Neural Direct Torque Control for Induction Motor under Voltage Source Inverter Open Switch Fault

Abstract

Background: The study of induction motor behavior during abnormal conditions due to the presence of faults and the possibility to diagnose these abnormal conditions has been a challenging topic for many electrical machine researchers. Objective: Direct Torque Control (DTC) is applied to the control of an induction motor in healthy and an open circuit fault in the PWM three phase voltage fed inverter. Neural DTC is developed and used to improve the dynamic behavior of the drive system under faulty switch occurrence. Methods: The validity of the proposed control scheme is tested under normal conditions and switching fail in the Voltage Source Inverter (VSI) caused by an open circuit. Through a simulation testing of an induction motor drive system at different speed references, a comparison between basic DTC and Neural DTC is performed. Results: Simulated results on 1.5-kW induction motor drive show the performance of the proposed control in normal and faulty cases. The stator current, flux, torque, and speed at different references are determined and compared in the above techniques using MATLAB-SIMULINK. Conclusion: A Neural Network (NN) DTC control system under an open switch fault is proposed without the need for classical switching table. The use of hybrid intelligent techniques aims to improve the DTC performances in case of multiple faults occurrence.