Investigation on Torque Sharing Function for Torque Ripple Minimization of Switched Reluctance Motor: A Flower Pollination Algorithm Based Approach

Abstract

ABSTRACT Switched reluctance motor (SRM) drive has outstanding features like robust construction, double saliency, fault tolerance capability, and heat withstanding capability. Despite these, torque ripple is an intrinsic characteristic of SRM because of double salient geometry, air gap length, and switching sequence of motor phase currents, making it unsuitable for slick torque and high dynamic performances applications. This paper discusses the novel Torque Sharing Function (TSF) implementation and the flower pollination algorithm-based intelligent controller to minimize the torque ripples in Switched Reluctance Motor (SRM). TSF values depend on commutation angles during turn on, turn off, and overlapping angles. The proposed TSF and flower pollination algorithm evaluates the performance parameters using the machine model and controls the torque ripples for a wide range of speeds. Additionally, optimal results are obtained using three TSFs functions with valuable guidance provided for TSF selection. The proposed TSF with flower pollination algorithm-based intelligent controller finds its suitability for online evaluation of optimal turn on, turn off, and overlapping angles. The performance of the proposed method has effectively reduced the torque ripple in SRM than the Differential Evolution (DE) algorithm.