Electric reactor radiation type iron core section optimization method and system based on genetic algorithm and dynamic programming algorithm
By using a hybrid coding method combining genetic algorithms and dynamic programming algorithms, the shortcomings of global parameter search and local optimization in reactor design are addressed. This method achieves the global optimal solution for the radial core cross-section of the reactor, improves material utilization and magnetic circuit permeability, reduces losses, and achieves the best balance between energy efficiency and cost.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-13
- Publication Date
- 2026-03-10
AI Technical Summary
Existing technologies make it difficult to achieve cross-dimensional and full-parameter collaborative optimization in reactor design, resulting in low design efficiency, poor loss control, and low material utilization.
A hybrid coding method based on genetic algorithm and dynamic programming algorithm is adopted. By constructing a multi-constraint combinatorial optimization problem, and combining continuous, integer and discrete parameters, the radial core cross section of the reactor is optimized. The local optimum is solved by dynamic programming algorithm, and the global parameter search is performed by genetic algorithm. Combined with tournament selection, polynomial mutation and elite retention strategies, the global convergence of the algorithm is ensured.
The global optimal solution for the radial core cross-section of reactors with large-scale, multi-stage structures was achieved, which improved the core cross-sectional area and material utilization, optimized the magnetic circuit conductivity, reduced copper and iron losses, and achieved the optimal balance between reactor energy efficiency and cost.
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Figure CN121637992A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of power equipment design and optimization technology, specifically relating to a method and system for optimizing the radial core cross-section of a reactor based on genetic algorithm and dynamic programming algorithm. Background Technology
[0002] In reactor design, the cross-sectional structure of the radial core directly affects magnetic circuit efficiency, losses, and material costs, and its optimization is a key aspect of improving equipment performance.
[0003] In existing technologies, an exhaustive search method is used to find the optimal cross-sectional parameters. This method solves for the maximum cross-sectional area by traversing all possible parameter combinations (such as outer diameter, number of sectors, and silicon steel sheet thickness). However, this method suffers from a severe combinatorial explosion problem, with time complexity increasing exponentially with the parameter dimension, making it only suitable for very small-scale scenarios. In practical engineering, due to computational resource limitations, only local parameter searches are possible, resulting in a gap between the solution quality and the global optimum, and low material utilization. While subsequent dynamic programming algorithms reduce time complexity to O(nL²), they obtain strictly local optimal solutions under fixed global parameter constraints (such as outer diameter, inner diameter, and number of sectors). This requires relying on a preset parameter range, and recalculation is necessary when parameters are changed. This limitation of fixed parameters makes it difficult to adapt to the full-dimensional optimization needs of multi-stage, large-scale iron cores, leading to low design efficiency and poor loss control.
[0004] Therefore, existing technologies have significant shortcomings in balancing global parameter exploration capabilities with local optimization accuracy, and there is an urgent need for a technical solution that can achieve cross-dimensional, full-parameter collaborative optimization. Summary of the Invention
[0005] The purpose of this invention is to address the shortcomings of existing technologies in balancing global parameter search capabilities and local optimization accuracy by providing a method and system for optimizing the radial core cross-section of reactors based on genetic algorithms and dynamic programming algorithms.
[0006] In a first aspect, embodiments of this application provide a method for optimizing the cross-section of a reactor radial core based on a genetic algorithm and a dynamic programming algorithm, the method comprising: S1. Define the optimization of the radial core cross section of the reactor as a multi-constraint combined optimization problem, determine the objective function as maximizing the core cross-sectional area, and satisfy the manufacturing process constraints of stage length discretization, thickness modulus constraint and boundary non-interference; S2. Construct a parameter set for the radial core cross-section, the parameter set including continuous parameters, integer parameters and discrete parameters; S3. Encode the parameter set using a hybrid encoding method to generate a population; S4. For each individual in the population, solve the local optimal solution of the core section under the parameter set constraints by the dynamic programming algorithm, and calculate the fitness value of the local optimal solution; S5. Based on the fitness value, a genetic algorithm is used to iteratively optimize the population. The iterative optimization includes selection, crossover, and mutation operations, and a high-quality individual is retained through an elite retention strategy. S6. Determine whether the preset convergence condition is met. If the preset convergence condition is not met, return to step S4. If the preset convergence condition is met, output the globally optimal maximum core cross-sectional area.
