core width, P2: core height, P3: yoke width, P4: yoke thickness) and the optimization targets (P5: cost, P6: efficiency, P7: power density). By observing, we can clearly see the influence of different design variables on each target parameter.

[0176] Through Figure 4The following potential rules can be summarized from the analysis: Cost is mainly affected by the core width (P1) and height (P2), and cost management can be effectively achieved by reasonably controlling these two variables. There is a synergistic relationship between efficiency and power density, that is, improving efficiency will also increase power density, which means that efficiency can be optimized to achieve higher power density to meet the high efficiency and high power density goals in the project. The width and thickness of the yoke play a key role in thermal load performance. Sensitivity analysis shows that increasing the yoke thickness helps to improve power density, while the yoke width has little effect on power density. Therefore, under the premise of meeting the thermal load and mechanical strength, it is possible to give priority to adjusting the yoke thickness to achieve the best balance between power density and cost.

[0177] In the multi-objective optimization design of high-frequency transformers, the optimization of core width (P1) and core height (P2) has an important impact on cost, efficiency and power density. The core width should be adjusted first to control the cost, and the efficiency and power density should be improved by appropriately increasing the core height. At the same time, the increase in yoke thickness (P4) has a positive effect on power density, which can further meet the high efficiency and high power density requirements of the project. Based on the above analysis, it is finally determined that Scheme 8 in Table 2 is the optimal design scheme for 1MVA high-frequency transformer.

[0178] Embodiment 2

[0179] This embodiment discloses a multi-physics field optimization design system for large-capacity high-frequency transformers;

[0180] like Figure 5 As shown, a multi-physics field optimization design system for a large-capacity high-frequency transformer includes:

[0181] The optimization target building module is configured to: build optimization targets based on power density, efficiency and cost respectively, and set priorities according to application requirements;

[0182] The core optimization module is configured to: introduce the calculation model of core loss and winding power loss, model the core loss based on the generalized Steinmetz equation, and optimize the winding loss using the skin effect and proximity effect;

[0183] The design variables and constraints module is configured to: determine the design variables and constraints, perform sensitivity analysis on the design variables through multi-physics simulation, and determine the design variables that have the greatest impact on power density, efficiency and cost for optimization;

[0184] The optimal solution acquisition module is configured to: establish a steady-state electric-magnetic-thermal-mechanical multi-physics field bidirectional coupling model, simulate the interaction between different physical fields through the finite element analysis method, analyze the temperature field, electromagnetic field and stress field of the core and winding, and optimize the electromagnetic performance and heat dissipation performance of the high-frequency transformer.

[0185] Embodiment 3

[0186] The purpose of this embodiment is to provide a computer-readable storage medium.

[0187] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a multi-physical field optimization design method for a large-capacity high-frequency transformer as described in Example 1.

[0188] Embodiment 4

[0189] The purpose of this embodiment is to provide an electronic device.

[0190] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer as described in Example 1 are implemented.

[0191] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0192] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0193] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A multi-physics field optimization design method for a large-capacity high-frequency transformer, characterized in that: include: According to the application requirements of high-frequency transformers, the optimization targets of high-frequency transformer cost, power density and efficiency are established respectively; Based on the optimization objectives of high-frequency transformer cost, power density and efficiency, the calculation model of core loss and winding loss is introduced to obtain the loss of high-frequency transformer; Based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints, the core size constraint is introduced; Based on the constraint of core size, the Taguchi method is used to optimize the design of the high-frequency transformer. Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the optimized high-frequency transformer is carried out to obtain the optimal design scheme of the high-frequency transformer.

2. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: The optimization goal based on power density is: Where P d is the power density; P0 is the rated power of the high-frequency transformer, V c is the volume of the core, V w is the volume of the Litz wire winding; Cost C t for: C t =MLT×(N p +N s )C w +V c C c Where MLT is the average turn length of the winding, N p 、N s is the number of turns of the primary and secondary windings, C w , C c are the cost coefficients of Litz wire and nanocrystalline core respectively; The efficiency η is: Among them, P t is the total loss of the high frequency transformer.

3. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: The core losses are modeled using a modified generalized Steinmetz equation, where: The improved generalized Steinmetz equation is as follows: In the formula, is the core loss; T is the period; B is the magnetic flux density; α, β, k are the core parameters used for loss calculation, which depend on the type of core used; Since the core loss depends on the flux density and the operating frequency, the optimal flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as: In the formula, B op The optimal flux density required to minimize the power loss of the high-frequency transformer; H c is the coercivity of the core material; k a , K c , k w is the coefficient of the core material used; ΔT a is the temperature change; ρ cu is the resistivity of copper; k u is the window utilization coefficient; k c is a constant related to the eddy current loss of the core material; f s a to the power of a, a is the Steinmetz coefficient, which is determined by the core material; K v is the type of voltage waveform applied to the HFT; f is the operating frequency of the high-frequency transformer; k f is the core stacking factor; ∑VA is the total rated power; A p is the product of the core window area and the core cross-sectional area of ​​the frequency transformer; f s is the switching frequency of the high-frequency transformer; K f is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; K t is the correction factor related to heat conduction.

4. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: The modeling process of the winding loss is specifically as follows: Calculate the AC resistance of the winding: in is the diameter of the Litz wire; l is the number of winding layers; n s is the packing factor; ξ is the number of strands in each Litz wire conductor; d cu is the diameter of the copper conductor, δ s is the skin effect; N is the number of winding turns; Maintain the ratio of the AC to DC resistance of the winding at a specific value, as shown in the following equation: In the formula, r o is the radius of the circular conductor, frequency f s Skin effect under s It is expressed as: The power loss in the primary and secondary windings is expressed as: in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively; Where P winding is the winding loss.

5. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: The core size constraints are: In the formula, h c is the core height; d c is the width of the core yoke; t c is the core thickness; l c is the core width; N s,p is the number of turns of the secondary winding and the number of turns of the primary winding; B sat is the saturation magnetic flux density; h max , l max ,t max ,d max are the maximum height, maximum width, maximum thickness and maximum yoke width allowed by the core; T is the operating temperature of the high-frequency transformer; T max N is the maximum allowable temperature rise of the high-frequency transformer; min and N max Refers to the minimum number of winding turns and the maximum number of winding turns respectively.

6. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: Based on the constraints of core size, the process of optimizing the design of high-frequency transformer using Taguchi method is as follows: The core width, height, thickness and yoke width are used as the design variables of the core size, and the level number is set to describe the range of variation; In combination with the characteristics of the four-variable mixed level, an orthogonal table is selected to determine the test points as the design scheme; the test points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table; The target performance is simulated and evaluated based on the parameter combination, and the influence of each design variable is evaluated by using range analysis and variance analysis methods.

7. A multi-physics field optimization design method for a large-capacity high-frequency transformer as claimed in claim 1, characterized in that: Based on the selective non-dominated genetic algorithm, the Pareto frontier relationship analysis is performed on the high-frequency transformer after the optimized design, and the process of obtaining the optimal design scheme of the high-frequency transformer is as follows: Perform fitness evaluation on the high-frequency transformer after optimization design, wherein the fitness is evaluated with cost, power density and efficiency as objective functions; Set the stop criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, stop running; The width, height, thickness and yoke width of the core are used as design variables, and the design variables that meet the multi-objective optimization criteria are selected; By selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables; As a further technical solution, it also includes performing multi-physical field bidirectional coupling simulation analysis on the scheme, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

8. A multi-physics field optimization design system for large-capacity high-frequency transformers, characterized in that: include: The optimization target building module is configured to: build optimization targets of high-frequency transformer cost, power density and efficiency respectively according to application requirements of the high-frequency transformer; The loss acquisition module is configured to: based on the optimization objectives of high-frequency transformer cost, power density and efficiency, introduce a calculation model of core loss and winding loss to acquire the loss amount of the high-frequency transformer; The constraint establishment module is configured to: introduce a core size constraint based on a maximum feasible size of a high frequency transformer core, electrical constraints, thermal and electromagnetic constraints; The optimal design scheme acquisition module is configured as follows: based on the constraint of the core size, the Taguchi method is used to optimize the design of the high-frequency transformer, and based on the selective non-dominated genetic algorithm, the Pareto frontier relationship analysis is performed on the high-frequency transformer after the optimized design to obtain the optimal design scheme of the high-frequency transformer.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer are implemented as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer are implemented as described in any one of claims 1-7.

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