SRM Torque Ripple Reduction Using Grey Wolf and Teaching and Learning Based optimization in Hysteresis Control

The SRM has slowly but steadily made it’s way in the motor market through small but significant applications in various fields ranging from household items like vacuum cleaner to big mining vehicles fairly due to its reliability and fault tolerant operation. Despite many advantages a few drawbacks that limit the market share of SRM are acoustic noise and pulsating torque profile. We propose here a Switched Reluctance Model utilizing Hysteresis control in which we determine the ideal turn ON and turn OFF angles for the Switched Reluctance Machine using the Teaching and Learning Based optimization technique and Grey Wolf optimization technique to reduce the torque ripple and increase average total torque.