微特电机 ›› 2022, Vol. 50 ›› Issue (2): 31-35.

• 设计分析 • 上一篇    下一篇

基于响应面法的圆筒型永磁直线电机推力特性优化

付豪, 吴尧辉   

  1. 河南理工大学 电气工程及其自动化学院,焦作 45400
  • 收稿日期:2021-12-06 出版日期:2022-02-28 发布日期:2022-02-25
  • 通讯作者: 付豪(1993—),男,硕士研究生,研究方向为电机与电器。
  • 作者简介:吴尧辉(1964—),男,高级工程师,研究方向为电机与电器、电力系统继电保护等。
  • 基金资助:
    国家自然科学基金项目(61340015)

Optimization of Thrust Characteristics of Tubular Permanent Magnet Linear Motor Based on Response Surface Method

FU Hao, WU Yaohui   

  1. School of Electrical Engineering and Automation, Henan Polytechnic University,Jiaozuo 454000,China
  • Received:2021-12-06 Online:2022-02-28 Published:2022-02-25

摘要: 为了提高圆筒型永磁直线电机推力和降低电机的推力波动,采用响应面法和遗传算法对电机进行优化设计。利用响应面法建立电机平均推力和推力波动的解析模型,建立目标函数。为了计算由响应面法得到的二阶解析模型的系数,应用有限元法,采用Box-Behnken法对几何设计变量进行了数值实验。利用遗传算法作为搜索工具,对电机进行优化设计,以提高电机性能和减小永磁材料的成本。有限元仿真结果表明,与原始电机相比,优化后的电机平均推力提升了18.67%,推力波动降低了73.20%,用磁量减少了22.67%。

关键词: 圆筒型永磁直线电机, 推力, 推力波动, 响应面模型, 多目标优化

Abstract: In order to improve the thrust and reduce the thrust fluctuation of the tubular permanent magnet linear motor, the response surface method and genetic algorithm were used to optimize the design of motor. The analytical models of average thrust and thrust fluctuation of motor were established by response surface method, and the objective function was established. In order to calculate the coefficients of the second-order analytical model obtained by the response surface method, the geometric design variables were numerically tested by using the finite element method and the Box-Behnken method. Genetic algorithm was used as a search tool to optimize the design of the motor in order to improve the performance of the motor and reduce the cost of permanent magnet materials. The finite element simulation results showed that compared with the original motor, the average thrust of the optimized motor was increased by 18.67%, the thrust fluctuation was reduced by 73.20%, and the magnetic consumption was reduced by 22.67%.

Key words: tubular permanent magnet linear motor(TPMLM), thrust, thrust ripple, response surface model, multi-objective optimization

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