Soft computing approaches of direct torque control for DFIM Motor's

Abstract

Conventional Direct Torque Control (DTC) is widely used for torque and speed control in doubly-fed induction machines (DFIM). However, it has notable drawbacks, including high torque and flux ripples, which generate acoustic noise and reduce system performance. To address these limitations, several advanced approaches have emerged. This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. Results show that DTC-ANN and DTC-SVM stand out for their ripple reduction performance, making them particularly suitable for applications requiring high precision. Additionally, DTC-FL and DTC-SMC excel in robustness against system parameter variations, a valuable asset for evolving industrial environments. Optimization approaches such as DTC-GA, DTC-ACO, and DTC-RTO contribute to reducing switching losses and improving energy efficiency, a crucial aspect for large-scale applications. Finally, P-DTC offers excellent dynamics and precise speed tracking, making it ideal for rapid response systems. These findings provide valuable insights for researchers and engineers seeking to optimize modern DTC system performance according to the specific needs of their applications.