VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING A MULTILEVEL INVERTER

Abstract

Variable speed operation of Induction Motor drives can be divided into two categories: Scalar control and Vector control. Scalar control is used for low-performance drives that only regulate the magnitude and frequency of stator voltage or current. The constant (V/f) control is the most commonly used scalar control method. It provides moderate dynamics performance and is thus used in applications in which high-speed accuracy is not required, such as pumps, elevators, and fans. These techniques of control result in poor flux and torque response. Vector control or Direct torque control can be used to implement high-performance induction motor drives. Vector control makes the AC drives equivalent to the DC drives in the independent control of flux and torque and superior to them in their dynamic performance. These developments positioned the AC drives for high-performance applications. Vector Control (VC), Field Oriented Control (FOC), and Direct Torque Control (DTC) have become the industry norm for induction motor control to achieve variable speed. The vector control technique separates the two stator current components, so that one can independently control flux and the other can independently control torque, just like a separately excited fully compensated DC motor. There are three different 3-level multilevel inverters (three-level diode clamped inverter, three-level cascaded H-Bridge inverter and three-level flying capacitor inverter) topologies have been implemented here. The gating pulses are generated for a three-phase three-level voltage source inverter using the SVM technique. This thesis aimed to give a vector control or field-oriented control (FOC) of a three-phase induction motor drive with a three-phase three-level multilevel Inverter SVM and give a detailed comparison between using different three types of classic three-level multilevel inverter based on various criteria including power circuit configuration and implementation complexity.