Practical intelligent modeling of three-phase induction machine

This manuscript studies parameter identification of an induction motor using various optimization algorithms. Inertia, friction and load torque of a real three phase motor are optimized based on artificial bee colony (ABC), particle swarm and genetic optimization algorithms. A DC and no-load locked-rotor experiments are performed to spot other parameters. A MATLAB included model and a state space dynamic model of induction motor are simulated to investigate the performance of the identification. This method guarantees the high accuracy and estimation speed of parameter identification. Result of artificial bee colony algorithm shows the least difference between the speeds of real and state space motors over the first half of minute when the motor starts. This indeed reflects the robustness of the proposed algorithm with respect to the other implemented optimization algorithms.