Artificial neural network based direct torque control of induction motor drives

Abstract

Direct torque control (DTC) of induction motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector.This paper presents simple structured neural networks for flux position estimation, sector selection and stator voltage vector selection for induction motors using direct torque control (DTC) method. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The conventional flux position estimator, sector selector and stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.