Particle Swarm Optimization Approach for Optimal Design of Switched Reluctance Machine

Abstract

Problem statement: Switched Reluctance Motors (SRMs) are widely used in various applications due to their inherent simplicity and rugged construction In SRM, torque output and torque ripple are sensitive to stator and rotor pole arcs and their selection is a vital part of SRM design process. In this study Particle Swarm Optimization technique is proposed for determining optimum pole arc of SRM. Approach: The problem of determining optimum pole arc is formulated as a multiobjective optimization problem with the objective of maximizing average torque and minimizing torque ripple. A comprehensive program based on analytical model is developed in Matlab to compute the value of inductance and average torque. Results: The optimization procedure is tested on 8/6, four-phase, 5 HP, 1500 rpm SRM. The results are compared and investigated with those obtained from Genetic Algorithm (GA) technique and Finite Element Analysis(FEA) simulation. Conclusion: The results demonstrate that the proposed method is effective and outperforms GA in terms of solution quality, accuracy, constraint handling.