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Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation

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arxiv 1702.00884 v1 pith:WPBGXAL5 submitted 2017-02-03 cs.SY cs.SY

Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation

Shahrokh Akhlaghi , Ning Zhou , Zhenyu Huang This is my paper
classification cs.SY cs.SY
keywords dynamicestimationcovarianceestimatingfilterkalmannoisestates
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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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Cited by 2 Pith papers

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  2. Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling

    cs.RO 2026-07 conditional novelty 4.5

    Confidence-gated Q/R covariance scheduling on one continuous EKF modestly but consistently improves BlueROV2 pool dead reckoning over a single global noise profile.