[0007] Further, in step S1, the objective function is:
[0008] in, A B Where m is the total cross-sectional area of the core, and m is the number of sectors. B i For the first i Level length, A i =P( B i () represents the cumulative thickness.
[0009] Further, in step S1, the manufacturing process constraints include: Level length discretization:
[0010] Thickness modulus constraint:
[0011] Boundary non-interference:
[0012] in, B min For the minimum grade, B h For the step size of the class leader, D p For the thickness of silicon steel sheets, D w The outer diameter of the reactor winding. y 2 represents local geometric parameters. α The angle of inclination of the stacked pieces.
[0013] Furthermore, in step S2, the continuous parameters include the outer diameter, inner diameter, and sector angle of the core; the integer parameters include the number of sectors and the number of stages; and the discrete parameters include the thickness of the silicon steel sheet and the inter-stage range.
[0014] Furthermore, in step S3, the hybrid encoding method includes: Continuous parameters are encoded with real numbers and converted into engineering value ranges through linear mapping. Integer parameters are encoded using integers and their values are limited by interval segmentation; Discrete parameters are encoded in binary and mapped to a preset set of discrete values; The chromosome generated by hybrid coding is defined as:
[0015] in, D w The outer diameter of the reactor winding. D n denoted as the inner diameter of the reactor winding, m as the number of poles in the core, and n as the number of sectors in the core.
[0016] Furthermore, the specific steps of step S4 are as follows: S41. Based on the global constraints in the individual parameter set, construct the state transition equations for the sector hierarchy:
[0017] in, To contribute value, For state, As decision variables, Representing decision variables In state The set of feasible decisions at the k-th level; S42. Using the maximum cross-sectional area of a single sector as a sub-objective, calculate the optimal thickness set of silicon steel sheets at each level through reverse recursion; S43. Integrate the optimal solutions of all sectors to obtain the local optimal solution of the core cross section, and calculate the fitness value of the local optimal solution:
[0018] in, DP_Solver It is a dynamic programming solver.
[0019] Furthermore, the specific steps of step S5 are as follows: S51. A tournament-style selection process is used to select individuals from the population, assuming the population size is... N The scale of the tournament is s t Calculate individual c i The probability of being selected; S52. Perform crossover operations on the selected individuals. Arithmetic crossover is used for real-valued coding segments, and single-point crossover is used for integer-valued coding segments and discrete coding segments. The crossover probability is adjusted within the range of [0.6, 0.9]. S53. Perform mutation operation on the individuals after crossover. Gaussian mutation is used for real-valued coding segments, random integer mutation is used for integer coding segments, and random replacement mutation is used for discrete coding segments. The mutation probability is adjusted within the interval [0.01, 0.1]. S54. Implement an elite preservation strategy, selecting the most fit individuals from each generation of the population. k The best individuals are directly retained in the next generation of the population, thus obtaining the globally optimal solution.
[0020] Furthermore, in step S51, the individual c i The probability of being selected is:
[0021] in, To select operators, T For the number of tournaments, usually T = N The formula is:
[0022] in, r ( c i (for individuals) c i The fitness ranking proportion.
[0023] Furthermore, the preset convergence condition in step S6 is: The number of iterations reaches the preset value and the change in the fitness value of the globally optimal individual is less than the preset threshold of 0.001%.
[0024] Secondly, embodiments of this application also provide a reactor radial core cross-section optimization system based on genetic algorithm and dynamic programming algorithm, the system comprising: The problem definition module is used to define the optimization of the radial core cross-section of the reactor as a multi-constraint combined optimization problem, and to determine the objective function as maximizing the total cross-sectional area of the core, while satisfying the manufacturing process constraints of stage length discretization, thickness modulus constraint and boundary non-interference. The parameter set construction module is used to construct the parameter set of the radial core section. The parameter set includes continuous parameters, integer parameters, and discrete parameters. A hybrid encoding module is used to encode the parameter set using a hybrid encoding method to generate a population; The dynamic programming local optimization module is used to solve the local optimal solution of the iron core section under the parameter set constraints for each individual in the population using a dynamic programming algorithm, and to calculate the fitness value of the local optimal solution. The genetic algorithm iterative optimization module is used to iteratively optimize the population based on the fitness value using a genetic algorithm. The iterative optimization includes selection, crossover, and mutation operations, and retains high-quality individuals through an elite retention strategy. The convergence judgment and global output module is used to return to the steps of the dynamic programming local optimization module if the preset convergence conditions are not met; if the preset convergence conditions are met, it outputs the globally optimal core cross-section parameters and corresponding structure.
