Dynamic congestion degree optimization equipment magnetic element design method, equipment and medium

Abstract

The invention relates to a dynamic congestion degree optimization equipment magnetic element design method, equipment and a medium, and the method comprises the steps: generating an initial population through random coding based on a constraint range; performing magnetic core loss calculation and transmission magnetic energy calculation on each individual in the initial population; performing non-dominated sorting on the initial population according to a calculation result, dividing the individuals into a non-dominated layer, and calculating the dynamic congestion degree of each individual in the non-dominated layer to obtain a sorting result; and performing iterative updating on the initial population based on a sorting result, when the maximum number of iterations is reached, outputting a non-dominated solution set on the Pareto front, setting a weight vector, calculating a weighted Euclidean distance between the non-dominated solution and an ideal point, and selecting a solution with the minimum weighted Euclidean distance as an optimal magnetic element design scheme. And by introducing a dynamic crowding degree calculation and adaptive sorting strategy, diversity of population distribution and high quality of an optimization result are realized, so that an optimal magnetic element design scheme suitable for different scenes is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-objective optimization design, and in particular to a design method, device and medium for a magnetic element of equipment with dynamic congestion optimization. Background Art

[0002] In the fields of electronic equipment, transformers and power systems, magnetic components (such as cores, inductors and transformers) are key components, and their performance directly affects the efficiency, power transmission capacity and energy loss of the equipment. Traditional magnetic component design mainly relies on experience and trial and error. The combination level of design parameters such as temperature, frequency, waveform, peak value of magnetic flux density and core materials often lacks systematicity and optimization, resulting in high core loss and low efficiency of magnetic energy transmission, which makes it difficult to meet the design requirements of efficient energy transmission and low loss.

[0003] In recent years, with the development of optimization algorithms, designs based on multi-objective optimization methods have gradually been applied to the engineering field. However, although existing multi-objective optimization algorithms (such as the non-dominated sorting genetic algorithm NSGA-II) can effectively handle the conflicting relationships between multiple objectives, they still have some shortcomings, such as: Limitations of crowding calculation: When the crowding distances of multiple individuals are equal, it is impossible to accurately judge the quality of the individuals, which affects the diversity and optimization effect of the population; Uneven population distribution: When solving the Pareto front solution set, the individual distribution may be too concentrated in a certain area, resulting in insufficient coverage of the solution set in the target space; Insufficient convergence: Traditional algorithms are prone to fall into local optimality for high-dimensional complex problems, affecting the quality of the Pareto frontier.

[0004] Therefore, how to construct a magnetic component optimization design model that can balance the minimization of core loss and the maximization of transmitted magnetic energy, and improve the existing algorithm to improve the optimization efficiency, solution set distribution uniformity and convergence performance has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a method, device and medium for designing magnetic components of equipment with dynamic congestion optimization, the purpose of which is to construct a magnetic component optimization design model that can balance the minimization of core loss and the maximization of transmitted magnetic energy, and to improve the existing algorithm to improve the optimization efficiency, solution set distribution uniformity and convergence performance.

[0006] To achieve the above object, the first aspect of the present invention provides a method for designing a magnetic component of an equipment with dynamic crowding optimization, comprising the following steps: Generate the initial population by random coding based on the constraints of temperature, frequency, magnetic flux density peak, waveform, and core material; Calculate the core loss and transmission magnetic energy of each individual in the initial population, obtain the core loss value and transmission magnetic energy value, and record them as the objective function value; According to the objective function value, the initial population is quickly non-dominated sorted, the individuals in the initial population are divided into multiple non-dominated layers, the dynamic crowding degree of each individual in the non-dominated layer is calculated, and the crowding degree sorting result is obtained; Based on the crowding ranking results, the initial population is iteratively updated until the maximum number of iterations is reached; When the maximum number of iterations is reached, the non-dominated solution set on the Pareto front is output; A weight vector is set according to the requirement, and a weighted Euclidean distance between each solution in the non-dominated solution set and the ideal point is calculated; The solution with the smallest weighted Euclidean distance is selected as the optimal magnetic component design solution, and the five-factor parameter combination of temperature, frequency, waveform, magnetic flux density peak and core material is output.

[0007] Furthermore, it also includes: performing polynomial interpolation fitting on the non-dominated solution set on the Pareto front, calculating the curvature based on the second-order derivative of the fitting curve, identifying the inflection point with the largest curvature, and using the five-factor parameter combination corresponding to the inflection point as the optimal magnetic component design solution in the loss mutation sensitive scenario.

[0008] Furthermore, the method of calculating the core loss and the transmitted magnetic energy for each individual in the initial population and recording the objective function value of the individual includes: The decision variables of each individual in the initial population are decoded to obtain the specific temperature, frequency, waveform, peak value of magnetic flux density and core material parameters: According to the decoded parameters, the core loss value is calculated based on the following core loss model: The transmission magnetic energy value is calculated based on the following transmission magnetic energy formula: The core loss value and the transmitted magnetic energy value are recorded as two sub-objectives of the objective function; in, represents the core loss, is the decision variable, Indicates temperature, represents the operating frequency of the magnetic component, Represents the waveform, represents the peak value of magnetic flux density, Indicates the core material, Indicates the transmission of magnetic energy.

[0009] Furthermore, fast non-dominated sorting includes the following methods: Sort all individuals by the objective function value, identify individuals that are not dominated by other individuals to form the first non-dominated layer; Repeatedly identify the non-dominated layer from the remaining individuals until all individuals are assigned.

