Genetic Algorithm Based Optimal Design of Switching Circuit Parameters for a Switched Reluctance Motor Drive

Abstract

In this paper, an optimization method based on genetic algorithms (GA) is applied to find the best design parameters of the switching power circuit for a switched reluctance motor (SRM). The optimal parameters are found by GA with two objective functions, i.e. efficiency and torque ripple. A fuzzy expert system for predicting the performance of a switched reluctance motor has been developed. The design vector consists of design parameters, and output performance variables are efficiency and torque ripple. An accurate analysis program based on improved magnetic equivalent circuit (IMEC) method has been used to generate the input-output data. These input-output data are used to produce the optimal fuzzy rules for predicting the performance of SRM. Table look-up scheme and gradient decent training are used for optimal fuzzy prediction designed. The results of the optimal switching power circuit design for a 8/6, four phase, 4 kW, 250 V, 1500 rpm SR motor.