A low magnetic flux leakage ring type air core reactor parameter optimization design method and system

Abstract

The present application relates to the technical field of reactor parameter optimization design, and particularly relates to a low leakage magnetic ring type air-core reactor parameter optimization design method and system, the method comprises obtaining reactor function parameters, obtaining a reactor design variable group and a reactor function parameter group that meet inductance constraints, and the like, the present application solves the problem that in the prior art, due to reliance on software simulation, a reactor parameter that meets the working condition requirements cannot be found, the present application can quickly obtain a reactor parameter that meets the working condition requirements, reduces the requirement for simulation software, and improves design efficiency.

Description

Technical Field

[0001] This invention relates to the field of reactor parameter optimization design technology, specifically to a method and system for parameter optimization design of a low-leakage magnetic ring type air-core reactor. Background Technology

[0002] While traditional column-type air-core reactors are relatively simple in structure and manufacturing process, their magnetic field distribution exhibits divergent characteristics during operation, resulting in significant magnetic leakage. This induces eddy currents or currents in surrounding metal components or circuits, leading to severe heat generation and potential electromagnetic interference to secondary systems. Furthermore, when a short-circuit fault occurs in a column-type reactor, the rapid change in reactor current generates significant electrical stress in surrounding conductors, potentially causing severe deformation in extreme cases. Toroidal air-core reactors, on the other hand, consist of several coil groups, each divided into two parallel parts, with each part connected in series. Each coil group is further connected in parallel by several sub-coils. Based on this unique closed-loop arrangement of the coils, toroidal air-core reactors can confine most of the magnetic field within the coil, thereby reducing the required magnetic clearance during operation. Therefore, the design of toroidal air-core reactors... Design and manufacturing are of great significance for urban substations that need to consider land use costs and offshore converter platforms that require compact design. Currently, toroidal air-core reactors have not been widely used in practical applications because there is no systematic parameter optimization design method for toroidal air-core reactors. Their special structure makes the design methods of cylindrical air-core reactors unsuitable. At the same time, for a given set of design parameters, the temperature rise factor that restricts its safe and stable operation can only be verified through finite element simulation, which greatly reduces design efficiency. Therefore, if there were a design scheme that could directly derive reactor parameters based on user needs without the need for model building and calculation verification, it would greatly shorten the design time and be more conducive to the practical application of toroidal air-core reactors. In summary, the existing technology suffers from the problem of failing to find reactor parameters that meet the operating conditions due to its reliance on software simulation. Summary of the Invention

[0003] This invention addresses the problems existing in the prior art by providing a parameter optimization design method and system for a low-leakage magnetic ring type air-core reactor;

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, embodiments of the present invention provide a parameter optimization design method for a low-leakage magnetic ring type air-core reactor, including:

[0006] Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter;

[0007] Based on the reactor design variables, and according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor.

[0008] Based on the reactor design variables, the reactor functional parameters are obtained through comprehensive design algorithms under the condition of satisfying the inductance constraint. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity.

[0009] Based on the reactor design variables, through repeated genetic iterations, all reactor design variable sets and reactor functional parameter sets that meet the inductance constraints are obtained;

[0010] Based on the reactor design variable group and the reactor functional parameter group, the reactor design variables with relatively better cost and temperature rise are obtained through non-dominated sorting.

[0011] Based on the sorted set of reactor functional parameters and reactor design variables, an optimization design algorithm is used to obtain reactor functional parameters and reactor design variables with relatively better temperature rise and cost.

[0012] Preferably, the temperature rise empirical algorithm includes:

[0013] Based on the reactor design variables, the coil arrangement density is determined using a coil arrangement algorithm.

[0014] Based on the reactor design variables, the heat flux density of the coil is determined using a heat flux density algorithm.

[0015] The temperature rise value is determined by a temperature rise algorithm based on the coil arrangement density, coil heat flux density, and reactor design variables.

