Least squares and genetic algorithms for parameter identification of induction motors

Abstract

This paper deals with off-line parameter identification of induction motors by means of least square (LS) techniques and genetic algorithms (GA), using stator voltages, stator currents and velocity as input-output data. For analytical identification by LS algorithms, filtering of experimental data is performed by means of anticausal filters. Two models useful for identification are derived in which the products of acceleration and rotor fluxes, usually neglected, are taken into account. The GA-based identification method consists of the determination of the best parameters which match input-output behaviour of the motor. Both methods are investigated and compared by means of experiments carried out on a 1-kW induction motor.