Online estimation of induction motor parameters using a modified particle swarm optimization technique

This paper addresses an application of Particle Swarm Optimization algorithm to dynamically estimate and track the changes in parameters of an induction Motor in steady-state condition. In real-time operation, the performance of the control system is influenced by various environmental and internal factors. The former studies used the data in offline condition of the motor to estimate the parameters. In this novel method however, the measured three-phase currents, voltages and the speed of the induction machine are used as inputs and the effect of the temperature rise on the motor parameters; i.e., rotor and stator resistances is investigated implementing a two-stage single-flock particle swarm optimization technique. The simulation is based on a proper model of the induction motor, including electromagnetic and mechanical elements. The parameters are calculated and the estimation errors are minimized via a normalized root mean square error measure respectively.