PERFORMANCE OPTIMIZATION OF SWITCHED RELUCTANCE MOTOR DRIVE USING META-HEURISTIC TECHNIQUE

Abstract

This thesis presents a population centric teaching-learning based optimisation (TLBO) technique for optimizing the performance of switched reluctance motor (SRM) drives. SRMs have proven to be a good substitute of Permanent Magnet (PM) motors despite minor disadvantages like noise, vibration, and torque ripple. In this work, TLBO is used to compute the turn-on and turn-off angles of the stator phase windings to optimise a multi-objective function comprising the SRM drive performance indices like average torque, torque per unit copper loss and softness factor (inverse of the torque ripple factor) while subjecting the drive to the Indian Drive Cycle (IDC). Two real time controllers have been used to tune the turn-on and turn-off angles of the stator phase windings. The optimal turn-on and turn-off angles are used to generate the gate pulses for the converter switches. A hysteresis controller is incorporated to regulate the stator current between prescribed limits. The entire model was developed in SIMULINK. The improved SRM performance indices show the effectiveness of the proposed controller.