On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices

Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However...

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Main Authors: Li, Beibei, Xiao, Gaoxi, Lu, Rongxing, Deng, Ruilong, Bao, Haiyong
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/155303
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1553032022-03-17T07:34:44Z On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices Li, Beibei Xiao, Gaoxi Lu, Rongxing Deng, Ruilong Bao, Haiyong School of Electrical and Electronic Engineering School of Computer Science and Engineering Engineering::Electrical and electronic engineering Feasibility and Limitations Smart Grids Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However, the feasibility and limitations of such an approach have not been systematically studied in the existing literature. In this paper, we explore the feasibility and limitations of adopting the PFDD approach to thwart FDI attacks on power grid state estimation. Specifically, we thoroughly study the feasibility of using PFDD to detect FDI attacks by considering single-bus, uncoordinated multiple-bus, and coordinated multiple-bus FDI attacks, respectively. We prove that PFDD can detect all these three types of FDI attacks targeted on buses or super-buses with degrees larger than 1, if and only if the deployment of D-FACTS devices covers branches at least containing a spanning tree of the grid graph. The minimum efforts required for activating D-FACTS devices to detect each type of FDI attacks are, respectively, evaluated. In addition, we also discuss the limitations of this approach; it is strictly proved that PFDD is not able to detect FDI attacks targeted on buses or super-buses with degrees equalling 1. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB08006004 and Grant 2016YFB08006005; in part by the National Natural Science Foundation of China under Grant 61872255, Grant U1736212, and Grant 61572334; in part by the Fundamental Research Funds for the Central Universities, Sichuan University under Grant YJ201933; in part by the Ministry of Education, Singapore under Contract MOE2016-T2-1-119; in part by the Future Resilient System Project at the Singapore-ETH Centre funded by the National Research Foundation of Singapore under its Campus for Research Excellence and Technological Enterprise Program; in part by the Nanyang Technological University (NTU) Internal Funding - SUG - CoE (M4082287) and A*STAR-NTU-SUTD AI Partnership under Grant RGANS1906; in part by the Natural Science Foundation of Zhejiang Province under Grant LY17F020006; and in part by the Key Research and Development Program of Science and Technology Department of Zhejiang Province under Grant 2017C01015. Paper no. TII-19-0385. 2022-03-17T07:34:44Z 2022-03-17T07:34:44Z 2019 Journal Article Li, B., Xiao, G., Lu, R., Deng, R. & Bao, H. (2019). On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices. IEEE Transactions On Industrial Informatics, 16(2), 854-864. https://dx.doi.org/10.1109/TII.2019.2922215 1551-3203 https://hdl.handle.net/10356/155303 10.1109/TII.2019.2922215 2-s2.0-85078707205 2 16 854 864 en MOE2016-T2-1-119 M4082287 RGANS1906 IEEE Transactions on Industrial Informatics © 2019 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Feasibility and Limitations
Smart Grids
spellingShingle Engineering::Electrical and electronic engineering
Feasibility and Limitations
Smart Grids
Li, Beibei
Xiao, Gaoxi
Lu, Rongxing
Deng, Ruilong
Bao, Haiyong
On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
description Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However, the feasibility and limitations of such an approach have not been systematically studied in the existing literature. In this paper, we explore the feasibility and limitations of adopting the PFDD approach to thwart FDI attacks on power grid state estimation. Specifically, we thoroughly study the feasibility of using PFDD to detect FDI attacks by considering single-bus, uncoordinated multiple-bus, and coordinated multiple-bus FDI attacks, respectively. We prove that PFDD can detect all these three types of FDI attacks targeted on buses or super-buses with degrees larger than 1, if and only if the deployment of D-FACTS devices covers branches at least containing a spanning tree of the grid graph. The minimum efforts required for activating D-FACTS devices to detect each type of FDI attacks are, respectively, evaluated. In addition, we also discuss the limitations of this approach; it is strictly proved that PFDD is not able to detect FDI attacks targeted on buses or super-buses with degrees equalling 1.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Beibei
Xiao, Gaoxi
Lu, Rongxing
Deng, Ruilong
Bao, Haiyong
format Article
author Li, Beibei
Xiao, Gaoxi
Lu, Rongxing
Deng, Ruilong
Bao, Haiyong
author_sort Li, Beibei
title On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
title_short On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
title_full On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
title_fullStr On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
title_full_unstemmed On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices
title_sort on feasibility and limitations of detecting false data injection attacks on power grid state estimation using d-facts devices
publishDate 2022
url https://hdl.handle.net/10356/155303
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