EFFICIENCY OPTIMIZATION IN INDUCTION MOTOR DRIVES

Abstract

The induction motor is most widely used motor in industrial and domestic applications due to its high reliability and lower cost. The variable speed operation of the induction motor with the help of power electronics is the most common way of improving energy efficiency in electric drives. The energy efficient drives in modern processes results in high quality and cheaper products, lower production cost and energy saving and hence reducing of global pollution. Recent development in the area of digital signal processing and microprocessors has made the implementation of high performance IM drives cost effective and viable solution to adjustable speed applications in industries. The aim of the present thesis work is to deal with the advance control methods for variable speed induction motor drives with improved energy efficiency. This is achieved by optimal control of the flux producing current component using loss model and search control methods of the IM drive. The performance analysis of scalar control and vector controlled of induction motor drive is being studied. Modeling and simulation of volts-hertz (V/f) and indirect field oriented control (IFOC) have been carried out by using MATLAB/Simulink and full spectrum simulator (FSS). Full spectrum simulator provides both offline and real time simulation with hardware in loop (HIL) facility. Results obtained from both MATLAB and FSS offline simulator are compared with the FSS online simulator. Sensitivity analysis of machine performance variables is carried out to predict the motor performance affected by parameter variations through mathematical and computational methods. To design robust drive many control techniques with online parameter estimation have been described. The model reference adaptive system based stator resistance and motor speed estimation methods in a sensorless induction motor drive is analyzed in detail. The MRAS based control is applicable for zero and low speed applications and found stable in all vi

four quadrants of operations of drive. The stator resistance estimation adaptive mechanism utilizes adaptive neuro-fuzzy inference system (ANFIS) and the estimated stator resistance is used for making the rotor speed estimation system independent of stator resistance and makes the system robust against the temperature variation. The sensorless IM drive system with MRAS estimator shows robust feature concerned with parameter variation for stator resistance from its initial value during working condition of IM. A simple and easily realizable loss model technique for implementation of a PI and fuzzy logic controller based efficiency optimization algorithm for a vector controlled induction motor drive is also presented. A new approach to optimize the efficiency based on optimal control of iron losses only is introduced and compared with the optimal control of the total losses under different operating conditions. A novel algorithm of searching the flux current component for efficiency optimization of vector controlled induction motor drives through search control (SC) approach is also investigated and dynamic performance of the drive system is analyzed in detail. This new algorithm called “deep valley search algorithm” and can be considered as an alternative to the search control problems. This approach uses the optimal control of the flux producing current component for efficiency improvement by reducing the core losses and to minimize the measured dc-link power to the inverter at light load conditions.