Effective Identification of FOC Induction Motor Parameters Based on Few Measurements

This paper applies genetic algorithms (GAs) to the problem of parameter identification for field orientation control (FOC) induction motors. Kron´s two-axis dynamic model in per-unit system is given, and the model´s parameters are estimated by a GA using the motor´s dynamic response to a direct on-line start. Results with different levels of measurement noise are presented for the model both in the per-unit system and in actual values. For comparison, the results of a simple random search (SRS) method under the same condition are also given. The parameter identification accuracy, convergence speed, and practicality of the algorithm have been improved significantly by use of the model in the per-unit system. Fewer measurements are required to identify the induction motor parameters accurately.