Dynamic Modelling & Simulation of Induction Motor Drives

Abstract

Induction motors (IMs) have many applications in the industries, because of the low maintenance and robustness. The speed control of IM is more important to achieve maximum torque and efficiency. The rapid development of power electronic devices and converter technologies in the past few decades, however, has made possible efficient speed control by varying the supply frequency and voltage, giving rise to various forms of adjustable-speed IM drives. In about the same period, there were also advances in control methods and artificial intelligent (AI) techniques, including expert system, fuzzy logic, neural networks, and genetic algorithm. Researchers soon realized that the performance of IM drives can be enhanced by adopting artificial intelligent-based methods. This paper presents dynamic modeling and simulation of IM using AI controller. The integrated environment allows users to compare simulation results between classical and AI controllers. The fuzzy logic controller and artificial neural network controllers (NNCs) are also introduced to the system for keeping the motor speed to be constant when the load varies. The performance of fuzzy logic and artificial neural network (ANN)-based controllers is compared with that of the conventional proportional integral controller. The performance of the IM drive has been analyzed for constant, variable loads, and induction generator mode.

Appendix

The following parameters of the induction motor are chosen for the simulation studies:

Three phase star connected squirrel cage IM kW = 0.5 HP, 0.147 kW, rated stator voltage = 230 V, frequency = 50 Hz, rated current = 2A, N = 4, speed = 1,500 rpm J = 0.001 kg m², f = 0.000124 R _s  = 14.6 Ω/phase, R _r = 12.76 Ω/phase, L _m = 0.2963 H, L _s = 0.0222H, L _r = 0.058.