Optimal dynamic and steady‐state performance of switched reluctance motor using water cycle algorithm

Abstract

Saliency and nonlinear magnetization characteristics of switched reluctance motor (SRM) are causes for its poor performance regarding torque ripples. This paper presents an innovative methodology based on a water cycle algorithm (WCA) for SRM speed control with minimum torque ripples and maximum torque per ampere. WCA enhances the dynamic and steady-state performance of this motor by optimizing a multiobjective function consisting of three parts. The first part is the integral time absolute error (ITAE) of motor speed with respect to a desired reference speed, the second part is an index for a motor torque smoothness factor (TSF), and the third part is the torque per ampere ratio (TAR). The desired output of WCA is the appropriate gains of the PI speed controller and optimum turn on and off angles of the H-bridge driving circuit. The results obtained from WCA are used to construct an adaptive controller-based artificial neural network (ANN) to automatically tune the gains of the PI speed controller and the turn on and off angles. The ANN controller ensures superior steady-state and dynamic motoring operation over a wide range of speed commands and different loads. The results of WCA are verified by other well-known meta-heuristic optimized techniques, such as the Genetic algorithm (GA). The suggested method exhibits an excellent speed transient response in addition to minimum torque ripples and maximum TAR.

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