Parameter estimation of doubly fed induction generator driven by wind turbine

In order to reduce the environmental consequences of electric power generation, there has been a growing interest in the use of renewable resources for generating electricity. One way of generating electricity from renewable sources is to use wind turbines that convert the kinetic energy contained in the flowing air into electrical energy. As wind power is integrated in large scale European and North American power systems, investigating the dynamic behavior of these turbines is of great importance. Unfortunately, the parameters of the wind turbine needed to conduct dynamic analysis are frequently unknown or inaccurate. This paper analyzes the behavior of two Kalman filter based estimation techniques, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), for parameter estimation of the doubly fed induction generator (DFIG) driven by wind turbine. The performance of these two methods is evaluated from different aspects: estimation accuracy, computation time, and robustness to variation of the initial parameter estimates and filter gains. Our experiments show that the performance of the UKF is superior to that of the EKF.