Rotor topology optimization of a synchronous reluctance motor based on deep neural network model

Abstract

Synchronous Reluctance Motors (SynRMs) have been widely used in some industrial fields because of their attractive characteristics, such as high efficiency, low cost, and simple structure. In order to reduce the torque ripple of the SynRMs, a non-parametric model is usually used to optimize the rotor structure. However, the conventional method has the problems of the low-accuracy and poor generalization ability. In this paper, an optimization method of the rotor structure is proposed to reduce the torque ripple by utilizing the deep learning algorithm. Firstly, the sample data of the relationship between the rotor structural parameters and torque ripple are obtained with the finite element analysis (FEA). The fast calculation model is established by the deep neural network (DNN). Then, with the goal of not weakening the torque density and minimizing the torque ripple, the immune clone algorithm (ICA) is utilized to optimize the structural parameters of the rotor at different operating points. Finally, the correctness and validity of the method are verified by the simulation analysis. It is concluded that the accuracy of the model established by DNN is acceptable. The proposed method can significantly reduce the torque ripple and increase the torque density.