Local sequential ensemble Kalman filter for simultaneously tracking states and parameters

Accurate information about dynamic states and parameters is important for efficient control and operation of a power system. To improve the estimation accuracy of states and parameters, this paper applies a local sequential ensemble Kalman filter (EnKF) method to simultaneously estimate dynamic states and parameters using phasor-measurement-unit (PMU) data. Based on simulation studies using multi-machine systems, the proposed method performed favorably in tracking both states and parameters in real time.