Artificial intelligence based direct torque control of induction motor drive system

• Main Authors: Alhamin, Sarinah Binti, Sim, Sy Yi, Chien, Siong Kai, Zambri, Nor Aira, Mustafa, Farahiyah, Alvin Lim, Meng Siang
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers
• Online Access: http://eprints.uthm.edu.my/2604/1/KP%202020%20%28242%29.pdf; http://eprints.uthm.edu.my/2604/

In this project, a three-phase Induction motor (IM) under the direct torque control (DTC) technique is studied. IM is known for its simple engines and its self-starter feature but it always suffered a setback in the area of torque and speed control as it is a highly coupled nonlinear plant and proves to be most complex and expensive speed drive. The application of direct torque control (DTC) is beneficial for fast torque reaction in IM but provide high torque and ripples due to harmonic effects. Thus, the speed control of induction motor is important to achieve maximum torque and efficiency. The aim of this study is to improve tracking performance of the induction motor drive using artificial intelligence control system. A method for controlling induction motor drive is presented with Proportional-Integral (PI) controller and Artificial Neural Networks (ANNs) for performance comparison. MATLAB/SIMULINK software is used to develop a three-phase 2 pole-cage type induction motor model. Also the performances of the two controllers have been verified in terms of its speed and torque responses. The ANN is trained so that the speed of the drive tracks the reference speed. This study proved that the performance and dynamics of the induction motor are enhanced using ANN controller as compared with PI controller.