Genetic and particle swarm optimization algorithms based direct torque control for torque ripple attenuation of induction motor. (2022)

• Record Type: Journal Article
• Authors: Elgbaily, Mohamed; Anayi, Fatih; Packianather, Michael
• Is Part Of: Materials today. Volume 67:Part 4(2022)
• Page Start: 577
• Page End: 590
• Publication Date: 2022
• Subjects: Genetic Algorithm -- Particle Swarm Optimization -- Direct Torque Control -- Induction Motors; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.08.293
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This paper introduces analysis, control, and comparison of two benchmarking optimization approaches called Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Direct Torque Control (DTC) of a three-phase Induction Motor (IM). This study aims to determine the most efficient and robust of the two different metaheuristic optimization techniques including PID-PSO and PID-GA for DTC of IM. The purpose of the proposed control technique that has been presented is to get over the most significant drawback of DTC, which is a high level of torque output. The issue of torque ripples needs to be reduced to a significant amount using the two proposed control methods PSO-DTC and GA-DTC. As a result, PSO-DTC is the most applicable scheme. The proposed PID-PSO of DTC provided an excellent work performance for IM system drive. The comparison results of the suggested control methods showed a significant improvement of the control system compared to the classical DTC. The result is a high fidelity estimate of electromagnetic torque and speed for computation of motor parameters. A high ripple suppression capability was achieved by the PSO-DTC, which was measured at 22.5 % out of 47.28 % for the traditional approach. Both proposed control schemes were implemented using MATLAB/Simulink platform.