Evolutionary Computation Based Multi-objective Pole Shape Optimization of Switched Reluctance Machine


This paper presents the application of elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II) to determine optimum pole shape design for performance enhancement of Switched Reluctance Machine (SRM). 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. The problem of determining optimal pole arc is formulated as a multi-objective optimization problem with the objective of maximizing average torque, minimizing torque ripple and copper loss. In order to account for the geometry as well as for the nonlinearity of material utilized, the Finite Element Method (FEM) is used to determine the performance of the machine. The proposed optimization technique is applied to determine optimal pole shape of an 8/6, four-phase, 5 HP, 1500 rpm SRM. The results show the effectiveness of the proposed approach and confirm the application of NSGA-II as a promising tool for solving SRM design problems. The results obtained by NSGA-II are compared and validated with classical multi-objective approach based on weighted sum method using Differential Evolution (DE) algorithm.