永磁驱动器的多目标参数优化设计
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摘要
为了满足永磁驱动器在实际运行中具有较大的输出转矩和较小的涡流损耗的目的,建立了永磁驱动器的3维有限元模型,分析了永磁驱动器的主要结构参数对输出转矩和涡流损耗的影响。采用正交实验设计方法,获得了用于非线性永磁驱动器建模的样本空间。采用支持向量机建立永磁驱动器的非线性回归模型,并用多目标微粒群算法进行永磁驱动器的结构参数多目标优化,得到了最优结构参数解。有限元仿真实验对比优化前后永磁驱动器的涡流密度和受力分布的结果,验证了该优化方法用于永磁驱动器结构参数优化是可行的。优化结果使永磁驱动器可在涡流损耗较小的情况下获得较大的输出转矩,从而进一步提高系统的工作效率。
The three-dimensional FEM(Finite Element Method) model of a permanent magnet drive is established. The influence of the main configuration parameter in permanent magnet drive on the output torque and the eddy current loss is analyzed. To obtain sample space used to establish the nonlinear regression model,the method of orthogonal experimental design is arranged. In order to require both larger output torque and smaller eddy current loss in the operation of the permanent magnet drive,the nonlinear regression model of the permanent magnet drive is established using the Support Vector Machine(SVM). Then the structure parameters of the permanent magnet drive is optimized using multi-objective particle swarm algorithm. At last,the FEM simulating results show that the parameter optimization method based on SVM and multi-objective particle swarm algorithm is feasible for researching the permanent magnet drive, and the optimization results improve the work efficiency of the system.
The three-dimensional FEM(Finite Element Method) model of a permanent magnet drive is established. The influence of the main configuration parameter in permanent magnet drive on the output torque and the eddy current loss is analyzed. To obtain sample space used to establish the nonlinear regression model,the method of orthogonal experimental design is arranged. In order to require both larger output torque and smaller eddy current loss in the operation of the permanent magnet drive,the nonlinear regression model of the permanent magnet drive is established using the Support Vector Machine(SVM). Then the structure parameters of the permanent magnet drive is optimized using multi-objective particle swarm algorithm. At last,the FEM simulating results show that the parameter optimization method based on SVM and multi-objective particle swarm algorithm is feasible for researching the permanent magnet drive, and the optimization results improve the work efficiency of the system.
引文
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[2]Elies P,Lemarqrand G.Analytical study of radial stability of permanent magnet synchronous couplings[J].IEEE Transactions on Magnetics,1999,35(4):3421-3423.
[3]Ravaud R,Lemarquand G,Lemarquand V,et al.Permanent magnet couplings:Field and torque three-dimensional expressions based on the coulombian model[J].IEEE Transactions on Magnetics,2009,45(4):1950-1958.
[4]陈本永,雷勇,李延宝,等.磁悬浮式纳米级微动工作台的理论分析和建模研究[J].仪器仪表学报,2006,27(9):1125-1128.(Chen B Y,Lei Y,Li Y B,et al.Theoretical analysis and modeling of a magnetic levitation micromotion stage[J].Chinese Journal of Scientific Instrument,2006,27(9):1125-1128.)
[5]A Wallace,A von Jouanne,A Ramme.A permanent magnet coupling with rapid disconnect capability[J].IET Electric Motors and Drives,2000,82(1):58-64.
[6]Ying Z D,Xu X F,Zhu J A.Analysis of simulation design of the disc eddy current braking device[C].2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(CMCE2010).Changchun,2010:309-311.
[7]Ravaud R,Lemarquand V,Lemarquand G.Analytical design of permanent magnet radial couplings[J].IEEE Transactions on Magnetics,2010,46(11):3860-3865.
[8]Guohai Liu,Lingling Chen,Wenxiang Zhao,et al.Internal model control of permanent magnet synchronous motor using support vector machine generalized inverse[J].IEEE Transactions on Industrial Informatics,2013,9(2):890-898.
[9]Chuanhou Gao,Ling Lian,Shihua Luo.Modeling of the thermal state change of blast furnace hearth with support vector machines[J].IEEE Transactions on Industrial Electronics,2012,59(2):1134-1145.
[10]任子晖,王坚.加速收敛的粒子群优化算法[J].控制与决策,2011,26(2):201-206.(Ren Zihui,Wang Jian.Accelerate convergence particle swarm optimization algorithm[J].Control and Decision,2011,26(2):201-206.)
[11]Modares H,Alfi A,Fateh M.Parameter identification of chaotic dynamic systems through an improved particle swarm optimization[J].Expert Systems with Applications,2010,37(5):3714-3720.
[12]Abdelsalam A Eajal,El-Hawary M E.Optimal capacitor placement and sizing in unbalanced distribution systems with harmonic consideration using particle swarm optimization[J].IEEE Transaction on Power Delivery,2010,25(3):1734-1741.
[13]Omkar S N,Khandelwal R,Ananth T V S,et al.Quantum behaved particle swarm optimization(QPSO)for multi-objective design optimization of composite structures[J].Expert Systems with Applications,2009,36(8):1312-1322.
[14]Kalyanmoy Deb.Multi-objective optimization using evolutionary algorithm[M].Chichester:John Wiley&Sons,2001.
[15]Knowles J D,Corne D W.Approximating the non-2dominated front using the Pareto archived evolutionary strategy[J].Evolutionary Computation,2000,8(2):149-172.