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Title: | Decentralized dynamic state estimation for multi-machine power systems with non-Gaussian noises: Outlier detection and localization |
Authors: | Qu, B Wang, Z Shen, B Dong, H |
Keywords: | multi-machine power systems;decentralized state estimation;non-Gaussian noise;particle filter;outlier detection and localization |
Issue Date: | 2-May-2023 |
Publisher: | Elsevier |
Citation: | Qu, B. et al. (2023) 'Decentralized dynamic state estimation for multi-machine power systems with non-Gaussian noises: Outlier detection and localization', Automatica, 153, 111010, pp. 1 - 10. doi: /10.1016/j.automatica.2023.111010. |
Abstract: | 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/26734 |
DOI: | https://doi.org/10.1016/j.automatica.2023.111010 |
ISSN: | 0005-1098 |
Other Identifiers: | ORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401 111010 |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
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FullText.pdf | Embargoed until 2 May 2024 | 2.17 MB | Adobe PDF | View/Open |
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