Dry-type air-core reactor structure optimization method and system

Abstract

The invention discloses a dry-type air-core reactor structure optimization method and system, and mainly relates to the technical field of electrical equipment design optimization. Comprising the following steps: constructing an electromagnetic-thermal multi-physics field coupling finite element model; based on the constructed electromagnetic-thermal multi-physics field coupling finite element model, setting structural parameters of an improved particle swarm optimization algorithm; and based on the obtained parameters and the constructed model, carrying out optimization structure verification and outputting a verification result. The method has the beneficial effects that the limitation of a traditional optimization method is overcome, and the global search capability and the convergence speed are effectively improved, so that a more accurate and efficient optimization scheme is provided for reactor design.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment design optimization, and in particular to a dry-type air-core reactor structure optimization method and system based on finite element and improved particle swarm algorithm. Background Art

[0002] As a crucial electrical device in power systems, dry-type air-core reactors are widely used in power transmission and distribution, limiting current fluctuations, ensuring voltage stability, and protecting the power system. As power system power levels and equipment operating frequencies increase, the heat generated by reactors during operation gradually increases, particularly in the enclosure area, where the temperature rise becomes increasingly pronounced. Excessively high hotspot temperatures not only affect the thermal stability of the reactor but can also lead to performance degradation, operational failures, and even shortened equipment lifespan. Therefore, effectively controlling reactor temperature, particularly reducing the hotspot temperature in the enclosure area, has become a critical issue in reactor design.

[0003] Traditional reactor design methods typically rely on empirical rules or simple thermal analysis models, often ignoring the coupling effects between multiple physical fields. This limits the accuracy of design results and optimization efficiency. Especially in complex power system application scenarios, traditional methods often fail to meet the higher requirements for reactor performance, thermal management, and stability. Existing optimization methods primarily focus on optimizing a single physical field, lacking sufficient accuracy and effectiveness when dealing with complex electromagnetic-magnetic-thermal coupling problems. In recent years, a growing number of studies have turned to optimization methods based on multi-physics coupling, particularly combining finite element analysis with optimization algorithms, in an effort to achieve more precise thermal management and structural optimization in reactor design. Traditional particle swarm optimization (PSO) algorithms face challenges in tackling complex optimization problems involving multiple physical fields, such as slow convergence and a tendency to get stuck in local optimal solutions. This is particularly true for high-dimensional, multi-constrained structural optimization problems, where they exhibit low computational efficiency and poor global search capabilities. Furthermore, existing optimization methods often overlook issues such as uneven initial solution distribution and poor local convergence during the PSO search process, limiting their application to complex engineering problems.

[0004] Therefore, how to combine advanced computing methods in a complex environment of multi-physical field coupling to improve the optimization accuracy and computational efficiency of reactor design, especially the control of hotspot temperature, has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The purpose of the present invention is to provide a dry-type air-core reactor structural optimization method and system based on finite element and improved particle swarm optimization algorithm, which overcomes the limitations of traditional optimization methods, effectively improves the global search capability and convergence speed, and thus provides a more accurate and efficient optimization scheme for reactor design.

[0006] To achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: On the one hand, a method for optimizing the structure of a dry-type air-core reactor is provided, comprising the following steps: S1: Construct electromagnetic-thermal multi-physics field coupled finite element model; S2: Based on the constructed electromagnetic-thermal multi-physics field coupling finite element model, the structural parameters of the improved particle swarm optimization algorithm are set; S3: Based on the parameters obtained in step S2 and the model constructed in step S1, the optimized structure is verified and the verification results are output.

[0007] Preferably, step S1 includes: S11: In the COMSOL Multiphysics environment, the AC / DC module is used to establish an electromagnetic field model of the reactor and solve the Maxwell equations to obtain the magnetic field distribution. The material properties include the conductivity of the winding conductor. and thermal conductivity of the encapsulating material ; S12: The current density With Joule heat source Associate and establish the heat conduction equation:

[0008] in, is the material density, is the specific heat capacity, is the temperature field.

[0009] Preferably, step S2 includes: S21: Initialize the particle swarm, specifically: Use Tent chaos map to generate the initial position of particles and speed :

[0010]

[0011] Among them, the Tent chaos mapping formula is: ; S22: Adaptive inertia weight adjustment for the initialized particle swarm:

[0012] in, , , is the current iteration number, is the maximum number of iterations; S23: Set particle update rules:

[0013]

[0014] in, , 、 for Random numbers; S24: Based on steps S21-S23, the optimization of the objective function is completed: .

[0015] Preferably, step S3 includes: S31: The optimal parameters output from step S2 Substitute into the model in step S1; S32: Through the heat radiation equation Calculate surface heat dissipation, where is the emissivity, is the Stepan-Boltzmann constant; S33: Verify the hotspot temperature drop Is greater than or equal to .

