Journal
2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/PESGM.2017.8273755
Keywords
Kalman filter; dynamic state estimation (DSE); innovation/residual-based adaptive estimation; process noise scaling; measurement noise matching
Funding
- U.S. Department of Energy (DOE)
- DOE [DE-AC05-76RL01830]
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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.
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