[0016] Preferably, the line-and-pie arrangement algorithm includes:

[0017] Line pie arrangement fitting function:

[0018] p = dN d / πD

[0019] Wherein, p is the density of the coil arrangement, d is the thickness of the coil, and N is... d The number of coils is indicated by D, which represents the inner diameter of the reactor.

[0020] Preferably, the heat flux density algorithm includes:

[0021] Heat flux density fitting function:

[0022] q = Q / S

[0023] Wherein, q is the heat flux density of the coil, Q is the reactor loss, and S is the heat dissipation area of ​​the reactor.

[0024] Preferably, the temperature rise algorithm includes:

[0025] Temperature rise fitting function:

[0026] ΔT=0.234p 0.1 H 0.2 q 0.9

[0027] Wherein, ΔT is the temperature rise value, and H is the reactor height.

[0028] Preferably, the repeated genetic iterations include:

[0029] Based on the obtained Nth group of reactor design variables, and according to the design range of the design variables, the N+1th group of reactor design variables is obtained through crossover mutation of the genetic algorithm.

[0030] Based on the N+1th group of reactor design variables, the reactor functional parameters are recalculated using a comprehensive design algorithm.

[0031] Determine whether the number of iterations has reached the maximum number of iterations. If it has, then obtain the reactor design variable group and the reactor function parameter group by collecting all reactor design variables and corresponding reactor function parameters.

[0032] Preferably, the non-dominated sorting includes:

[0033] The temperature rise value of the reactor functional parameters is fitted to the temperature rise target, and the conductor value of the reactor functional parameters is fitted to the cost target.

[0034] Based on the temperature rise target and cost target, determine whether the temperature rise target of group N is greater than the temperature rise target of group N+1 and whether the cost target of group N is greater than the cost target of group N+1. If they are true, determine that the functional parameters of group N+1 reactors are superior to those of group N reactors, and prioritize the functional parameters of group N+1 reactors over those of group N reactors. At the same time, prioritize the design variables of group N+1 reactors over those of group N reactors.

[0035] Preferably, the optimal design algorithm includes: based on the sorted reactor functional parameter group and reactor design variable group, generating optimized reactor functional parameter front and optimized reactor design variable front by sorting the reactor functional parameters and reactor design variables at the forefront;

[0036] Based on the optimized frontier of reactor functional parameters and the optimized frontier of reactor design variables, the optimal reactor functional parameters and optimal reactor design variables are obtained through the Pareto optimal approach.

[0037] Meanwhile, the present invention also provides a parameter optimization design system for a low-leakage magnetic ring type air-core reactor, comprising:

[0038] Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter;

[0039] Based on the reactor design variables, and according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor.

[0040] Based on the reactor design variables, the reactor functional parameters are obtained through comprehensive design algorithms under the condition of satisfying the inductance constraint. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity.

[0041] Based on the reactor design variables, through repeated genetic iterations, all reactor design variable sets and reactor functional parameter sets that meet the inductance constraints are obtained;

[0042] Based on the reactor design variable group and the reactor functional parameter group, the reactor design variables with relatively better cost and temperature rise are obtained through non-dominated sorting.

[0043] Based on the sorted set of reactor functional parameters and reactor design variables, an optimization design algorithm is used to obtain reactor functional parameters and reactor design variables with relatively better temperature rise and cost.

[0044] In addition, the present invention also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, cause the processor to implement the parameter optimization design method described above.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This parameter optimization design method comprises six main steps: obtaining conductor specifications, obtaining the initial first set of reactor design variables, obtaining reactor functional parameters, obtaining a set of reactor design variables and a set of reactor functional parameters that meet inductance constraints, obtaining the optimal reactor design variables, and obtaining the optimal reactor functional parameters and optimal reactor design variables. Based on these six steps, this invention proposes a parameter optimization design method for toroidal air-core reactors. This method avoids finite element simulation calculations, reduces reliance on data, saves design time, improves design efficiency, and fills a gap in the optimization design of toroidal air-core reactors. Furthermore, compared with existing technologies, this invention provides a parameter optimization design method for toroidal air-core reactors that can quickly obtain reactor parameters that meet operating conditions, reduces the need for simulation software, improves design efficiency, and can select a relatively optimal set of solutions from existing parameters. This is of great significance for the practical application of toroidal air-core reactors. Attached Figure Description

