Optimizing Torque Ripples In Switched Reluctance Motor Via ANN

Abstract

This paper provides a detailed analysis of the performance of SRM motors, focusing on reducing high ripples. In this modern world, there are various types of motors available, among them SRM is getting recognition cause of its inherent advantages such as simple construction, high speed, low cost, high efficiency, and reduced dependency on rare-earth materials and offering significant advantages of both IM and DC brush motors. These traits position SRM as a superior choice among variable-speed motors. But its performance is affected by high ripples and noise. To address this issue, the research inspects the application of Artificial Neural Networks (ANNs) to attenuate torque ripples in SRMs and build up their overall performance. Artificial neural networks are found to be a favourable technique because of their accurate results, simplicity, speed, and stability compared to other methods like PI and HCC, which are undesirable in transient responses. A comprehensive study was performed using MATLAB SIMULINK to demonstrate the positive outcomes, including the presented waveforms