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. Index Terms-Kalman filter, dynamic state estimation (DSE), innovation/residual-based adaptive estimation, process noise scaling, measurement noise matching.
Other works use adaptive Kalman filters to estimate the covariance matrices REF .
Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation
2017 IEEE Power & Energy Society General Meeting
2017 IEEE Power & Energy Society General Meeting
Mathematics Engineering Computer Science