[0016] Preferably, in the multi-physics field coupled finite element model, the bidirectional coupling of the electromagnetic field and the temperature is achieved by: Magnetic field distribution Determine the current density ; Joule heat source Input the heat conduction equation and update the temperature field ; Temperature feedback updates material conductivity ,in, .

[0017] Preferably, the adjustment strategy of the adaptive inertia weight is: When the fitness variance of the particle swarm is less than the threshold Force reset To escape from local optimum.

[0018] Preferably, the optimization parameter constraints also include thermal balance requirements:

[0019] in, is the convection coefficient, is the heat dissipation surface area.

[0020] On the other hand, an optimization system based on the above-mentioned dry-type air-core reactor structure optimization method is provided, comprising: A finite element solver module configured to perform COMSOL electromagnetic (AC / DC) and thermal bidirectional coupled calculations. Improved PSO algorithm engine for: built-in Tent chaos initializer and adaptive weight controller; The parameter verification interface is used to compare the temperature field distribution before and after optimization, and output a hotspot temperature drop report and stress safety factor.

[0021] Compared with the prior art, the beneficial effects of the present invention are: By using an improved particle swarm optimization algorithm to optimize the reactor's key structural parameters, the hotspot temperature in the encapsulation area is significantly reduced, improving thermal management performance. Simultaneously, precise simulation analysis using an electromagnetic-magnetic-thermal multi-physics field coupled finite element model ensures that the optimization results meet the temperature control objectives and mechanical stability requirements under actual operating conditions, thereby extending the equipment's service life and enhancing operational reliability, with significant engineering application value and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention; Figure 3 It is a structural schematic diagram of the dry-type air-core reactor of the present invention.

[0023] Description of the numbers in the accompanying drawings: 1. Rainproof cap; 2. Support bar; 3. Winding; 4. Encapsulation; 5. Star frame. DETAILED DESCRIPTION

[0024] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0025] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention, and should not be understood as limiting the present invention.

[0026] Example: like Figure 1 As shown, this embodiment provides a method for optimizing the structure of a dry-type air-core reactor, comprising the following steps: S1: Construct electromagnetic-thermal multi-physics field coupled finite element model; S2: Based on the constructed electromagnetic-thermal multi-physics field coupling finite element model, the structural parameters of the improved particle swarm optimization algorithm are set; S3: Based on the parameters obtained in step S2 and the model constructed in step S1, the optimized structure is verified and the verification results are output.

[0027] Wherein, step S1 is specifically as follows: S11: In the COMSOL Multiphysics environment, the AC / DC module is used to establish an electromagnetic field model of the reactor and solve the Maxwell equations to obtain the magnetic field distribution. The material properties include the conductivity of the winding conductor. and thermal conductivity of the encapsulating material ; S12: The current density With Joule heat source Associate and establish the heat conduction equation:

[0028] in, is the material density, is the specific heat capacity, is the temperature field; In the multiphysics coupled finite element model, the bidirectional coupling between electromagnetic field and temperature is achieved through the following methods: Magnetic field distribution Determine the current density ; Joule heat source Input the heat conduction equation and update the temperature field ; Temperature feedback updates material conductivity ,in,

[0029] In this embodiment, the parameters shown in Table 1 are set as initial parameters, and the model is constructed based on these parameters.

[0030] Table 1 Initial model parameters

[0031] Step S2 includes: S21: Initialize the particle swarm, specifically: Use Tent chaos map to generate the initial position of particles and speed :

[0032]

[0033] Among them, the Tent chaos mapping formula is: ; S22: Adaptive inertia weight adjustment for the initialized particle swarm:

[0034] in, , , is the current iteration number, is the maximum number of iterations; The adjustment strategy of adaptive inertia weight is: When the fitness variance of the particle swarm is less than the threshold Force reset To escape from local optimum S23: Set particle update rules:

[0035]

[0036] in, , 、 for Random numbers; S24: Based on steps S21-S23, the optimization of the objective function is completed: ; In this embodiment, the algorithm parameters are set as follows: \begin{align*} &w_{\max} = 0.9, \; w_{\min} = 0.4, \; t_{\max} = 50 \\ &c_1 = c_2 = 1.5, \; \text{Number of particles} = 30 \\ &X_{\text{Range}}: \begin{cases} Encapsulation thickness \in [10, 30]\,\text{mm} \\ \text{Airway width} \in [5, 15]\,\text{mm} \\ Airway height in [300, 500] mm \end{cases} \end{align*}; The optimization results are shown in Table 2 (convergence at the 38th generation).

[0037] Table 2 Optimization results

[0038] Step S3 includes: S31: The optimal parameters output from step S2 Substitute into the model in step S1; S32: Through the heat radiation equation Calculate surface heat dissipation, where is the emissivity, is the Stepan-Boltzmann constant; S33: Verify the hotspot temperature drop Is greater than or equal to ; The optimization parameter constraints also include thermal balance requirements:

[0039] in, is the convection coefficient, is the heat dissipation surface area; The specific performance code is as follows: \begin{align*} \text{left form}&: \int_{\Omega} QdV = 1.25 \times 10^3 \, \text{W} \\ \text{Right-form}&: h A_s (T_{\text{max}} - T_{\text{amb}}) = 25 \times 8.2\times (119-25) \approx 1.23 \times 10^3 \, \text{W} \\ &\Rightarrow 1.25 \times 10^3 \, \text{W} \leq 1.23 \times 10^3 \, \text{W} \quad (\text{error}<2\%, \text{meets the requirement}) \end{align*}; In this embodiment, the verification of the temperature field is shown in Table 3.

[0040] Table 3 Comparison of finite element simulation

[0041] It can be seen that the improved PSO algorithm combined with a specific parameter range (such as k∈[0.18, 0.22]), supported by the finite element model, can significantly improve the thermal management performance of the dry-type air-core reactor, providing a sufficient implementation basis for the patent.

[0042] like Figure 2-3 As shown, this embodiment further provides an optimization system based on the above-mentioned dry-type air-core reactor structure optimization method, including: A finite element solver module configured to perform COMSOL electromagnetic (AC / DC) and thermal bidirectional coupled calculations. Improved PSO algorithm engine for: built-in Tent chaos initializer and adaptive weight controller; The parameter verification interface is used to compare the temperature field distribution before and after optimization, and output a hotspot temperature drop report and stress safety factor.

[0043] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for optimizing the structure of a dry-type air-core reactor, characterized in that: The following steps are involved: S1: Construct electromagnetic-thermal multi-physics field coupled finite element model; S2: Based on the constructed electromagnetic-thermal multi-physics field coupling finite element model, the structural parameters of the improved particle swarm optimization algorithm are set; S3: Based on the parameters obtained in step S2 and the model constructed in step S1, the optimized structure is verified and the verification results are output.

2. A dry-type air-core reactor structure optimization method according to claim 1, characterized in that: Step S1 includes: S11: In the COMSOL Multiphysics environment, the AC / DC module is used to establish an electromagnetic field model of the reactor and solve the Maxwell equations to obtain the magnetic field distribution. The material properties include the conductivity of the winding conductor. and thermal conductivity of the encapsulating material ; S12: The current density With Joule heat source Associate and establish the heat conduction equation: in, is the material density, is the specific heat capacity, is the temperature field.

3. The method for optimizing the structure of a dry-type air-core reactor according to claim 1, wherein: Step S2 includes: S21: Initialize the particle swarm, specifically: Use Tent chaos map to generate the initial position of particles and speed : Among them, the Tent chaos mapping formula is: ； S22: Adaptive inertia weight adjustment for the initialized particle swarm: in, , , is the current iteration number, is the maximum number of iterations; S23: Set particle update rules: in, , 、 for Random numbers; S24: Based on steps S21-S23, the optimization of the objective function is completed: 。 4. The method for optimizing the structure of a dry-type air-core reactor according to claim 1, wherein: Step S3 includes: S31: The optimal parameters output from step S2 Substitute into the model in step S1; S32: Through the heat radiation equation Calculate surface heat dissipation, where is the emissivity, is the Stepan-Boltzmann constant; S33: Verify the hotspot temperature drop Is greater than or equal to .

5. The method for optimizing the structure of a dry-type air-core reactor according to claim 1, wherein: In the multi-physics field coupled finite element model, the bidirectional coupling between electromagnetic field and temperature is achieved in the following way: Magnetic field distribution Determine the current density ; Joule heat source Input the heat conduction equation and update the temperature field ; Temperature feedback updates material conductivity ,in, .

6. A dry-type air-core reactor structure optimization method according to claim 3, characterized in that: The adjustment strategy of the adaptive inertia weight is: When the fitness variance of the particle swarm is less than the threshold Force reset To escape from local optimum.

7. The method for optimizing the structure of a dry-type air-core reactor according to claim 4, characterized in that: The optimization parameter constraints also include thermal balance requirements: in, is the convection coefficient, is the heat dissipation surface area.

8. An optimization system based on the dry-type air-core reactor structure optimization method according to claim 1, characterized in that: include: A finite element solver module configured to perform COMSOL electromagnetic (AC / DC) and thermal bidirectional coupled calculations. Improved PSO algorithm engine for: built-in Tent chaos initializer and adaptive weight controller; The parameter verification interface is used to compare the temperature field distribution before and after optimization, and output a hotspot temperature drop report and stress safety factor.

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