Parameter identification of induction motors using differential evolution

Abstract

Parameter identification of system models is a fundamental step in the process of designing a controller for a system. In control engineering, a wide selection of analytic identification techniques exists for linear systems, but not for nonlinear systems. Instead, the model parameters may be determined by an optimization algorithm by minimizing the error between model output and measured data. We apply the differential evolution algorithm to parameter identification of two induction motors. The motors are used in the house circulation pumps produced by the Danish pump manufacturer Grundfos A/S. The experiments presented use differential evolution, and is a follow-up study of an comparison of eight stochastic search algorithms on the two motor identification problems. In conclusion, the differential evolution algorithm outperformed the previously best known algorithms on both problems.