[0025] As can be seen from the above technical solutions, the present invention has the following advantages: This application presents a method and system for optimizing the radial core cross-section of reactors based on genetic algorithms and dynamic programming. Through a hierarchical collaborative optimization architecture, dynamic programming solves local optima, while genetic algorithms perform intelligent global parameter space search. This overcomes the limitations of fixed parameters, achieving cross-dimensional parameter collaborative optimization and obtaining the global optimum, thus overcoming the shortcomings of traditional methods in local optimization. Hybrid coding chromosomes adapt to different types of parameters, combined with tournament selection, multinomial mutation, and elite retention strategies, ensuring global convergence of the algorithm while keeping computation time within an engineering-usable range. This efficiently addresses the combinatorial optimization challenges of large-scale, multi-level structures. This application enables improvements in core cross-sectional area and material utilization, optimization of magnetic circuit permeability, and reduction of copper and iron losses, achieving an optimal balance between reactor energy efficiency and cost. Attached Figure Description
[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a flowchart illustrating the reactor radial core cross-section optimization method based on genetic algorithm and dynamic programming algorithm proposed in this application.
[0028] Figure 2 This is a schematic diagram of the reactor radial core cross-section optimization system based on genetic algorithm and dynamic programming algorithm in this application. Detailed Implementation
[0029] The various embodiments of the present invention will be described more fully in the detailed steps of the method for optimizing the radial core cross-section of a reactor based on genetic algorithms and dynamic programming algorithms, which will be described in detail below. The present invention may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present invention to the specific embodiments disclosed herein, but rather the present invention should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the present invention.
[0030] It should be understood that, when used in this specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.
[0031] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.
[0032] To make the objectives, features, and advantages of this invention more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solutions protected by this invention. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0033] Please see Figure 1 The diagram shows a flowchart of a method for optimizing the radial core cross-section of a reactor based on genetic algorithms and dynamic programming algorithms. The method includes the following steps: S1. Define the optimization of the radial core cross section of the reactor as a multi-constraint combined optimization problem, determine the objective function as maximizing the core cross-sectional area, and satisfy the manufacturing process constraints of stage length discretization, thickness modulus constraint and boundary non-interference; It should be noted that this step, by clarifying the multi-constraint combination optimization attributes of reactor core cross-section optimization, takes maximizing the core cross-sectional area as the core objective, providing an optimization direction for improving magnetic circuit permeability and reducing copper and iron losses. At the same time, clarifying manufacturing process constraints such as stage length discretization, thickness modulus, and boundary non-interference can prevent theoretical optimization solutions from deviating from actual production conditions, and provide a clear and compliant problem model for hierarchical optimization of genetic algorithms and dynamic programming algorithms.
[0034] S2. Construct a parameter set for the radial core cross-section, the parameter set including continuous parameters, integer parameters and discrete parameters; It should be noted that by constructing a clearly categorized parameter set, including continuous parameters (outer diameter, inner diameter, minimum stage length), integer parameters (number of sectors, number of stages), and discrete parameters (range, silicon steel sheet thickness) corresponding to actual engineering variables, a decoding basis is provided for the hybrid encoding of the genetic algorithm, avoiding confusion of parameter types; at the same time, a clear fixed parameter input is provided for the dynamic programming layer, ensuring the connection between global and local optimization. S3. Encode the parameter set using a hybrid encoding method to generate a population; It should be noted that a hybrid encoding method is adopted, with encoding logic adapted for continuous, integer, and discrete parameters respectively, to avoid encoding distortion; the generated population has the ability to search for continuous parameters and meets the engineering constraints for integer and discrete parameters, ensuring the diversity and quality of the population.
[0035] S4. For each individual in the population, solve the local optimal solution of the core section under the parameter set constraints by the dynamic programming algorithm, and calculate the fitness value of the local optimal solution; It should be noted that the local optimal solution of the core section under the constraints of each individual parameter set is solved by dynamic programming algorithm to ensure the optimality of the local solution; at the same time, the complex multi-constraint combination optimization problem is decomposed into layers as a local solver of the genetic algorithm, which not only ensures the engineering accuracy of the local solution, but also provides a fitness evaluation basis for the global search of the genetic algorithm, realizing the connection between local accuracy and global exploration.
