An integrated converter model and enhanced control approach for torque ripple minimisation of switched reluctance motor: a CMFG-RNN technique. Issue 10 (23rd August 2020)

• Record Type: Journal Article
• Authors: Chaple, Megha D.; Bodkhe, Sanjay B.
• Is Part Of: International journal of ambient energy. Volume 41:Issue 10(2020)
• Page Start: 1142
• Page End: 1153
• Publication Date: 2020-08-23
• Subjects: Switch reluctance motor -- torque ripple -- CMFG -- RNN -- FOPID controller and integrated converter; Power resources -- Periodicals; Renewable energy sources -- Periodicals; 621.04205
• DOI: 10.1080/01430750.2018.1507927
• Languages: English
• ISSNs: 0143-0750

Abstract

ABSTRACT: In this paper, a control mechanism for speed control of switched reluctance motor (SRM) with torque ripple reduction utilising moth flame optimisation algorithm and genetic algorithm with recurrent neural network (CMFG-RNN) is presented. The control mechanism comprises of FOPID speed controller in the external loop and current controller in the internal loop along with control of turn-on and turn-off angles for the SRM. The issue of getting the ideal estimations of proportional, integral and derivative gains for both speed and current controller along with the turn-on and turn-off angles is considered as a multi-objective optimisation problem with the goals of limiting the Integral Squared Error of speed and torque ripple. Simulations of CMFG-RNN-based control of SRM are done utilising SIMULINK/MATLAB software. With a specific end goal to assess the strong execution of CMFG-RNN, the comparison of current, speed, flux and torque under different techniques are considered. The outcomes got by CMFG-RNN are contrasted and validated with ALO and NSGA-II techniques. The outcomes uncover that CMFG-RNN-based controllers give better execution as far as lesser torque ripple and quick settling time because of its systematic random search capabilities, thereby enhancing the dynamic execution of SRM drive.