[0010] Furthermore, the calculation of dynamic congestion includes the following methods: Sort the individuals in the population and arrange the non-boundary individuals in order according to the objective function value; Initialize the initial crowding of each individual, assign the maximum crowding to the boundary individual, and set the left and right crowding of the non-boundary individual to zero initially; Calculate the left crowding degree of the non-boundary individual, where the left crowding degree is determined based on the difference in objective function values ​​between the current individual and its left adjacent individual; Calculate the right crowding degree of the non-boundary individual, where the right crowding degree is determined based on the difference in objective function values ​​between the current individual and its right adjacent individual; The left crowding degree and the right crowding degree are accumulated to obtain the dynamic crowding degree of the current individual; When individuals need to be deleted from the population, the individuals with the least crowding are identified and deleted; Recalculate the dynamic crowding degree of the left and right adjacent individuals of the deleted individual and update the dynamic crowding degree; Based on the updated dynamic crowding degree, the remaining individuals are re-sorted; Repeat the above steps until the required number of solutions is met.

[0011] Further, based on the dynamic congestion ranking results, the following operations are performed until the maximum number of iterations is reached: Individuals with priority in non-dominated hierarchy and large dynamic crowding degree are selected to form the parent population; Perform crossover and mutation operations on the parent population to generate the offspring population; Merge the parent population and the child population to get a new population; Perform fast non-dominated sorting on the new population and sort it according to the dynamic crowding degree, and select the best individuals from them to form the next generation population; Increase the number of iterations. If the number is greater than the maximum number of iterations, terminate the algorithm and output the optimal solution set; otherwise, return to the above steps.

[0012] Further, the method of calculating the weighted Euclidean distance includes: The core loss and transmitted magnetic energy of each solution in the non-dominated solution set are normalized to obtain the standardized values and , where the standardization process is as follows: in, and are the standardized values, corresponding to the standardized results of core loss and transmitted magnetic energy, respectively. Indicates the core loss value corresponding to the alternative magnetic component design scheme, It represents the transmission magnetic energy value corresponding to the alternative magnetic component design scheme, It is the set of values ​​of all alternative magnetic component design schemes in terms of core loss index. It is the set of values ​​of all alternative magnetic component design schemes in terms of transmission magnetic energy index. is the minimum value among all core loss values, that is, the optimal core loss. It is the maximum value of all core loss values, that is, the worst core loss. It is the minimum value among all the transmission magnetic energy values, that is, the worst transmission magnetic energy. It is the maximum value among all the transmission magnetic energy values, that is, the optimal transmission magnetic energy; Calculate the weighted Euclidean distance of the ideal point based on the normalized value and the weight vector:

[0013] in, is the weighted Euclidean distance, is the weight vector, is the standardized target value, that is and , For the ideal point.

[0014] Furthermore, the method for determining the optimal magnetic component design solution in the loss mutation sensitive scenario includes: Obtain the non-dominated solution set on the Pareto front, sort it according to the two objectives of core loss and transmitted magnetic energy, and obtain the discrete points in the solution set; Perform polynomial interpolation fitting on the discrete points on the Pareto front to obtain a fitting curve; Calculate the second derivative of the fitting curve to obtain the curvature of each discrete point; The five-factor parameter combination corresponding to the inflection point with the maximum output curvature is taken as the optimal magnetic component design solution.

[0015] To achieve the above objectives, the second aspect of the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to implement the steps of the method for designing magnetic components for equipment with dynamic congestion optimization when executing a computer program stored in the memory.

[0016] To achieve the above objectives, the third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for designing equipment magnetic components with dynamic congestion optimization are executed.

[0017] Beneficial effects of the present invention: Compared with the prior art, the present invention provides a design method, device and medium for equipment magnetic components with dynamic crowding optimization. By constructing a multi-objective optimization design model with the goal of minimizing core loss and maximizing transmitted magnetic energy, combined with the constraints of five factors including temperature, frequency, waveform, magnetic flux density peak and core material, the traditional non-dominated sorting genetic algorithm (NSGA-II) is improved, and a dynamic crowding and adaptive sorting strategy is proposed. This technical solution effectively solves the problem that individuals cannot be reasonably selected when the crowding distance is the same by dynamically updating the crowding of individuals; the introduction of a dynamic crowding adaptive sorting method improves the uniformity and diversity of population distribution; the convergence and coverage of the solution set are enhanced by an improved fast non-dominated sorting and elite retention strategy, thereby obtaining a set of non-dominated solution sets with excellent performance on the Pareto front. Finally, by introducing the weighted improved ideal point method and the curvature inflection point method, combined with the needs of different application scenarios, the optimal magnetic component design solution is selected from the Pareto front solution set, realizing the optimization design needs of magnetic components in multiple scenarios such as loss aversion, loss tolerance, loss acceptance and loss mutation sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0019] Figure 1 It is a flow chart of a method for designing equipment magnetic components with dynamic crowding optimization disclosed in an embodiment of the present invention.

[0020] Figure 2 It is a non-dominated solution set graph of a DNSGA-II algorithm disclosed in an embodiment of the present invention.

[0021] Figure 3 It is a relationship diagram between a magnetic core loss and transmitted magnetic energy disclosed in an embodiment of the present invention.

[0022] Figure 4 It is a distribution diagram of core loss and transmitted magnetic energy under different temperature conditions disclosed in an embodiment of the present invention.

[0023] Figure 5 This is a diagram showing the influence of frequency on magnetic core loss and transmitted magnetic energy disclosed in an embodiment of the present invention.

[0024] Figure 6This is a diagram showing the influence of different waveforms on core loss and transmitted magnetic energy disclosed in an embodiment of the present invention.

