Sensorless DTC Based on Artificial Neural Network for Independent Control of Dual 5-Phase Induction Machine Fed by a Three-Level NPC Inverter

Abstract

This paper deals with an independent control of two parallel-connected five-phase induction machines (FPIM) fed by NPC three-level inverter. In effect a direct torque control (DTC) of two parallelconnected FPIMs has been developed to ensure a simple and fast decoupled control over the stator flux and electromagnetic torque and high performance in event of machine parameters disturbances. However, DTC suffer from the torque and flux ripples due the hysteresis controllers. In this context, an intelligent DTC based on Artificial Neural Network (ANN) has been proposed to minimize the stator flux and electromagnetic torque ripples in a steady and transient states and therefore reduction of the stator current harmonic THD. hence, Intelligent ANN hysteresis controllers and switching table of the DTC have been incorporated to select the optimum voltage vector of the NPC-VSI to be applied in the control of two parallel-connected FPIM. Moreover, a virtual current sensor (VCS) approach is proposed to configure a fault-tolerant control scheme (FTC). The effectiveness of the proposed (DTC-ANN) and the FTC have been checked by an intensive simulation in different operating conditions.