Parameter Identification for FOC Induction Motors Using Genetic Algorithms with Improved Mathematical Model

Abstract

This paper applies genetic algorithms (GAs) to the problem of parameter identification for field orientation control (FOC) of induction motors. The motor's general model, which is generally used for speed control applications, is improved for the purpose of improving the parameter identification accuracy by the assumption that the stator self inductance Ls is identical to the rotor self inductance Lr. The motor's dynamic response to a direct on-line start is used to estimate the parameters. Results are presented for both the general and improved mathematical models of the induction motor, using different levels of measurement noise. For comparison, the results of a simple random search (SRS) method under the same condition are also given. The results show that the improved model increases significantly the parameter identification accuracy and that the performance of the GA method is much better than that of a simple random search technique. It is concluded that the GA is a powerful tool for parameter identification.