[0025] Figure 7 This is a diagram showing the influence of a peak value of magnetic flux density on magnetic core loss and transmitted magnetic energy disclosed in an embodiment of the present invention.

[0026] Figure 8 This is a diagram showing the influence of different core materials on core loss and transmitted magnetic energy disclosed in an embodiment of the present invention.

[0027] Fig. 9 It is a Pareto front solution set comparison diagram of a DNSGA-II algorithm and an NSGA-II algorithm disclosed in an embodiment of the present invention.

[0028] Fig.10 It is a radar chart comparing indicators of a DNSGA-II algorithm and an NSGA-II algorithm disclosed in an embodiment of the present invention.

[0029] Fig.11 It is a diagram for selecting optimal magnetic component design schemes for loss-averse, loss-tolerant, and loss-accepting types disclosed in an embodiment of the present invention.

[0030] Fig.12 It is a diagram for selecting an optimal design scheme for a loss mutation-sensitive magnetic component disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] According to an embodiment of the present invention, it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the following production method, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] like Figure 1 As shown, the present invention provides a method for designing equipment magnetic components with dynamic crowding optimization, comprising the following steps: Step S100, generating an initial population by random coding based on the constraint range of temperature, frequency, magnetic flux density peak value, waveform, and magnetic core material; Step S200, calculating the core loss and the transmitted magnetic energy for each individual in the initial population, obtaining the core loss value and the transmitted magnetic energy value, and recording them as the objective function value; Step S300, performing a fast non-dominated sort on the initial population according to the objective function value, dividing the individuals in the initial population into multiple non-dominated layers, calculating the dynamic crowding degree of each individual in the non-dominated layer, and obtaining a crowding degree sorting result; Step S400: based on the crowding ranking result, iteratively update the initial population until the maximum number of iterations is reached; Step S500: When the maximum number of iterations is reached, output the non-dominated solution set on the Pareto front.

[0034] Step S600, setting a weight vector according to requirements, and calculating a weighted Euclidean distance between each solution in the non-dominated solution set and the ideal point; Step S700: Select the solution with the smallest weighted Euclidean distance as the optimal magnetic element design solution, and output the five-factor parameter combination of temperature, frequency, waveform, magnetic flux density peak value and magnetic core material.

[0035] In this embodiment, as described in step S100 above, based on the constraint range of temperature, frequency, magnetic flux density peak value, waveform, and magnetic core material, the initial population is generated by random coding, including the following contents: First, each design variable is encoded according to the preset constraint range, and the mapping relationship between continuous floating point numbers and decision vectors is used to implement the encoding rules, where temperature The value range is ,frequency The value range is , peak flux density The value range is , waveform The value range is a discrete set {1, 2, 3}, and the core material The value range of is a discrete set {1, 2, 3, 4}. Then, based on the random floating point number generation method, a , , , and The random encoding value of , maps the floating point number to the corresponding physical parameter value through the decoding formula, ensuring that the generated parameter combination meets the constraints. Finally, the generated population size is The initial population provides basic data for the subsequent optimization process.

[0036] Genetic coding ,in They are all continuous floating point numbers in (0,1), and their mapping relationship with the elements in the decision vector is as follows: in, Indicates the temperature of the magnetic component, and are the maximum and minimum values ​​of temperature in the experimental data, The encoded value representing the temperature in the range [0, 1] can be mapped to the actual value of the temperature; Indicates frequency, and are the maximum and minimum values ​​of the frequency in the experimental data, Represents the encoded value of frequency in the range [0, 1]. The actual value of the frequency can be obtained through this value mapping; represents the peak value of magnetic flux density, and are the maximum and minimum values ​​of the peak magnetic flux density in the experimental data, Indicates the encoding value of the peak value of magnetic flux density in the range [0, 1]; Represents the waveform, which can be one of three waveforms: sine wave, triangle wave or trapezoidal wave, The code value representing the waveform; is the type of core material, Code value indicating the core material.

[0037] In this embodiment, as described in step S200 above, the core loss and transmission magnetic energy are calculated for each individual in the initial population, and the objective function value of the individual is recorded, which specifically includes the following contents: First, the decision variables of the individuals in the initial population are decoded. , get the temperature ,frequency , peak flux density , Waveform and core materials Secondly, based on the core loss model , substitute the decoded parameters into the core loss calculation formula to calculate the individual core loss value Then, according to the magnetic energy transfer formula , the frequency and peak flux density Substitute and calculate the individual transmission magnetic energy value Finally, the core loss value and the transmitted magnetic energy value of each individual are recorded as two sub-goals of the objective function, respectively, to provide basic data for the subsequent non-dominated sorting and dynamic crowding calculation.

[0038] In this embodiment, as described in step S300 above, for the optimization problem of two objectives such as the optimization design model of the magnetic component, some multi-objective evolutionary algorithms can be used to find multiple Pareto optimal solutions, such as the nondominated sorting genetic algorithm (NSGA). In view of the three problems of NSGA, namely, the high time complexity of nondominated sorting, the lack of elite retention strategy, and the need to manually specify shared parameters to ensure population diversity, NSGA-I has three key improvements: fast nondominated sorting, elite retention strategy, and parameter-free niche operator, which have better performance in terms of solution quality and convergence.

