Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation
By Shahrokh Akhlaghi, Ning Zhou, Zhenyu Huang. IEEE Power & Energy Society General Meeting, 2017
350 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/8bc8feccb25a9eb2887e2352739ba37b3123bd20
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Citations in the corpus
- Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees (2022, positive): “Such variances can be estimated online using methods such as those proposed in Odelson et al., 2006; Akhlaghi et al., 2017.”