[0036] S5. Based on the fitness value, a genetic algorithm is used to iteratively optimize the population. The iterative optimization includes selection, crossover, and mutation operations, and a high-quality individual is retained through an elite retention strategy. It should be noted that the above steps are based on the fitness value obtained by the dynamic programming algorithm (DP) for evaluation. Tournament selection is used to select high-quality individuals, and crossover and mutation operations are used to maintain population diversity and avoid getting trapped in local optima. At the same time, the elite retention strategy is relied upon to ensure that the optimal solution is not lost and to explore the global parameter space.
[0037] S6. Determine whether the preset convergence condition is met. If the preset convergence condition is not met, return to step S4. If the preset convergence condition is met, output the globally optimal maximum core cross-sectional area. It should be noted that by using convergence judgment, meaningless iterations can be terminated to save computing resources and avoid the algorithm from getting stuck in an inefficient loop; when the conditions are met, the output will be the globally optimal cross-sectional area that satisfies the multi-constraint optimization objective, ensuring that the output results meet the manufacturing process requirements.
[0038] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, another embodiment of a reactor radial core cross-section optimization method based on genetic algorithm and dynamic programming algorithm is provided, the method including: S1. The optimization of the radial core cross-section of the reactor is defined as a multi-constraint combined optimization problem. The objective function is determined to maximize the core cross-sectional area, while satisfying the constraints of stage length discretization, thickness modulus constraint, and boundary non-interference manufacturing process constraint. In step S1, the objective function is:
[0039] in, A B Where m is the total cross-sectional area of the core, and m is the number of sectors. B i For the first i Level length, A i =P( B i () represents the cumulative thickness.
[0040] In step S1, the manufacturing process constraints include: Level length discretization:
[0041] Thickness modulus constraint:
[0042] Boundary non-interference:
[0043] in, B min For the minimum grade, B h For the step size of the class leader, D p For the thickness of silicon steel sheets, D w The outer diameter of the reactor winding. y 2 represents local geometric parameters. α The angle of inclination of the stacked pieces.
[0044] S2. Construct a parameter set for the radial core cross-section, the parameter set including continuous parameters, integer parameters and discrete parameters; in step S2, the continuous parameters include the core outer diameter, inner diameter and sector angle; the integer parameters include the number of sectors and the number of stages; the discrete parameters include the silicon steel sheet thickness and the inter-stage range.
[0045] For example, based on the engineering design requirements of the radial core cross-section of the reactor, the parameter set is constructed as follows: In the continuous parameters, the outer diameter of the core is set to 200mm-600mm, which is a common range in engineering (to match the winding assembly space), the inner diameter is set to 80mm-240mm, which is 0.4-0.6 times the outer diameter (to ensure the rationality of the magnetic circuit), and the sector angle is set to 15°-60° according to the magnetic field distribution of the core (to avoid excessive local magnetic flux density); In the integer parameters, the number of sectors is taken as an even number of 4-12 (to match the symmetrical magnetic circuit design), and the number of stages is set to 6-24 according to the lamination process (to balance the cross-sectional area and processing difficulty); In the discrete parameters, the thickness of the silicon steel sheet is selected from the industry standard specifications of 0.3mm and 0.35mm (to take into account the permeability and cost), and the inter-stage range is selected as 0.5mm and 1mm (to meet the cutting accuracy of the silicon steel sheet). Each parameter corresponds to the actual production and assembly constraints of the project. S3. Encode the parameter set using a hybrid encoding method to generate a population; in step S3, the hybrid encoding method includes: Continuous parameters are encoded with real numbers and converted into engineering value ranges through linear mapping. Integer parameters are encoded using integers and their values are limited by interval segmentation; Discrete parameters are encoded in binary and mapped to a preset set of discrete values; The chromosome generated by hybrid coding is defined as:
[0046] in, D w The outer diameter of the reactor winding. D n denoted as the inner diameter of the reactor winding, m as the number of poles in the core, and n as the number of sectors in the core.
[0047] It should be noted that the definition of the decoding function has the following requirements: For continuous parameters, use a linear mapping.
[0048] For integer parameters, the interval [0,1] is evenly divided into multiple smaller intervals, each interval corresponding to an integer value (or discrete value).
[0049] For parameters with constraints (such as the ratio of inner diameter to outer diameter), the constraints are directly embedded in the decoding function to ensure that the generated parameters meet engineering requirements.