[0047] Figure 1 This is a system flowchart of an embodiment of the present invention;

[0048] Figure 2 This is a flowchart of the temperature rise empirical algorithm according to an embodiment of the present invention;

[0049] Figure 3 This is a structural diagram of the central ring type air-core reactor according to an embodiment of the present invention;

[0050] Figure 4 This is a circuit connection diagram of the central loop type air-core reactor according to an embodiment of the present invention;

[0051] Figure 5 This is a flowchart of the parameter optimization design of the central loop type air-core reactor according to an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram illustrating the optimization result of the optimal design algorithm in an embodiment of the present invention; Detailed Implementation

[0053] It is worth noting that, unless otherwise specified, the methods used in this invention are all conventional methods; and the raw materials and equipment used are all conventional commercially available products, and their sources are not specifically limited.

[0054] The present invention will be further described below with reference to specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0055] Figures 1-6 The accompanying drawings are related to embodiments of the present invention. Figure 1 As shown, embodiments of the present invention include:

[0056] like Figure 1As shown, a parameter optimization design method for a low-leakage magnetic ring type air-core reactor includes:

[0057] Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter S101;

[0058] Based on the reactor design variables, according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor S102.

[0059] Based on the reactor design variables, the reactor functional parameters are obtained under the condition of satisfying the inductance constraint through comprehensive design algorithms. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity S103.

[0060] Based on the reactor design variables, through repeated genetic iterations, all reactor design variable groups and reactor functional parameter groups S104 that meet the inductance constraints are obtained;

[0061] Based on the reactor design variable group and the reactor functional parameter group, the reactor design variable S105 with relatively better cost and temperature rise is obtained through non-dominated sorting.

[0062] Based on the sorted set of reactor functional parameters and reactor design variables, an optimization design algorithm is used to obtain reactor functional parameters and reactor design variables S106 with relatively favorable temperature rise and cost.

[0063] like Figure 2 As shown, the temperature rise empirical algorithm includes:

[0064] Based on the reactor design variables, the coil arrangement density S1031 is determined by the coil arrangement algorithm;

[0065] Based on the reactor design variables, the heat flux density S1032 of the coil is determined by the heat flux density algorithm.

[0066] Based on the coil arrangement density, coil heat flux density, and reactor design variables, the temperature rise value S1033 is determined using a temperature rise algorithm.

[0067] The line-pie arrangement algorithm includes:

[0068] Line pie arrangement fitting function:

[0069] p = dN d / πD

[0070] Wherein, p is the density of the coil arrangement, d is the thickness of the coil, and N is... d The number of coils is indicated by D, which represents the inner diameter of the reactor.

[0071] The heat flux density algorithm includes:

[0072] Heat flux density fitting function:

[0073] q = Q / S

[0074] Wherein, q is the heat flux density of the coil, Q is the reactor loss, and S is the heat dissipation area of ​​the reactor.

[0075] The temperature rise algorithm includes:

[0076] Temperature rise fitting function:

[0077] ΔT=0.234p 0.1 H 0.2 q 0.9

[0078] Wherein, ΔT is the temperature rise value, and H is the reactor height.

[0079] like Figures 1-6 As shown, the repeated genetic iterations include:

[0080] Based on the obtained Nth group of reactor design variables, and according to the design range of the design variables, the N+1th group of reactor design variables is obtained through crossover mutation of the genetic algorithm.

[0081] Based on the N+1th group of reactor design variables, the reactor functional parameters are recalculated using a comprehensive design algorithm.

[0082] Determine whether the number of iterations has reached the maximum number of iterations. If it has, then obtain the reactor design variable group and the reactor function parameter group by collecting all reactor design variables and corresponding reactor function parameters.

[0083] like Figures 1-6 As shown, the non-dominated sorting includes:

[0084] The temperature rise value of the reactor functional parameters is fitted to the temperature rise target, and the conductor value of the reactor functional parameters is fitted to the cost target.

