Decentralized dynamic state estimation for multi-machine power systems with non-Gaussian noises: Outlier detection and localization

In this paper, the decentralized dynamic state estimation (DSE) problem is investigated for a class of multi-machine power systems with non-Gaussian noises and measurement outliers. A model decoupling approach is adopted to facilitate the decentralized DSE for large-scale power systems. The particle filtering technique plays a key role in the developed DSE scheme with aim to tackle the nonlinearities and the non-Gaussian noises. To mitigate the negative impact from the measurement outliers on the DSE performance, a novel sliding-window-based online algorithm is proposed to detect and further locate the possible outliers based on the historical measurement data. Specifically, some criteria are constructed to (i) determine whether a newly arriving measurement vector is contaminated by measurement outliers and (ii) locate the abnormal components of such a vector. A conditional posterior Cramér–Rao lower bound is derived to evaluate the estimation performance of the proposed DSE algorithm. Finally, simulation experiments are carried out on the IEEE-39 bus system to verify the effectiveness of the proposed DSE algorithm under the non-Gaussian noises and the measurement outliers. This work was supported in part by the National Natural Science Foundation of China under Grants 61933007, U21A2019 and 62273088, the Program of Shanghai Academic/Technology Research Leader of China under Grant 20XD1420100, the Hainan Province Science and Technology Special Fund of China under Grant ZDYF2022SHFZ105, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany .