基于RBF近似模型的低速永磁电机齿槽转矩优化  

Optimization of Cogging Torque for Low-Speed Permanent Magnet Motor Based on RBF Approximation Model

作　　者：刘雪杰 周瑾[1] 金超武[1] 汪达鹏 LIU Xue-jie;ZHOU Jin;JIN Chao-wu;WANG Da-peng(Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学

出　　处：《微特电机》2020年第1期25-29,共5页Small & Special Electrical Machines

基　　金：江苏省重点研发资助项目(BE2016180);江苏高校“青蓝工程”资助项目;南京航空航天大学校研究生创新基地(实验室)开放基金项目(kfjj20180504)

摘　　要：针对一台32极36槽的低速永磁电机,利用Isight优化平台,基于径向基(RBF)神经网络方法和最优拉丁超立方的样本空间,分别建立齿槽转矩、漏磁系数与设计变量间的近似模型,在近似模型的基础上采用多岛遗传算法(MIGA)寻优。优化后的齿槽转矩有限元仿真结果与RBF近似模型预测值的相对误差为1.6%,比初值降低59%,说明了该优化方法的有效性;Isight命令流的使用,提高了优化效率。Aiming at a low-speed permanent magnet motor with 32 poles and 36 slots,on the Isight optimization platform,approximate models between cogging torque,magnetic flux leakage coefficient and design variables were set up separately by optimal latin hypercube sampling and the radial basis function(RBF)method,and multi island genetic algorithm(MIGA)was used to the optimal design.The relative error between the finite element simulation results and the predicted value of the RBF approximation model was 1.6%,and the optimized cogging torque was 59%lower than the initial value,which proves the effectiveness of the optimization method.The use of Isight command stream improved the efficiency of optimization.

关 键 词：永磁同步电机 齿槽转矩 径向基神经网络法 ISIGHT 多岛遗传算法 有限元仿真 

分 类 号：TM341[电气工程—电机] TM351