Optimum Design of Switched Reluctance Machine Using Adaptive Particle Swarm Optimization

This paper presents swarm intelligence based Adaptive Particle Swarm Optimization (APSO) technique to determine optimum design of Switched Reluctance Machine (SRM). In APSO technique, the inertia weight factor is made adaptive on the basis of objective functions of the current and best solutions to avoid premature convergence. The SRM design is treated as nonlinear multivariable constrained optimization problem. The objective functions for obtaining desired design are maximizing torque density, minimizing torque ripple and minimizing copper loss with stator and rotor pole arc as design variables. The potential of the proposed approach is tested on 8/6 four-phase, 5 HP, 1500 rpm SRM and the results are compared with those obtained from Genetic Algorithm (GA) and classical PSO technique. The results demonstrate that the proposed method is superior in terms of solution quality, accuracy, robustness and computational efficiency.