Dynamic State Estimation of DFIG Using Square-root Cubature Kalman Filter

This paper proposes the implementation of dynamic state estimation of Doubly Fed Induction Generator (DFIG) using Cubature Kalman Filter (CKF) and Square-root Cubature Kalman Filter (SRCKF). First, the related research of DFIG dynamic state estimation is reviewed, and then the shortcomings of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) and the advantages of CKF and its extended algorithm are explained. Then, a specific DFIG model is used for simulation analysis. The simulation results compare the performance of UKF, CKF and SRCKF. It shows that SRCKF can avoid the non-positive definiteness of the covariance matrix during CKF iteration, and has better filtering effect.