Optimization of Synchronous Reluctance Motor Based on Radial Basis Network

Abstract

This paper presents surrogate-model based optimization for synchronous reluctance motor (SynRm) with transversally laminated rotor. A radial basis function (RBF) model with 12 input variables and three outputs is first trained. A dataset is obtained using finite element method to estimate parameters of RBF model. By building RBF model, the RBF network can predicts the outputs of the SynRm with good accuracy Using non-dominated sorting genetic algorithm (NSGA II), pareto front is obtained. The SynRm is designed to maximize the maximum developed torque and power factor of the motor with constrained torque ripple.