Comparative study of control strategies applied to the five-phase induction machine

Abstract

This thesis focuses on the study and implementation of advanced control techniques for five-phase induction motors, which are increasingly valued for their enhanced reliability, superior performance, and fault-tolerant capabilities compared to conventional three-phase systems. The research explores and compares three prominent control strategies: Direct Torque Control (DTC), Direct Torque Control with Space Vector Modulation (DTC-SVM), and Finite Control Set Model Predictive Control (FCS-MPC). Additionally, the thesis introduces two significant advancements: the integration of artificial neural networks (ANNs) to enhance control performance and the implementation of a Model Reference Adaptive System (MRAS) for speed sensorless control. The results highlight the strengths and limitations of each technique, providing insights into their suitability for various industrial applications. This work paves the way for further research into hybrid approaches that combine the strengths of these methods and tailor them to handle fault conditions, fully harnessing the advantages offered by five-phase induction motors.