[0050] For example, outer diameter Define it as follows: Based on practical engineering requirements, the outer diameter of the iron core typically has a design range (e.g., 200mm to 600mm). Chromosomal genes It is a real number between [0,1] that is mapped to [200,600] by a linear transformation.
[0051] The mathematical expression is:
[0052] Linear mapping is the simplest and most commonly used decoding method, ensuring continuous coverage of the solution space.
[0053] series Define it as follows: Based on practical engineering considerations, the reasonable range for the series is defined as 6 to 24. Since these are integers and discrete, continuous gene values are mapped to discrete integers. Furthermore, even numbers are required, so the step size is 2. Using 10 discrete points, [0,1] is divided into 10 equal parts, each 0.1, then rounded to 0, 1, ..., 9, and then mapped to 6, 8, ..., 24.
[0054] The mathematical expression is:
[0055] Other parameters should be selected based on the actual needs of the project, choosing the appropriate decoding function.
[0056] S4. For each individual in the population, solve for the local optimal solution of the iron core cross section under the parameter set constraints using a dynamic programming algorithm, and calculate the fitness value of the local optimal solution; the specific steps of step S4 are as follows: S41. Based on the global constraints in the individual parameter set, construct the state transition equations for the sector hierarchy:
[0057] in, To contribute value, For state, As decision variables, Representing decision variables In state The set of feasible decisions at the k-th level; S42. Using the maximum cross-sectional area of a single sector as a sub-objective, calculate the optimal thickness set of silicon steel sheets at each level through reverse recursion; S43. Integrate the optimal solutions of all sectors to obtain the local optimal solution of the core cross section, and calculate the fitness value of the local optimal solution:
[0058] in, DP_Solver It is a dynamic programming solver.
[0059] It should be noted that solving for the maximum cross-sectional area is equivalent to solving for the maximum cross-section of a single sector. A single sector is divided into n stages according to the number of stacking levels, and the solution order is: n, n-1, n-2, ..., 2, 1. The dynamic programming algorithm solves in reverse order.
[0060] Each stage state is a set of optional stacks, where: The types of stacked sheets are:
[0061] The stack width is:
[0062] The thickness of the laminate is:
[0063] The stacked set is:
[0064] Pick Any element, in The stage needs to be started from Make a decision from the elements, and the width of the decision stack should be less than Elements that meet the conditions are The decision variables are denoted as... The set consisting of all elements that meet the conditions is called Allowable decision set, denoted as Therefore, the recurrence relation for dynamic programming can be derived as follows: ; The boundary conditions are: .
[0065] S5. Based on the fitness value, a genetic algorithm is used to iteratively optimize the population. The iterative optimization includes selection, crossover, and mutation operations, and a high-quality individual is retained through an elite retention strategy. The specific steps of step S5 are as follows: S51. A tournament-style selection process is used to select individuals from the population, assuming the population size is... N The scale of the tournament is s t Calculate individual c i The probability of being selected; in step S51, the individual c i The probability of being selected is:
[0066] in, To select operators, T For the number of tournaments, usually T =N The formula is:
[0067] in, r ( c i (for individuals) c i The fitness ranking ratio .
[0068] S52. Perform crossover operations on the selected individuals. Arithmetic crossover is used for real-valued coding segments, and single-point crossover is used for integer-valued coding segments and discrete coding segments. The crossover probability is adjusted within the range of [0.6, 0.9]. S53. Perform mutation operation on the individuals after crossover. Gaussian mutation is used for real-valued coding segments, random integer mutation is used for integer coding segments, and random replacement mutation is used for discrete coding segments. The mutation probability is adjusted within the interval [0.01, 0.1]. S54. Implement an elite preservation strategy, selecting the most fit individuals from each generation of the population. k The best individuals are directly retained in the next generation of the population, thus obtaining the globally optimal solution.
[0069] It should be noted that the solution space is:
[0070] in, It is a positive real number (outer diameter: , inner diameter: Minimum class length: ), A positive integer (number of sectors: ,series: ), Offline values (range: Silicon steel sheet thickness: ).
[0071] Let the first generation population ,but:
[0072] Among them, the selection operator is ; The crossover operator is ; The mutation operator is ; Satisfying the mutation operator makes .
[0073] S6. Determine if the preset convergence condition is met. If the preset convergence condition is not met, return to step S4; if the preset convergence condition is met, output the globally optimal maximized core cross-sectional area. The preset convergence condition in step S6 is: The number of iterations reaches the preset value and the change in the fitness value of the globally optimal individual is less than the preset threshold of 0.001%.