[0085] Based on the temperature rise target and cost target, determine whether the temperature rise target of group N is greater than the temperature rise target of group N+1 and whether the cost target of group N is greater than the cost target of group N+1. If they are true, determine that the functional parameters of group N+1 reactors are superior to those of group N reactors, and prioritize the functional parameters of group N+1 reactors over those of group N reactors. At the same time, prioritize the design variables of group N+1 reactors over those of group N reactors.

[0086] like Figures 1-6 As shown, the optimal design algorithm includes: based on the sorted reactor functional parameter group and reactor design variable group, the sorted reactor functional parameters and reactor design variables are sorted to generate the optimized reactor functional parameter front and the optimized reactor design variable front;

[0087] Based on the optimized frontier of reactor functional parameters and the optimized frontier of reactor design variables, the optimal reactor functional parameters and optimal reactor design variables are obtained through the Pareto optimal approach.

[0088] Meanwhile, the present invention also provides a parameter optimization design system for a low-leakage magnetic ring type air-core reactor, comprising:

[0089] Based on the parameter optimization design method described above, the parameter optimization design system includes a parameter optimization design platform, which is used for:

[0090] Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter S101;

[0091] Based on the reactor design variables, according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor S102.

[0092] Based on the reactor design variables, the reactor functional parameters are obtained under the condition of satisfying the inductance constraint through comprehensive design algorithms. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity S103.

[0093] Based on the reactor design variables, through repeated genetic iterations, all reactor design variable groups and reactor functional parameter groups S104 that meet the inductance constraints are obtained;

[0094] Based on the reactor design variable group and the reactor functional parameter group, the reactor design variable S105 with relatively better cost and temperature rise is obtained through non-dominated sorting.

[0095] Based on the sorted set of reactor functional parameters and reactor design variables, an optimization design algorithm is used to obtain reactor functional parameters and reactor design variables S106 with relatively favorable temperature rise and cost.

[0096] In addition, the present invention also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, cause the processor to implement the parameter optimization design method described above.

[0097] Example 1:

[0098] The following is combined Figures 1-6 The working principle of the parameter optimization design method shown in the embodiments will be explained;

[0099] like Figure 1 As shown, this parameter optimization design method includes four main steps: Step 1: Obtaining the conductor specifications; Step 2: Obtaining the initial first set of reactor design variables; Step 3: Obtaining the reactor functional parameters; Step 4: Obtaining the reactor design variable set and reactor functional parameter set that meet inductance constraints; Step 5: Obtaining the optimal reactor design variables; Step 6: Obtaining the optimal reactor functional parameters and optimal reactor design variables. Based on these six steps, the conductor specifications are first determined according to the current carrying capacity, and the conductor diameter is determined with a certain margin. Next, the ranges of the four design variables are set, and values ​​are randomly generated within each range, combining to form an initial solution. Then, the data is substituted into the inductance analytical formula represented by the four design variables to calculate the inductance values ​​for each variable. The parameter combinations are retained within the allowable error range of the target inductance. Finally, the temperature rise and conductor length corresponding to each set of parameters are calculated. Using a genetic algorithm, new solutions are obtained by crossover and mutation of solutions that meet the conditions. This process is repeated until the maximum number of iterations is reached. Finally, all solutions that meet the conditions after iteration are sorted non-dominated. A Pareto front is obtained with the objectives of minimizing temperature rise and shortening conductor length. On the Pareto front, a suitable solution is selected based on the emphasis of the objective function. This invention proposes a parameter optimization design method for toroidal air-core reactors, avoiding finite element simulation calculations, reducing reliance on data, saving design time, improving design efficiency, and filling a gap in the optimization design of toroidal air-core reactors. Furthermore, compared with existing technologies, this invention provides a parameter optimization design method for toroidal air-core reactors that can quickly obtain reactor parameters that meet operating conditions, reducing the need for simulation software, improving design efficiency, and selecting a relatively optimal set of solutions from existing parameters. This is of great significance for the practical application of toroidal air-core reactors.

