PERFORMANCE ANALYSIS OF INDIRECT VECTOR CONTROL OF INDUCTION MOTOR USING INTELLIGENT CONTROLLERS

Abstract

This dissertation deals with performance analysis of Vector control of IM using intelligent control schemes. Drives today have become an indispensable part of the industrial applications requiring high performance. Electric motors have their impact on almost every sphere of modern life. Electric motors convert electrical energy into useful mechanical energy. The induction motor drives are superior to the dc drives in many aspects such as performance, power to weight ratio, maximum speed capability, efficiency, low initial cost and robustness. The DC motor like performance of drive is achievable using Vector control of induction motor. The induction machine has a very non-linear multivariable structure which makes it very difficult to control. The temperature dependence on motor parameters affects the response of the drive. The 3 phase squirrel cage induction motor is generally used for the variable speed application because they are rugged, reliable, and economical and require less maintenance. These are least expensive motors. Induction motor is a complex higher-order, nonlinear, strongly coupled, and multi-variable system. When operated directly from the line voltages, an induction motor operates at a almost constant speed. In the present work, the indirect vector control of Induction motor has been studied, simulated and hardware prototype of the same is developed. The three phase induction motor is fed from a Voltage source IGBT inverter which is controlled using a Hysteresis PWM controller. The load currents 𝑖𝑎, 𝑖𝑏 and 𝑖𝑐 are compared with the reference currents 𝑖𝑎∗ , 𝑖𝑏∗ and 𝑖𝑐∗ and error signals are passed through hysteresis band to generate the firing pulses, which are operated to produce output voltage in manner to reduce the current error. Various intelligent control schemes like fuzzy logic control and ANFIS control have been developed for speed control of IVCIM and performance of these intelligent control schemes are compared with the conventional PI controller. The comparison between the three techniques shows that the FLC has a robust performance. The transient response is smoother in case of FLC. During sudden load changes, the response obtained from the ANFIS is better. The ANFIS eliminates the transients during sudden change in load as compared to that of PI and fuzzy logic controller. On the other hand, the PI controller shows significant variations to change in load conditions. The response of FLC is faster than PI and ANFIS controller.