[0074] For example, the preset number of iterations is set to 100 generations, and the preset fitness value change threshold is 0.001%. After each iteration, the fitness value (corresponding to the core cross-sectional area) of the globally optimal individual is recorded. When iterating to the 92nd generation, the globally optimal fitness value changes by only 0.0008% compared to the 91st generation, and this change is less than 0.001% in the subsequent 8 generations. When the number of iterations reaches the preset value of 100 generations, the double convergence condition is met, the iteration stops, and the globally optimal maximized core cross-sectional area is output.
[0075] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0076] Please see Figure 2 As shown below, the reactor radial core cross-section optimization system based on genetic algorithm and dynamic programming algorithm of this application belongs to the same inventive concept as the reactor radial core cross-section optimization method based on genetic algorithm and dynamic programming algorithm in the above embodiments. For details not described in detail in the embodiments of the reactor radial core cross-section optimization system based on genetic algorithm and dynamic programming algorithm, please refer to the embodiments of the reactor radial core cross-section optimization method based on genetic algorithm and dynamic programming algorithm described above.
[0077] The system includes: The problem definition module is used to define the optimization of the radial core cross-section of the reactor as a multi-constraint combined optimization problem, and to determine the objective function as maximizing the total cross-sectional area of the core, while satisfying the manufacturing process constraints of stage length discretization, thickness modulus constraint and boundary non-interference. The parameter set construction module is used to construct the parameter set of the radial core section. The parameter set includes continuous parameters, integer parameters, and discrete parameters. A hybrid encoding module is used to encode the parameter set using a hybrid encoding method to generate a population; The dynamic programming local optimization module is used to solve the local optimal solution of the iron core section under the parameter set constraints for each individual in the population using a dynamic programming algorithm, and to calculate the fitness value of the local optimal solution. The genetic algorithm iterative optimization module is used to iteratively optimize the population based on the fitness value using a genetic algorithm. The iterative optimization includes selection, crossover, and mutation operations, and retains high-quality individuals through an elite retention strategy. The convergence judgment and global output module is used to return to the steps of the dynamic programming local optimization module if the preset convergence conditions are not met; if the preset convergence conditions are met, it outputs the globally optimal core cross-section parameters and corresponding structure.
[0078] This embodiment, through the interactive collaboration of a problem-solving module, a parameter set construction module, a hybrid coding module, a dynamic programming local optimization module, a genetic algorithm iterative optimization module, and a convergence judgment and global output module, overcomes the limitations of traditional dimensionality based on genetic and dynamic programming algorithms. Compared to previous methods, it achieves full-dimensional free search by utilizing global and local parameter collaborative optimization. This improves the core cross-sectional area and material utilization, optimizes the magnetic circuit permeability, and achieves the optimal balance between reactor energy efficiency and cost.
[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for optimizing the cross-section of a radiation core of a reactor based on a genetic algorithm and a dynamic programming algorithm, characterized in that, The method comprises the following steps: S1. defining the reactor radial core section optimization as a multi-constraint combination optimization problem, determining the objective function as maximizing the core section area, and satisfying the level length discretization, thickness modulus constraint and boundary non-interference manufacturing process constraint; S2. constructing a parameter set of the radial core section, the parameter set comprising continuous parameters, integer parameters and discrete parameters; S3. encoding the parameter set in a mixed coding manner to generate a population; S4. solving the local optimal solution of the core section under the constraint of the parameter set corresponding to each individual in the population by a dynamic programming algorithm, and calculating the fitness value of the local optimal solution; S5. based on the fitness value, iteratively optimizing the population by a genetic algorithm, the iterative optimization comprising selection, crossover and mutation operations, and retaining high-quality individuals by an elite retention strategy; S6. determining whether a preset convergence condition is met, if the preset convergence condition is not met, returning to step S4; and if the preset convergence condition is met, outputting the globally optimal maximum core section area.
2. The method for optimization of the cross-section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 1, characterized in that, In step S1, the objective function is: wherein, A B S is the total cross-sectional area of the core, m is the number of sectors, B i is the first i stage length, A i = P( B i ) is the cumulative thickness.
