Optimization of Torque Ripples in an Interior Permanent Magnet Synchronous Motor Based on the Orthogonal Experimental Method and MIGA and RBF Neural Networks

Abstract

Interior permanent magnet synchronous motors (IPMSMs) have high power densities and speed control performance, and they are widely used in the industry. The problem of reducing the torque ripple of an IPMSM is one of the hot issues in the field of electrical machine design. In order to determine the optimal combination of the geometric parameters to reduce the torque ripple of an IPMSM, a range analysis was conducted on the data from the orthogonal experiments in this study, dividing the rotor geometric parameters into two categories (important and ordinary) based on their degree of impact on the torque ripples of the IPMSM. Thereafter, an optimization of the ordinary parameters was carried out based on the results of the range analysis, whereas the optimization of the important parameters was carried out through a method that combined a multi-island genetic algorithm (MIGA) and Radial Basis Function (RBF) neural networks. The torque ripple of the IPMSM was effectively reduced without materially affecting the output power. Finally, the results of this optimization process were verified using a finite element simulation. The optimization method used in this study divided the motor geometric parameters into two categories and applied a different method of optimization to each parameter type, so it was able to efficiently optimize multiple geometric parameters for the IPMSM.