Dynamic state estimation and parameter calibration of a DFIG using the ensemble Kalman filter

With the growing interest in the application of wind energy, doubly fed induction generators (DFIG) play an increasingly essential role in the power industry. It has been well recognized that modeling and monitoring the dynamic behavior of DFIGs are important to ensure power system reliability. Real-time estimation of the dynamic states of a DFIG is possible with high-speed measurements. But how to use such measurements to have high-quality estimation remains to be a challenge. Estimating dynamic states relies on a good dynamic model of the DFIG. Building a high-fidelity model is a problem in tandem with the dynamic state estimation problem. In this paper, we propose an ensemble Kalman filter (EnKF)-based method for the state estimation and parameter calibration of a DFIG. The mathematical formulation of state estimation combining with parameter estimation is presented. Simulation cases were studied to demonstrate the accuracy of both dynamic state estimation and parameter estimation. Sensitivity analysis is performed with respect to the measurement noise, initial state errors and parameter errors. The results indicate this EnKF-based method has a robust performance on the state estimation and parameter calibration of a DFIG.