Performance optimization of switched reluctance machine with robust speed controller

Abstract

The switched reluctance motor with the rising emphasis on energy efficiency is becoming prominent in industrial commercial and vehicular applications in the recent scenario The requirement for variable speed drive VSD for the operation of electric vehicles EV is ever increasing The paradigm shift in the auto industry towards EVs took the electrical machines to a whole new level Due to the inherent advantages of SRM and remarkable advances in the field of power switching devices they can be considered as a serious candidate for propulsion of EV newlineThis study uses MagNet software for the design of a 8/6 SRM. The physical model is developed in MagNet and the corresponding parameters obtained are used for modeling the machine in MATLAB Simulink The controllers are also designed for providing a better performance In order to design the controller the commutation angles are selected using two algorithms namely the Particle Swarm Optimization and the Genetic Algorithm In this study the commutation angles are chosen such that the constraints and the objective function are satisfied Based on the values predicted from GA and PSO the best ones are selected and validated in MATLAB Simulink machine model at various load conditions and set speed ranges newlineThe controllers being employed in the study are the PID controller and Sliding mode Controller SMC They are designed to regulate the speed with maximum average torque and minimum torque ripple This makes the SRM suitable for variable speed applications. The optimum switching angles obtained from PSO are fed and the performances of the controllers were investigated It was observed that SMC provides a better rigid performance in comparison to PID controller newline newline