[0039] The NSGA-I algorithm stratifies the population by the dominance relationship between individuals. First, a set of non-dominated individuals is identified as the first layer. Subsequently, non-dominated individuals are further selected from the remaining individuals to form the second layer, and this process continues until all individuals are stratified. The set of individuals in each layer represents the solution set that is not dominated by other individuals in the current iteration. Within each layer, the algorithm further calculates the crowding distance of the individuals, which is an indicator of the distribution density of individuals in the target space. When selecting operations, NSGA-II first compares the level and crowding distance of the individuals to determine the optimal set of individuals to form a new parent population. Next, the algorithm uses the crossover and mutation operations of the genetic algorithm to generate a new offspring population. After merging the newly generated offspring population with the existing parent population, genetic operations, including selection, crossover, and mutation, are performed to generate a new generation of populations. This process is iterated until the preset population size or other termination conditions are met. This method of calculating stratification and crowding distance, as well as the selection mechanism based on these indicators, is the key to the NSGA-II algorithm's ability to effectively handle multi-objective optimization problems. In this way, the algorithm can simultaneously consider the superiority and diversity of solutions, thereby exploring a set of balanced Pareto optimal solutions in the solution space.

[0040] In order to optimize the diversity of the population, this embodiment innovates the calculation method of the crowding degree and introduces the concept of dynamic crowding degree. This method solves the problem of difficulty in determining which individual should be deleted when the crowding degree is the same. Specifically, when an individual needs to be removed from the population, the solution with the lowest crowding degree is first identified and deleted. Subsequently, the crowding degree of the neighboring individuals is updated to ensure that the distribution of the remaining individuals in the population is more uniform, thereby maintaining the diversity of the population.

[0041] In the application of the NSGA-II algorithm, when two individuals have the same crowding distance, the traditional crowding measurement method cannot clearly indicate which individual should be given priority when performing individual deletion operations. In this case, the algorithm faces selection difficulties because there is a lack of a clear standard to guide the individual elimination decision. Take the 6 individuals in Table 1 below as an example: Table 1 Crowding distance table

[0042] Table 1 shows the crowding distance values ​​calculated for the six individuals under a single objective. According to the criteria of the NSGA-I algorithm, the crowding degree is calculated as follows: in, Represents an individual The degree of congestion; and Respectively represent individuals Neighboring individuals in the target space, Represents an individual, Represents the target space. This formula determines the priority of individuals by calculating the crowding degree of each individual. Individuals with smaller crowding degrees will be retained, while individuals with larger crowding degrees may be eliminated to enhance the diversity of solutions. When faced with the need to delete individuals based on crowding degrees, and the crowding distances of multiple individuals, the algorithm usually cannot determine which individual should be deleted. In this case, the NSGA-II algorithm may randomly select an individual for deletion.

[0043] In order to avoid potential problems caused by this randomness, this embodiment introduces a new left and right crowding index. This method first initializes the crowding of each individual to ensure that the crowding of the boundary nodes is given the maximum value to ensure that they can be retained to the next generation. For non-boundary nodes, the left crowding index is introduced. and right congestion The concept is initially set to 0.

[0044] After sorting the sub-objective function values, the left and right crowding of each non-boundary node are calculated in turn. This method can more accurately evaluate the crowding of each individual, thereby providing a more reasonable individual selection mechanism when facing equal crowding distances. Finally, based on the calculated left and right crowding, the crowding of each node can be determined, so that more informed decisions can be made in the deletion operation. This improvement not only improves the selection efficiency of the algorithm when dealing with individuals with the same crowding, but also enhances the algorithm's protection of population diversity by dynamically updating the crowding, making the final solution set more dispersed in the target space, which helps to explore a wider solution space.

[0045] In the NSGA-Ⅱ algorithm, the Pareto stratification and crowding of individuals are two key indicators for selecting and eliminating individuals. Within the same Pareto stratification, crowding is particularly important because it determines whether individuals are retained or eliminated. However, experimental analysis shows that there are certain limitations in using a fixed crowding sorting method. When a large number of individuals with low crowding are gathered in a certain area, the fixed crowding sorting strategy may cause all individuals in the area to be eliminated at once, thereby reducing the diversity of the final Pareto solution set.

[0046] In practical applications, when an individual is eliminated, the crowding of its neighboring individuals will also change. The fixed crowding ranking method ignores this change and may further reduce the diversity of the solution set.

[0047] In order to solve this shortcoming of the fixed crowding ranking method, this embodiment improves the NSGAI algorithm, introduces a crowding adaptive ranking strategy, and calls the improved NSGA-I algorithm the DNSGA-II algorithm. When the algorithm needs to eliminate the individual with the smallest crowding from the Fnth layer of the population, the DNSGA-II algorithm will take the following steps: S301, using crowding adaptive sorting strategy to eliminate individuals with the smallest crowding; S302: Record the position of the eliminated solution .

[0048] S303, recalculate the and The congestion of the solution.

[0049] S304: Re-sort according to the updated congestion, and continue to eliminate solutions with the smallest congestion until the number of solutions in the solution set meets the requirement.

[0050] Through this dynamic sorting strategy of congestion, the DNSGA-I algorithm can more effectively maintain the diversity of the solution set and ensure that the final Pareto solution set is more evenly distributed in the target space, thereby improving the optimization effect of the algorithm.

[0051] In this embodiment, as described in step S400 above, the process of the DNSGA-I algorithm is as follows: S401, initialize the population. Randomly generate a population of size The initial population , . Perform function evaluation on each individual, and perform fast non-dominated sorting and dynamic crowding calculation on the individuals in the population in turn.

[0052] S402, select the parent population. The order of the layers in which the individuals are located is compared with the crowding distance, and the best individuals are selected to form a new parent population.

[0053] S403, generate offspring population. Perform crossover and mutation on the parent generation to generate a new offspring population .