[0100] The following is combined Figures 1-6The working principle of the parameter optimization design method shown in the embodiments will be explained.

[0101] like Figure 2 As shown, the temperature rise empirical algorithm of this parameter optimization design method includes three main steps: First, based on the reactor design variables, the coil arrangement density is determined using a coil arrangement algorithm; second, based on the reactor design variables, the coil heat flux density is determined using a heat flux density algorithm; third, based on the coil arrangement density, coil heat flux density, and reactor design variables, the temperature rise value is determined using a temperature rise value algorithm. Specifically, based on the above steps, further, by referring to the empirical formula for temperature rise of cylindrical air-core reactors, the factors affecting the temperature rise of toroidal air-core reactors are explored. Besides losses, heat dissipation area, and height, the most significant difference between toroidal air-core reactors and cylindrical air-core reactors lies in the ring-shaped arrangement of coils, and their density also affects the temperature rise. Here, a quantity that can characterize the density of the coil arrangement is defined:

[0102] p = dN d / πD

[0103] Where p is defined as sparsity, used to characterize the density of the coil arrangement, d represents the coil thickness, Nd represents the number of coils, and D represents the reactor inner diameter. Considering that losses and heat dissipation area have a major and opposite impact on temperature rise, heat flux density is used to represent their respective effects.

[0104] q = Q / S

[0105] Where q represents heat flux density, Q represents loss, and S represents heat dissipation area. Based on the above investigation of the factors affecting the temperature rise of toroidal air-core reactors, and combined with the verification of simulation models corresponding to multiple sets of different parameters, an empirical formula for the temperature rise of toroidal air-core reactors is obtained through parameter fitting:

[0106] ΔT=0.234p 0.1 H 0.2 q 0.9

[0107] Combining the analytical calculation method for inductance, the analytical formula for inductance, the empirical formula for temperature rise, and the amount of wire are expressed using four design parameters. The inductance value corresponding to all parameters in this set is calculated. If it meets the allowable design error for inductance, the result is retained, and the temperature rise value and wire length are calculated; otherwise, it is discarded.

[0108] Among them, the formula for calculating self-inductance in the inductance analytical formula is:

[0109]

[0110] Wherein, μ0 is the vacuum permeability, N is the number of turns of the coil, and d avgLet d be the diameter of the center line of the disc. avg =d in +d out The d in d is the inner diameter of the wire disc. out The outer diameter of the wire disc is ρ; the relative wall thickness is ρ = (d / d) out -d in ) / (d out +d in The ci is a coefficient related to the conductor layout, where (i = 1, 2, 3, 4), and for a circular wire disc, c1 = 1.00, c2 = 2.46, c3 = 0.00, c4 = 0.20;

[0111] Among them, the formula for calculating mutual inductance in the inductance analytical formula is:

[0112]

[0113] Wherein, r1 is the radius of coil 1, r2 is the radius of coil 2, and r 12 Let θ be the distance between infinitesimal element 1 and infinitesimal element 2, θ be the angle between the line connecting the center of coil 1 and infinitesimal element 1 and the x-axis, φ be the angle between the line connecting the center of coil 2 and infinitesimal element 2 and the x-axis, and α be the angle between the z-axis of coil 2 and the z-axis of coil 1.

[0114] The formula for conductor length is as follows:

[0115]

[0116] Wherein, R1 is the inner diameter of the coil, N is the number of turns of the coil, d is the diameter of the conductor, and N d The number of thread rolls;

[0117] The following is combined Figures 1-6 The working principle of the parameter optimization design method shown in the embodiments will be explained;

