  Accurate estimation of the dynamic states of a synchronous machine (e.g.,
rotor s angle and speed) is essential in monitoring and controlling transient
stability of a power system. It is well known that the covariance matrixes of
process noise (Q) and measurement noise (R) have a significant impact on the
Kalman filter s performance in estimating dynamic states. The conventional
ad-hoc approaches for estimating the covariance matrixes are not adequate in
achieving the best filtering performance. To address this problem, this paper
proposes an adaptive filtering approach to adaptively estimate Q and R based on
innovation and residual to improve the dynamic state estimation accuracy of the
extended Kalman filter (EKF). It is shown through the simulation on the
two-area model that the proposed estimation method is more robust against the
initial errors in Q and R than the conventional method in estimating the
dynamic states of a synchronous machine.
