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  Distributed Event-Triggered Learning-Based Control for Battery Energy Storage Systems Under Persistent False Data Injection Attacks

Wan, Y., Wen, G., Yu, X., Kurths, J., Chen, Z. (2024): Distributed Event-Triggered Learning-Based Control for Battery Energy Storage Systems Under Persistent False Data Injection Attacks. - IEEE Transactions on Smart Grid, 15, 5, 4986-4997.
https://doi.org/10.1109/TSG.2024.3370912

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_30775 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_30775_1
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 ???ViewItemFull_lblCreators???:
Wan, Ying1, ???ENUM_CREATORROLE_AUTHOR???
Wen, Guanghui1, ???ENUM_CREATORROLE_AUTHOR???
Yu, Xinghuo1, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen2, ???ENUM_CREATORROLE_AUTHOR???           
Chen, Zhiyi1, ???ENUM_CREATORROLE_AUTHOR???
???ViewItemFull_lblAffiliations???:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: This paper aims to address distributed event-triggered learning-based secure control for multiple battery energy storage systems (BESSs) under persistent false-date injection (FDI) attacks. To tackle FDI attacks and also save communication resources, a distributed learning-based secure control based on a dynamic event-triggered framework is established. This control scheme uses an adaptive law to update the estimation matrix for a neural network (NN) approximator, using only relative state variables at triggered instants. To ensure uniform boundedness of all variables involved in the update law, a proper projection operator is introduced. Additionally, the updated law incorporates a low-pass filter structure, which can suppress unfavorable high-frequency oscillations when a high-gain learning rate is applied. It is rigorously proven that under such distributed event-triggered learning-based control protocols, frequency regulation, active power sharing, and SoC balancing can be achieved with arbitrary accuracy by adjusting the learning rates and control parameters. Finally, real-time simulations of the IEEE 57-bus system are performed using OPAL-RT to illustrate the efficacy of the developed learning strategy.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2024-02-282024-09-01
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1109/TSG.2024.3370912
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Sustainable Development
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Nonlinear Data Analysis
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???ViewItemFull_lblSourceTitle???: IEEE Transactions on Smart Grid
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 15 (5) ???ViewItemFull_lblSourceSequenceNo???: ???lbl_noEntry??? ???ViewItemFull_lblSourceStartEndPage???: 4986 - 4997 ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/1949-3061
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Institute of Electrical and Electronics Engineers (IEEE)