[0118] like Figures 1-6As shown, the iterative genetic design method of this parameter optimization method includes three main steps: First, based on the obtained Nth group of reactor design variables, according to the design range of the design variables, the N+1th group of reactor design variables is obtained through crossover and mutation using a genetic algorithm. Second, based on the N+1th group of reactor design variables, the reactor functional parameters are recalculated using a comprehensive design algorithm. Third, it is determined whether the number of iterations has reached the maximum number of iterations. If so, all reactor design variables and corresponding reactor functional parameters are collected to obtain the reactor design variable set and the reactor functional parameter set. First, the design requirements of the reactor are determined, typically based on the known inductance value and reactor current carrying capacity. First, determine the error range of the inductor design and determine the conductor specifications based on the current carrying capacity of the conductor. Second, select parameters that characterize the reactor size as design variables. Here, the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor are selected as design variables. Set the range of each of the four parameters and randomly select data within their respective ranges to form a set of initial parameters. Third, for all solutions that meet the conditions obtained in the first calculation, use a genetic algorithm to perform crossover mutation within the parameter design range to obtain a new set of solutions. Then, recalculate the inductance, temperature rise, and conductor length to realize the parameter iterative process. Repeat the above process until the maximum number of iterations is reached and all solutions that meet the conditions are collected.

[0119] The genetic algorithm involves specifying the population size, randomly selecting parameters from the respective ranges of the design variables to form the population, calculating the inductance of each set of parameters in the population, retaining the solutions that meet the conditions, and then using the solutions that meet the conditions as the parents. The parents are then subjected to crossover (randomly exchanging one or more variables among several sets of parameters) and mutation (randomly applying a small perturbation to a parameter among several sets of parameters) to generate new solutions. A new solution that meets the population size is then regenerated, thus realizing the iterative process.

[0120] The following is combined Figures 1-6 The working principle of the parameter optimization design method shown in the embodiments will be explained;

[0121] like Figures 1-6As shown, the non-dominated sorting of this parameter optimization design method includes three main steps. The first step is to fit the temperature rise value of the reactor functional parameters to the temperature rise target and the conductor value of the reactor functional parameters to the cost target. The second step is to determine whether the temperature rise target of the Nth group is greater than the temperature rise target of the N+1th group and whether the cost target of the Nth group is greater than the cost target of the N+1th group. If they are true, the reactor functional parameters of the N+1th group are determined to be superior to the reactor functional parameters of the Nth group, and the reactor functional parameters of the N+1th group are prioritized in the order of the reactor functional parameters of the Nth group. At the same time, the reactor design variables of the N+1th group are prioritized in the order of the reactor design variables of the Nth group. Based on the above steps, the algorithm is used to sort all the solutions that meet the above conditions. The sorting criteria are temperature rise and cost. One or more solutions with lower temperature rise and cost are selected.

[0122] The sorting process involves collecting all solutions that meet the inductance constraint after iteration, calculating the temperature rise and length of all solutions using formulas, and determining whether a solution should be retained according to the following principles: if the objective function value 1 of solution 1 is less than the objective function value 1 of solution 2 and the objective function value 2 of solution 1 is less than the objective function value 2 of solution 2, then solution 1 is considered superior to solution 2, and solution 1 is sorted before solution 2. Finally, only the solution sorted at the beginning is selected.

[0123] The following is combined Figures 1-6 The working principle of the parameter optimization design method shown in the embodiments will be explained;

[0124] like Figures 1-6 As shown, the non-dominated ranking of this parameter optimization design method includes two main steps. The first step: based on the ranked reactor functional parameter group and reactor design variable group, the ranked reactor functional parameters and reactor design variables are used to generate optimized reactor functional parameter fronts and optimized reactor design variable fronts. The second step: based on the optimized reactor functional parameter fronts and optimized reactor design variable fronts, the optimal reactor functional parameters and optimal reactor design variables are obtained through Pareto optimality. Based on the above steps, once the current carrying capacity of the conductor is determined, the conductor specifications are also determined. Therefore, the cost at this point can be replaced by the conductor length. Simultaneously, when reducing cost, i.e., reducing the conductor length, to ensure the inductance value of the reactor, the inner diameter of the reactor needs to be reduced, which increases the temperature rise. It is evident that the two objectives of temperature rise and cost are mutually restrictive and cannot be simultaneously optimized. Therefore, the Pareto optimality approach is adopted to select the solution that is relatively better than the other two.

