关键词: 无轴承永磁同步电机, 鲸鱼优化算法, 多目标优化, 随机森林回归

Abstract: An optimization method based on random forest regression and whale optimization algorithm is proposed for a bearingless permanent magnet synchronous motor, aiming to improve the motor′ s torque and levitation performance. By selecting appropriate motor structural parameters as variables, corresponding simulation experiments are designed, and random forest regression is used to derive a regression polynomial. Through the whale optimization algorithm is employed to obtain the pareto optimal solution. Corresponding simulations were established for the optimization points and compared. The results showed error between simulation data and the algorithm fitting values was small, and the performance parameters of the motor were all effectively improved.