[0054] S404, mix the parent and offspring populations to form a new population .

[0055] S405, selection. Select excellent individuals to join the population based on the number of layers of the intermediate population fast non-dominated sorting and the dynamic crowding sorting method used in this paper. .

[0056] S406, termination condition. ,like If it is greater than the maximum number of iterations MaxGen, the algorithm terminates and The non-dominated solution set is the Pareto optimal solution set; if If it is less than the maximum number of iterations MaxGen, the process returns to step S402.

[0057] In this embodiment, as described in the above steps S500 to S700, the DNSGA-Ⅱ algorithm is used to solve the magnetic component optimization design model, and the parameters are set as follows: population size is 100, the maximum number of iterations MaxGen is 1000, the crossover probability Cross is 0.8, and the mutation probability MU is 0.3. When the maximum number of iterations is reached, the number of non-dominated solutions on the Pareto front is 100. Then, the solutions that do not conform to the actual situation on the Pareto front are eliminated, that is, the non-dominated solutions with core loss less than 0, and the number of non-dominated solutions is obtained. is 93, and the non-dominated solution set of DNSGA-Ⅱ algorithm is as follows Figure 2 It can be noted that due to the conflict between the two sub-goals in the magnetic component optimization design model, it is impossible to achieve the optimality at the same time. It can only be optimized towards the optimal direction through reasonable allocation. The optimization effect of core loss and transmission magnetic energy on the non-dominated solution set is plotted as follows Figure 3-Figure 8 ,in, Figure 3 The relationship between core loss and transmitted magnetic energy is shown, showing the Pareto frontier between the two, that is, the trend of mutual restraint in the optimization process; Figure 4 The distribution of core loss and transmitted magnetic energy under different temperature conditions is shown. The color represents the temperature value, and the effect of temperature on the optimization solution can be observed. Figure 5 Responds to frequency The impact on core loss and transmitted magnetic energy. The lighter the color, the higher the frequency. This shows that high frequency may increase the transmitted magnetic energy but increase the core loss at the same time. Figure 6The influence of different waveforms on core loss and transmitted magnetic energy is shown. The colors represent the waveform categories, and the influence trend of different waveforms on the optimization target can be analyzed. Figure 7 The effect of the peak value of magnetic flux density on the core loss and the transmitted magnetic energy is shown. The color represents the magnetic flux density, indicating that the increase of magnetic flux density may help to improve the transmitted magnetic energy. Figure 8 The effect of different core materials on core loss and transmitted magnetic energy is shown. Colors represent different core materials, and the performance differences of different materials on the optimization target can be observed.

[0058] The multi-objective optimization algorithm obtains the optimal solution by continuously converging the Pareto solution set, and maintains strong diversity. In order to verify the advantages of the DNSGA-Ⅱ algorithm proposed in this embodiment, this embodiment is compared with the NSGA-I algorithm. The test parameters used are consistent with the DNSGA-I algorithm proposed in this embodiment. The comparison of the Pareto frontier solution sets of the DNSGA-Ⅱ algorithm and the NSGA-II algorithm is obtained. Fig. 9 .

[0059] The five indicators, namely, the inverse generation distance evaluation index (IGD), the hypervolume index (HV), the set coverage index (SC), the spacing index (SM) and the Schott distribution index (SSPread), are selected to evaluate the solution effects of the DNSGA-I algorithm and the NSGA-II algorithm respectively. The specific introduction of these five indicators is as follows: (1) Anti-generational distance evaluation index The Inverted Generational Distance (IGD) is an important tool for measuring algorithm performance. IGD evaluates the convergence and distribution of the algorithm by calculating the sum of the shortest distances between individuals on the Pareto surface and the Pareto optimal solution set generated by the algorithm. It is as follows: in, is a set of individuals distributed on the Pareto surface, is the number of individuals distributed on the Pareto surface, is the Pareto optimal solution set, Pareto surface Medium To the Pareto optimal solution set The minimum Euclidean distance of . In general, the smaller the IGD, the better the convergence and distribution performance of the algorithm.

[0060] (2) Hypervolume index Hypervolume (HV) is a performance evaluation metric widely used in multi-objective optimization. It measures the volume of the hypercube formed by the non-dominated solution set generated by the algorithm and a reference point in the target space. This metric can simultaneously evaluate the convergence and diversity of the solution set, that is, how close the solution set is to the true Pareto frontier and how the solution is distributed. The calculation formula is as follows: in, is a non-dominated solution set, is a super volume element, Essentially, it calculates the sum of the volumes of the hypercubes formed by all the points in the Pareto solution set and the reference point. In this embodiment, the reference point is selected as ,in It represents the maximum value of TME index in the solution set obtained by DNSGA-Ⅱ algorithm.

[0061] (3) Set coverage index Set Coverage (SC) is a binary indicator used to compare the coverage relationship between two solution sets A and B. It measures the degree to which the solutions in one solution set are dominated by the solutions in another solution set. Specifically, SC(A, B) represents the proportion of solutions in B that are dominated by at least one solution in A to the number of solutions in B. If the value of SC(A, B) is 1, it means that all solutions in B are dominated by at least one solution in A; if the value is 0, it means that no solution in B is dominated by the solution in A. The SC indicator can reflect the coverage ability of the solution set B generated by the algorithm relative to the reference solution set A. The smaller the value, the better the solution set B. In this embodiment, B is the solution set generated by the corresponding algorithm (NSGA-Ⅱ or DNSGA-II), and A is the solution set generated by the DNSGA-Ⅱ algorithm.