3. The method for optimization of the cross section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 2, characterized in that, In step S1, the manufacturing process constraint comprises: Stage length discretization: Thickness modulus constraint: Border non-interference: wherein, B min Lmin is the minimum length, B h Lstep is the length step, D p t is the silicon steel sheet thickness, D w D is the outer diameter of the reactor winding, y 2 is a local geometry parameter, α is the lamination tilt angle.
4. The method for optimizing the cross section of a reactor radiating core based on a genetic algorithm and a dynamic programming algorithm according to claim 3, characterized in that, In step S2, the continuous parameters comprise a core outer diameter, an inner diameter and a sector angle; the integer parameters comprise a sector number and a level number; and the discrete parameters comprise a silicon steel sheet thickness and a level difference.
5. The method for optimization of the cross-section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 4, characterized in that, In step S3, the mixed coding manner comprises: encoding the continuous parameters by real number coding and converting them into an engineering value range by linear mapping; encoding the integer parameters by integer coding and limiting the value by interval segmentation; encoding the discrete parameters by binary coding and mapping them to a preset discrete value set; defining the chromosome generated by the mixed coding as: wherein D w D is the outer diameter of the reactor winding, D n D is the inner diameter of the reactor winding, m is the number of stages of the core, and n is the number of sectors of the core.
6. The method for optimization of the cross-section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 5, characterized in that, The specific steps of step S4 are: S41. constructing a state transition equation of the sector level structure according to the global constraint in the individual parameter set: wherein, is a value contribution, is a state, is a decision variable, denotes a decision variable in a state the feasible decision set of the k-th layer under the state S42. calculating the optimal thickness set of each level silicon steel sheet by reverse recursion with the maximum section area of a single sector as a sub-objective; S43. integrating the optimal solutions of all sectors to obtain the local optimal solution of the core section, and calculating the fitness value of the local optimal solution as: wherein DP_Solver is a dynamic programming solver.
7. The method for optimization of the cross-section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 6, characterized in that, The specific steps of step S5 are: S51. Select individuals from the population using tournament selection, where the population size is N , the tournament size is s t , and the probability of an individual c i being selected is calculated. S52. performing a crossover operation on the selected individuals, the real value coding segment adopts arithmetic crossover, the integer value coding segment and the discrete coding segment adopt single-point crossover, and the crossover probability is adjusted in the interval [0.6, 0.9]; S53. performing a mutation operation on the crossed individuals, the real value coding segment adopts Gaussian mutation, the integer value coding segment adopts random integer mutation, and the discrete coding segment adopts random replacement mutation, and the mutation probability is adjusted in the interval [0.01, 0.1]; S54. Perform elitist strategy, reserve the top k elite individuals in each generation to the next generation, get the global optimal solution.
8. The method for optimization of the cross-section of the radiating core of a reactor based on genetic algorithms and dynamic programming algorithms according to claim 7, characterized in that, In step S51, the individual c i The probability of being selected is: wherein, is a selection operator, T is the number of tournaments, usually T = 1 N , the formula is: wherein, r ( c i ) is the fitness rank proportion of the individual c i .
9. The method for optimizing the cross section of a reactor radiating core based on a genetic algorithm and a dynamic programming algorithm according to claim 8, characterized in that, The preset convergence condition in step S6 is: the iteration number reaches a preset value and the fitness value change of the globally optimal individual is less than a preset threshold of 0.001%.
10. A reactor radiation type core section optimization system based on a genetic algorithm and a dynamic programming algorithm, characterized by, The system comprises: a problem determination module configured to define the reactor radial core section optimization as a multi-constraint combination optimization problem, determine the objective function as maximizing the total core section area, and satisfy the level length discretization, thickness modulus constraint and boundary non-interference manufacturing process constraint; A parameter set construction module is configured to construct a parameter set of the radial core section, the parameter set including continuous parameters, integer parameters and discrete parameters; A hybrid coding module is configured to code the parameter set in a hybrid coding manner to generate a population; A dynamic programming local optimization module is configured to solve a local optimal solution of the core section under the parameter set constraint for each individual in the population by a dynamic programming algorithm, and calculate a fitness value of the local optimal solution; A genetic algorithm iterative optimization module is configured to iteratively optimize the population by a genetic algorithm based on the fitness value, the iterative optimization including selection, crossover and mutation operations, and retaining high-quality individuals by an elitist strategy; A convergence judgment and global output module is configured to return to the step of the dynamic programming local optimization module if a preset convergence condition is not met, and output a globally optimal core section parameter and a corresponding structure if the preset convergence condition is met.