Dynamic state estimation of a doubly fed induction generator based on a comprehensive nonlinear model

One important challenge in controller design for Doubly-Fed Induction Generator (DFIG) or dynamic analysis of networks with DFIGs, is its nonlinearity besides invisibility of some important state variables. In this paper, a state estimation algorithm based on Extended Kalman Filter (EKF) is proposed for grid connected DFIG. A complete 15th order nonlinear model of DFIG equipped with a nonlinear controller is utilized, and all state equations are derived in appropriate form to be used for EKF. The results of the proposed state estimation algorithm can be used for modeling and analysis of any disturbance such as wind speed variations or faults occurrence in the network. To obtain electrical measures required for state estimation, a Phasor Measurement Unit (PMU) is utilized and all measurement and process noise are modeled. Accuracy of the proposed algorithm is evaluated by five different case studies covering the effect of initial guess for state variables, the effect of process and measurement noises, variation in wind speed and occurrence of a solid short circuit close to the DFIG. The simulation results demonstrate robustness and accuracy of the proposed algorithm in estimating dynamic state variables.