[0062] (4) Spacing index Spacing Metric (SM) is an indicator used to evaluate the uniformity of the distribution of non-dominated solution sets generated by multi-objective optimization algorithms. It measures the distribution of the solution set in the objective space by calculating the distance between each solution in the solution set and its nearest neighbor solution. If these distances are equal, it means that the solution set is evenly distributed on the Pareto frontier, and the value of Spacing Metric will be smaller, indicating that the algorithm performs well in terms of diversity. Conversely, if the distance difference is large, it means that the distribution in the solution set is uneven, and the value of Spacing Metric will be larger, indicating that the algorithm is insufficient in covering the entire Pareto frontier. It is worth noting that SM is particularly suitable for optimization problems with two objectives, and may not be effective when there are more than two objectives. The calculation formula is as follows: in, represents the Euclidean distance between two consecutive Pareto fronts, express The arithmetic mean of represents the Euclidean distance between the actual Pareto front and the ideal Pareto front, represents the number of solutions on the actual Pareto front, Represents the number of solutions in the non-dominated solution set. In general, the smaller SM is, the more uniform the Pareto front distribution obtained by the algorithm is.

[0063] (5) Schott distribution index Schott's Spread (SSpread) is an indicator that measures the uniformity of the distribution of non-dominated solutions generated by a multi-objective optimization algorithm. It is achieved by calculating the standard deviation of the distance between each solution in the solution set and its nearest neighbor solution. pread ) is smaller, indicating that the distribution of the solution set is more uniform. The calculation formula is as follows: in, is the total number of solutions in the solution set, No. Solved The distance between the nearest neighbor solutions in Yes all The average value of .

[0064] The results of these five evaluation indicators are shown in Table 2 and Fig.10 shown.

[0065] Table 2 Comparison of NSGA-I and DNSGA-II algorithm indicators

[0066] * To avoid values ​​that are too large to be displayed, HV, SC and SS pread The indicators were normalized.

[0067] The smaller the above five indicators are, the better. It can be found that compared with the NSGA-I algorithm, the DNSGA-II algorithm is significantly better in the four indicators of inverse generation distance evaluation index (IGD), hypervolume index (HV), set coverage index (SC) and spacing index (SM), and is only worse in the Schott distribution index (SSPread), which further confirms that the DNSGA-I algorithm has stronger convergence, diversity, uniformity and robustness.

[0068] In order to achieve the two sub-goals of minimizing core loss and maximizing transmitted magnetic energy, it is necessary to select the optimal conditions from the Pareto solution set according to different solution requirements. This embodiment retains multiple Pareto solutions for different preference scenarios based on the improved ideal point method, and gives corresponding optimal magnetic component design solutions respectively, further expanding the multi-objective optimization method into a multi-modal multi-objective optimization method. Its significance lies in: (1) It can meet the magnetic energy transmission requirements under different preset preference scenarios; (2) It is conducive to revealing certain potential characteristics in the application process of magnetic energy transmission; (3) It is convenient to quickly match the modified optimal magnetic component design solution for new scenarios and solve the dynamic adaptive optimization problem; (4) It can increase the possibility of finding a more robust optimal magnetic component design solution.

[0069] Assume that the Pareto front is ,in is the final population size (obtained earlier ), alternative magnetic component design The corresponding core loss and transmitted magnetic energy are and ,and Yes and .

[0070] First, the two indicators are standardized to have the same magnitude. The core loss is a cost indicator, the smaller the better. The standardization process is as follows. The transmission magnetic energy is a benefit indicator, the larger the better. The standardization process is as follows: in, and are the standardized values, corresponding to the standardized results of core loss and transmitted magnetic energy, respectively. Indicates the core loss value corresponding to the alternative magnetic component design scheme, It represents the transmission magnetic energy value corresponding to the alternative magnetic component design scheme, It is the set of values ​​of all alternative magnetic component design schemes in terms of core loss index. It is the set of values ​​of all alternative magnetic component design schemes in terms of transmission magnetic energy index. is the minimum value among all core loss values, that is, the optimal core loss. It is the maximum value of all core loss values, that is, the worst core loss. It is the minimum value among all the transmission magnetic energy values, that is, the worst transmission magnetic energy. It is the maximum value among all the transmission magnetic energy values, that is, the optimal transmission magnetic energy; Set ideal point is the set of idealized values ​​of all indicators. In the general ideal point method, according to the Euclidean distance formula, the alternative magnetic component design scheme is calculated. The distances from the ideal point are as follows: Among them, the two indicators of standardized core loss and transmitted magnetic energy are both cost-oriented indicators, the lower the better, so Finally, all the alternative magnetic component design schemes are sorted according to the calculated ideal distance. The furthest alternative magnetic design It is the most ideal choice.

[0071] This embodiment adds a weight vector based on different preference scenarios. ,satisfy and ,in and represent the preference weight coefficients of core loss and transmitted magnetic energy respectively, then the design scheme of alternative magnetic components in the improved ideal point method is The distances from the ideal point are as follows: in, is the weighted Euclidean distance, is the weight vector, is the standardized target value, that is and , is the ideal point; Different weight coefficients and regularization parameters are assigned to the ideal point method according to the differences in the preferences for minimizing core loss and maximizing transmitted magnetic energy in different scenarios. This section first considers the following three application scenarios: (1) Loss-averse scenario. In applications with high energy efficiency requirements, such as LED lighting power supplies or mobile device chargers, minimizing core loss is the primary consideration in the design. Because these devices usually have strict requirements on thermal management and efficiency, excessive core loss will lead to energy waste and increase the burden on the cooling system. In this case, the weight vector .

