Accurate estimation of the dynamic states of a synchronous machine (e.g.,rotor s angle and speed) is essential in monitoring and controlling transientstability of a power system. It is well known that the covariance matrixes ofprocess noise (Q) and measurement noise (R) have a significant impact on theKalman filter s performance in estimating dynamic states. The conventionalad-hoc approaches for estimating the covariance matrixes are not adequate inachieving the best filtering performance. To address this problem, this paperproposes an adaptive filtering approach to adaptively estimate Q and R based oninnovation and residual to improve the dynamic state estimation accuracy of theextended Kalman filter (EKF). It is shown through the simulation on thetwo-area model that the proposed estimation method is more robust against theinitial errors in Q and R than the conventional method in estimating thedynamic states of a synchronous machine.
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