[0125] The Pareto optimization approach, verified through simulation and experiments, reveals a mutually constraining relationship between the two objective functions of temperature rise and cost in the design process. Specifically, to reduce cost while maintaining inductance requirements, the coil arrangement must be denser, naturally increasing temperature rise. Conversely, to reduce temperature rise, thicker wires require increased wire length, increasing cost; sparser arrangement necessitates more turns to maintain inductance, also increasing cost. Since these two objectives are mutually constrained and cannot be simultaneously optimized, the Pareto optimization approach is employed. This involves finding the parameters with relatively smaller objective function values—the solutions ranked highest. However, there is likely more than one highest-ranking solution, forming a front, known as the Pareto front. The desired solution is found on this front. Figure 6 As shown in the figure, the two functions are mutually constrained, rather than being monotonically increasing functions that can simultaneously reach their minimum. The red-marked area is the Pareto front. For any blue point, a solution can be found on the front that is better than the two objective function values. Therefore, a suitable solution can be selected on the Pareto front according to the different focuses of the two objective functions.

[0126] The following is combined Figures 1-6 The working principle of the parameter optimization design method shown in the embodiments will be explained;

[0127] like Figures 1-6 As shown, the present invention also provides a parameter optimization design system for a low-leakage magnetic ring type air-core reactor. First, parameters are generated within the design range and the inductance is calculated to determine whether to retain them. Then, a genetic algorithm is used to iterate continuously to finally select all solutions that meet the inductance constraints and calculate the temperature rise and length corresponding to these solutions. Then, using the Pareto optimal approach, a non-dominated sorting method is used to obtain one or more relatively better sets of solutions.

[0128] like Figure 3 As shown in the diagram, the structure of the toroidal air-core reactor provided in this embodiment of the invention is as follows, with reference to... Figure 4 As shown in the figure, in this embodiment, the ring-shaped hollow reactor is divided into upper and lower halves in the top view and is connected in parallel. Each half is composed of several parallel coil groups. Each parallel coil group consists of two coil units connected in parallel. The current flowing through each coil is in the same direction. The ring structure confines the magnetic field inside the reactor.

[0129] like Figure 4 The diagram shown is a circuit connection diagram of the toroidal air-core reactor in an embodiment of the present invention; refer to... Figure 4As shown in this embodiment, each coil represents a coil unit, and the series and parallel connections between coils represent the actual circuit connection relationship. Therefore, by calculating the inductance of each coil using finite element simulation software, and by using the circuit connection relationship, the overall inductance of the reactor can also be obtained to verify the correctness of the analytical formula.

[0130] 1. This invention fills a gap in the parameter design of toroidal air-core reactors. It helps to leverage the advantages of toroidal air-core reactors compared to cylindrical air-core reactors, reducing the footprint and lowering design costs;

[0131] 2. This invention avoids verifying the rationality of parameters by building models through software, saving a lot of design time and greatly improving design efficiency.

[0132] 3. This invention is highly flexible. According to the working conditions, the corresponding parameters can be input to quickly obtain multiple sets of structural parameter schemes for toroidal hollow reactors, and the temperature rise and cost of the reactors are relatively small.

