ANN Assisted Improved Duty-DTC Algorithm for Open-End Winding Induction Motor Drive

Abstract

This article presents an improved duty ratio-based direct torque control (DDTC) algorithm for open-end winding induction motor (OEWIM) sensorless drives. An artificial neural network (ANN) algorithm is proposed to select the optimal voltage vector and its optimal duty ratio simultaneously. The ANN reduces the computational complexity and dependency on motor parameters. The torque tracking error is reduced by compensating for the effect of motor speed on torque. The proposed ANN-DDTC algorithm is verified through computer simulation and experimental tests. The experimental results and comparative results are presented.