Abstract
Particle swarm optimization (PSO) technique has been integrated with sensorless field oriented control induction motor (FOC IM) drive system to identify its optimal parameters. The proposed PSO algorithm is run in parallel with sensorless FOC IM drive taking the stator current as input to estimate both electrical and mechanical parameters. Such parameters are stator resistance, rotor resistance, rotor inductance, magnetizing inductance and motor inertia. The electrical parameters are fed to a back emf model reference adaptive system (BMF MRAS) to estimate its rotor shaft angular speed. On the other hand, the effect of changing estimated parameters from their actual ones on the FOC IM performance has been carried out. Moreover, the estimated motor inertia is used to estimate load torque to avoid any undesirable disturbance. The estimated parameters are obtained from the minimization of a certain fitness function which is represented by the summation of errors between real and estimated quantities. Finally, a comparison between real and estimated motor parameters is conducted in order to demonstrate the accuracy of the proposed technique.