[0072] (2) Loss-tolerant scenario. For high-power transmission applications, such as electric vehicle inverters or solar inverters, the priority of transmitting magnetic energy may be higher. These applications need to process and transmit a large amount of energy in a short time, so the core material needs to be able to support high-power magnetic energy transfer, even if this may mean a slight increase in core losses. At this time, the weight vector .

[0073] (3) Loss acceptance scenario. For applications with short expected usage time or intermittent use, such as handheld power tools or temporary power supplies, a certain degree of core loss may be accepted. In these cases, users may be more concerned about the magnetic energy transmission efficiency of the device to reduce energy cost expenditures rather than long-term energy efficiency performance. In this case, the weight vector .

[0074] In addition, in practical applications, if the existing transmission magnetic energy efficiency can already meet current needs well, but it is hoped to slightly increase the transmission magnetic energy without causing a significant mutation in the core loss, this embodiment names this application scenario as a loss mutation sensitive scenario.

[0075] For scenarios that are sensitive to sudden loss changes, the goal is essentially to find the magnetic component design solution that corresponds to the point with the greatest curvature (i.e., the point with the maximum curvature) on the Pareto front of "core loss-transmitted magnetic energy". This point is represented by a sudden change in trend on the curve, such as from rapid growth to slow growth, or vice versa. In a continuous sequence, the curvature can be calculated by a variety of methods, including the angle method, Menger curvature, or exponentially weighted moving average (EWMA). For non-uniform discrete sequences such as the Pareto front of "core loss-transmitted magnetic energy", it is first necessary to fit them using the polynomial interpolation method, and then uniformly sample the points on the fitting curve according to the core loss to obtain an equally spaced uniform discrete sequence. Finally, the curvature of each point on the Pareto front is calculated based on the quadratic derivative calculation formula of the uniform discrete sequence, and the point with the largest curvature is found as the inflection point.

[0076] in, Represents a function At the point A finite difference approximation of the second-order derivative (i.e., curvature) at the discrete point The curvature at , that is, the changing trend of the point; Represents the function value of the previous point; Represents the function value of the next point; represents the function value at the current point; it can be understood that the calculation result of this formula represents the curvature, which is used to measure the curvature at Trend changes at: if , indicating that the point is a local minimum (concave, the curve bends upward); if , indicating that the point is the local highest point (convex, the curve bends downward); if A large absolute value indicates that the change trend of the point is steep, that is, the curvature is large.

[0077] Based on the above method, the optimal magnetic component target achievement effects under four scenarios of loss aversion, loss tolerance, loss acceptance and loss mutation sensitivity are obtained as shown in Table 3 and Fig.11 , Fig.12 shown.

[0078] Table 3 Target achievement of optimal magnetic components in different scenarios Depend on Fig.11 It can be noticed that as the weight of the three scenarios of "loss aversion type - loss tolerance type - loss acceptance type" for core loss continues to decrease and the weight of the weight of the transmitted magnetic energy continues to increase, the corresponding optimal magnetic component target achievement effect moves toward the upper right corner, and the core loss and transmitted magnetic energy increase. At this time, the optimal magnetic component design solutions for the four scenarios of loss aversion, loss tolerance, loss acceptance and loss mutation sensitivity are obtained, as shown in Table 4: Table 4 Optimal magnetic component design solutions for different scenarios

[0079] In summary, the method of this embodiment establishes an optimization design model for magnetic components, and gives 0-1 continuous coding rules for five conditions: temperature, frequency, waveform, magnetic flux density peak and core material; then, on the basis of the NSGA-II multi-objective optimization algorithm, the concepts of neighboring crowding and dynamic crowding are introduced, an improved DNSGA-Ⅱ algorithm is proposed, and the solution process of the algorithm is given; then, the DNSGA-Ⅱ algorithm is run under the parameter conditions of population size N of 100, maximum number of iterations MaxGen of 1000, crossover probability Cross of 0.8, and mutation probability MU of 0.3. After simple positive value filtering, a set of (93) non-dominated solution sets on the Pareto front are obtained, and compared with the NSGA-II By comparing the two algorithms, it was found that the DNSGA-II algorithm was significantly better than the NSGA-Ⅱ algorithm in terms of the inverse generation distance evaluation index (IGD), hypervolume index (HV), set coverage index (SC) and spacing index (SM), which further confirmed that the DNSGA-II algorithm has stronger convergence, diversity, uniformity and robustness; finally, based on the improved ideal point method and inflection point method with weights, the optimal magnetic component design schemes under four scenarios: loss aversion, loss tolerance, loss acceptance and loss mutation sensitivity were obtained. In the order of loss mutation sensitivity, loss aversion, loss tolerance and loss acceptance, the core loss and transmission magnetic energy of their respective optimal magnetic component design schemes are improved successively.

[0080] According to another aspect of an embodiment of the present application, there is further provided an electronic device, including a processor and a memory, wherein the processor is configured to implement the steps of the method when executing a computer program stored in the memory.

[0081] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0083] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0085] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for designing equipment magnetic components with dynamic crowding optimization, characterized in that: The steps include: Generate the initial population by random coding based on the constraints of temperature, frequency, magnetic flux density peak, waveform, and core material; Calculate the core loss and transmission magnetic energy of each individual in the initial population, obtain the core loss value and transmission magnetic energy value, and record them as the objective function value; According to the objective function value, the initial population is quickly non-dominated sorted, the individuals in the initial population are divided into multiple non-dominated layers, the dynamic crowding degree of each individual in the non-dominated layer is calculated, and the crowding degree sorting result is obtained; Based on the crowding ranking results, the initial population is iteratively updated until the maximum number of iterations is reached; When the maximum number of iterations is reached, the non-dominated solution set on the Pareto front is output; A weight vector is set according to the requirement, and a weighted Euclidean distance between each solution in the non-dominated solution set and the ideal point is calculated; The solution with the smallest weighted Euclidean distance is selected as the optimal magnetic component design solution, and the five-factor parameter combination of temperature, frequency, waveform, magnetic flux density peak and core material is output.

2. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: Also includes: Polynomial interpolation fitting is performed on the non-dominated solution set on the Pareto front, and the curvature is calculated based on the second-order derivative of the fitting curve. The inflection point with the largest curvature is identified, and the five-factor parameter combination corresponding to the inflection point is used as the optimal magnetic component design scheme for loss mutation sensitive scenarios.

3. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: The method of calculating the core loss and the transmitted magnetic energy for each individual in the initial population, obtaining the core loss value and the transmitted magnetic energy value, and recording them as the objective function value includes: The decision variables of each individual in the initial population are decoded to obtain the specific temperature, frequency, waveform, peak value of magnetic flux density and core material parameters: According to the decoded parameters, the core loss value is calculated based on the following core loss model: The transmission magnetic energy value is calculated based on the following transmission magnetic energy formula: The core loss value and the transmitted magnetic energy value are recorded as two sub-objectives of the objective function value; in, represents the core loss, is the mathematical model used to calculate the core loss, which represents the core loss and decision variables The relationship between is the decision variable, Indicates temperature, represents the operating frequency of the magnetic component, Represents the waveform, represents the peak value of magnetic flux density, Indicates the core material, Indicates the transmission of magnetic energy.

4. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: Fast non-dominated sorting includes the following methods: Sort all individuals by the objective function value, identify individuals that are not dominated by other individuals to form the first non-dominated layer; Repeatedly identify the non-dominated layer from the remaining individuals until all individuals are assigned.

5. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: The calculation of dynamic congestion includes the following methods: Sort the individuals in the population and arrange the non-boundary individuals in order according to the objective function value; Initialize the initial crowding of each individual, assign the maximum crowding to the boundary individual, and set the left and right crowding of the non-boundary individual to zero initially; Calculate the left crowding degree of the non-boundary individual, where the left crowding degree is determined based on the difference in objective function values ​​between the current individual and its left adjacent individual; Calculate the right crowding degree of the non-boundary individual, where the right crowding degree is determined based on the difference in objective function values ​​between the current individual and its right adjacent individual; The left crowding degree and the right crowding degree are accumulated to obtain the dynamic crowding degree of the current individual; When individuals need to be deleted from the population, the individuals with the smallest dynamic crowding are identified and deleted; Recalculate the dynamic crowding degree of the left and right adjacent individuals of the deleted individual and update the dynamic crowding degree; Based on the updated dynamic crowding degree, the remaining individuals are re-sorted; Repeat the above steps until the required number of solutions is met.

6. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: Based on the crowding sorting results, the following operations are performed until the maximum number of iterations is reached: Individuals with priority in non-dominated hierarchy and large dynamic crowding degree are selected to form the parent population; Perform crossover and mutation operations on the parent population to generate the offspring population; Merge the parent population and the child population to get a new population; Perform fast non-dominated sorting on the new population and sort it according to the dynamic crowding degree, and select the best individuals from them to form the next generation population; Increase the number of iterations. If the number is greater than the maximum number of iterations, terminate the algorithm and output the optimal solution set; otherwise, return to the above steps.

7. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 1, characterized in that: Methods for calculating weighted Euclidean distance include: The core loss and transmitted magnetic energy of each solution in the non-dominated solution set are normalized to obtain the standardized values and , where the standardization process is as follows: in, and are the standardized values, corresponding to the standardized results of core loss and transmitted magnetic energy, respectively. Indicates the core loss value corresponding to the alternative magnetic component design scheme, It represents the transmission magnetic energy value corresponding to the alternative magnetic component design scheme, It is the set of values ​​of all alternative magnetic component design schemes in terms of core loss index. It is the set of values ​​of all alternative magnetic component design schemes in terms of transmission magnetic energy index. is the minimum value among all core loss values, that is, the optimal core loss. It is the maximum value of all core loss values, that is, the worst core loss. It is the minimum value among all the transmission magnetic energy values, that is, the worst transmission magnetic energy. It is the maximum value among all the transmission magnetic energy values, that is, the optimal transmission magnetic energy; The weighted Euclidean distance between each solution and the ideal point in the non-dominated solution set is calculated based on the standardized value and the weight vector: in, is the weighted Euclidean distance, is the weight vector, is the standardized target value, that is and , For the ideal point.

8. The method for designing magnetic components for equipment with dynamic congestion optimization according to claim 2, characterized in that: Methods for determining the optimal magnetic component design for loss-susceptible scenarios include: Obtain the non-dominated solution set on the Pareto front, sort it according to the two objectives of core loss and transmitted magnetic energy, and obtain the discrete points in the solution set; Perform polynomial interpolation fitting on the discrete points on the Pareto front to obtain a fitting curve; Calculate the second derivative of the fitting curve to obtain the curvature of each discrete point; The five-factor parameter combination corresponding to the inflection point with the maximum output curvature is taken as the optimal magnetic component design solution.

9. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the processor is used to implement the steps of the method for designing magnetic components of equipment with dynamic crowding optimization as claimed in any one of claims 1 to 8 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for designing magnetic components for equipment with dynamic crowding optimization according to any one of claims 1 to 8 are executed.

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