Optimization design of Switched Reluctance Motor based on Particle Swarm Optimization

Abstract

This paper introduces the Particle Swarm Optimization (PSO) in order to solve the problem that the current stochastic and deterministic optimization methods have not yet obtained the perfect solution to avoid local optimal solution, and the optimal search convergence speed is slow. According to the special nature of Switched Reluctance Motor (SRM) and its design features, the paper enhances PSO global search capability by adjusting and improving conventional algorithm. Finally, this paper describes the design optimization procedure of SRM using the improved PSO with the objective of maximizing average torque and minimizing torque ripple. PSO optimizes the motor design and achieved satisfactory results. The results are compared with GA based on a standard 1.1kW, 8/6 pole, 4 phases SRM. The obtained results are encouraging and verified using Finite Element Analysis (FEA), and the results mean that the adaptive PSO has shown promising success in the design optimization of SRM.