Optimization of a Multi-Type PMSM Based on Pyramid Neural Network

Abstract

In this paper, a novel bat algorithm based on the quantum computing concept and pyramid neural network (PNN) is presented and applied to the electromagnetic motor optimization problem. Due to the problems of high loss, high temperature rise and threatening motor safety, it is necessary to optimize the design of high-speed permanent magnet synchronous motor (HPMSM) structure. In order to use less training data and avoid the problem of large computational costs due to repeated finite element simulation in the electromagnetic structure design, this paper adopted a performance-driven method to establish the PMSM model. This model could effectively reduce the dimensions of the parameter space and establish an effective high-quality model within a wide range of parameters. For the purpose of obtaining a reliable proxy model with less training data, this paper adopted a pyramid-shaped neural network, which could reduce the risk of overtraining and improve the utilization of specific problem knowledge embedded in the training data set. The quantum bat algorithm (QBA) was used to optimize the structure of the PMSM. Compared with the classical GA and PSO algorithms, the QBA has the characteristics of a rapid convergence speed, simple structure, strong searching ability and stronger local jumping mechanism. The correctness and effectiveness of the proposed PNN-based QBA method were verified using simulation analysis and a prototype test.