ANN-Based Improved Direct Torque Control of Open-End Winding Induction Motor
Kaif Ahmed Lodi, Abdul R. Beig, Khaled Al Jaafari, Zeyar Aung
Abstract
This article presents an improved artificial neural network (ANN)-based direct torque control (DTC) algorithm for open-end winding induction motor (OEWIM) sensorless drives. The ANN replaces the traditional look-up table and serves as an efficient data storage system, allowing for optimal voltage vector selection based on small values of torque error and flux error for every angular position of the stator flux vector. By replacing the hysteresis controllers, improved torque and flux tracking are achieved with improved transient response. The proposed ANN-based DTC is verified through simulation as well as experimental tests. The experimental results and comparative results are presented.