[0133] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A parameter optimization design method for a low-leakage magnetic ring type air-core reactor, characterized in that, include: Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter; Based on the reactor design variables, and according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor. Based on the reactor design variables, the reactor functional parameters are obtained through comprehensive design algorithms under the condition of satisfying the inductance constraint. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity. The empirical algorithm for temperature rise includes: Based on the reactor design variables, the coil arrangement density is determined using a coil arrangement algorithm: Line pie arrangement fitting function: Wherein, p is the density of the coil arrangement, d is the coil thickness, Nd represents the number of coils, and D represents the inner diameter of the reactor; Based on reactor design variables, the heat flux density of the coil is determined using a heat flux density algorithm: Heat flux density fitting function: Wherein, q is the heat flux density of the coil, Q is the reactor loss, and S is the heat dissipation area of ​​the reactor; Based on the coil arrangement density, coil heat flux density, and reactor design variables, the temperature rise value is determined using a temperature rise algorithm; the temperature rise algorithm includes: Temperature rise fitting function: Wherein, ΔT is the temperature rise value, H is the reactor height, p is the density of the coil arrangement, and q is the coil heat flux density; Based on the reactor design variables, through repeated genetic iterations, all reactor design variable sets and reactor functional parameter sets that meet the inductance constraints are obtained; Based on the reactor design variable group and the reactor functional parameter group, the reactor design variables with relatively better cost and temperature rise are obtained through non-dominated sorting. Based on the sorted reactor functional parameter group and reactor design variable group, the sorted reactor functional parameters and reactor design variables are used to generate the optimized reactor functional parameter frontier and the optimized reactor design variable frontier. Based on the optimized frontier of reactor functional parameters and the optimized frontier of reactor design variables, the optimal reactor functional parameters and optimal reactor design variables are obtained through the Pareto optimal approach.

2. The parameter optimization design method according to claim 1, characterized in that, The repeated genetic iterations include: Based on the obtained Nth group of reactor design variables, and according to the design range of the design variables, the N+1th group of reactor design variables is obtained through crossover mutation of the genetic algorithm. Based on the N+1th group of reactor design variables, the reactor functional parameters are recalculated using a comprehensive design algorithm. Determine whether the number of iterations has reached the maximum number of iterations. If it has, then obtain the reactor design variable group and the reactor function parameter group by collecting all reactor design variables and corresponding reactor function parameters.

3. The parameter optimization design method according to claim 1, characterized in that, The non-dominated sorting includes: The temperature rise value of the reactor functional parameters is fitted to the temperature rise target, and the conductor value of the reactor functional parameters is fitted to the cost target. Based on the temperature rise target and cost target, determine whether the temperature rise target of group N is greater than the temperature rise target of group N+1 and whether the cost target of group N is greater than the cost target of group N+1. If they are true, determine that the functional parameters of group N+1 reactors are superior to those of group N reactors, and prioritize the functional parameters of group N+1 reactors over those of group N reactors. At the same time, prioritize the design variables of group N+1 reactors over those of group N reactors.

4. A parameter optimization design system for a low-leakage magnetic ring type air-core reactor, characterized in that, include: Based on the parameter optimization design method according to any one of claims 1-3, the parameter optimization design system includes a parameter optimization design platform, the parameter optimization design platform being used for: Based on the reactor current carrying capacity, the conductor specifications are obtained, wherein the conductor specifications include the conductor diameter; Based on the reactor design variables, and according to the design range of the design variables, the initial first set of reactor design variables is obtained through random number initial value processing. The reactor design variables include the inner diameter of the coil, the number of coil turns, the number of reactor coils, and the inner diameter of the reactor. Based on the reactor design variables, the reactor functional parameters are obtained through comprehensive design algorithms under the condition of satisfying the inductance constraint. The comprehensive design algorithms include an inductance analytical algorithm, a temperature rise empirical algorithm, and a conductor quantity algorithm. The reactor functional parameters include the reactor's inductance value, temperature rise value, and conductor quantity. Based on the reactor design variables, through repeated genetic iterations, all reactor design variable sets and reactor functional parameter sets that meet the inductance constraints are obtained; Based on the reactor design variable group and the reactor functional parameter group, the reactor design variables with relatively better cost and temperature rise are obtained through non-dominated sorting. Based on the sorted reactor functional parameter group and reactor design variable group, the sorted reactor functional parameters and reactor design variables are used to generate the optimized reactor functional parameter frontier and the optimized reactor design variable frontier. Based on the optimized frontier of reactor functional parameters and the optimized frontier of reactor design variables, the optimal reactor functional parameters and optimal reactor design variables are obtained through the Pareto optimal approach.

5. A computer-readable storage medium, characterized in that, The readable storage medium stores executable instructions that, when executed by a processor, cause the processor to implement the parameter optimization design method as described in any one of claims 1-3.

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