Direct torque control of induction machines utilizing mutilevel inverter and artificial intelligent

Abstract

This thesis presents a high performance Direct Torque Control (DTC) of induction machine (IM) drives. A summary of the theoretical aspects and principles of DTC are given with emphasis on two major problems, i.e. high torque ripple and variable switching frequency. In order to solve these problems, this thesis proposed direct torque control of induction machines utilizing multilevel inverter and artificial intelligent. It proposes to use three, five and seven level cascaded and diode clamped inverter topology which results in further torque ripple minimization compare to the two level inverter-based conventional DTC. It also proposed a new Fuzzy DTC using Sugeno as the inference method to replace the hysteresis comparators in the conventional DTC, which results in reducing the flux ripples significantly as well as reducing the Total Harmonic Distortion (THD) of the phase current since a more sinusoidal current wave is achieved by solving the problem of variable switching frequency. The simulation of the multilevel inverter topology-based Conventional and Fuzzy DTC is presented. The simulation results prove that torque ripple reduction is obtained while the stator flux ripples also manage to achieve reduction. Furthermore, the switching frequency is fixed and a smoother sinusoidal phase current is obtained.