Vector Control of Induction Motor Using Neural Networks Based Lookup Table for Reduced CMV

Abstract

This paper confers a vector control (VC) perspective for induction motor drives by combining the principles of vector and direct torque control (DTC) methods. In classical vector control, the switching states will be chosen dependent on current hysteresis regulators. Based on the instantaneous current waves, the hysteresis controllers generate the switching specimen. But, the classical DTC selects the pertinent voltage vector from lookup table extracted from the flux and torque error signals and sector information. The proposed perspective combines both VC and DTC techniques. In this paper d and q-axis current errors and sector information, the lookup table chooses an appropriate voltage vector. Moreover, to select the befitting voltage vector Neural Network (NN) based approach is presented in this paper. Also, to reduce the common mode voltage (CMV) variations, NN based lookup tables are proposed. To substantiate the proposed NN based vector control, simulation studies have been conveyed and results are collated.