Spatio-Temporal Synchrophasor Data Characterization for Mitigating False Data Injection in Smart Grids
Paper
Paper/Presentation Title | Spatio-Temporal Synchrophasor Data Characterization for Mitigating False Data Injection in Smart Grids |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), Wang, Weikang (Author), Liu, Yilu (Author), Fuhr, Peter (Author) and Morales-Rodriguez, Marissa (Author) |
Journal or Proceedings Title | Proceedings of the 2020 IEEE Power and Energy Society General Meeting (PESGM) |
ERA Conference ID | 50486 |
Number of Pages | 5 |
Year | 2019 |
Place of Publication | Atlanta, United States |
ISBN | 9781728119816 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/pesgm40551.2019.8973586 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8973586 |
Conference/Event | 2020 IEEE Power and Energy Society General Meeting (PESGM) |
IEEE Power and Energy Society General Meeting | |
Event Details | 2020 IEEE Power and Energy Society General Meeting (PESGM) Event Date 04 to end of 08 Aug 2020 Event Location Atlanta, United States |
Event Details | IEEE Power and Energy Society General Meeting PES-GM |
Abstract | As electric power grids' dependence on wide area monitoring systems (WAMS) is expected to increase significantly in the near future, the cyber security concerns of WAMS must be carefully addressed. False data injection attack (FDIA) is a typical cyber-physical attack of WAMS in modern smart grids. This paper presents a data mining-based approach to identify FDIA on frequency data of WAMS by revealing the spatio-temporal signatures of synchrophasor measurements. Specifically, recurrence quantification analysis (RQA) is utilized to extract temporal signatures of frequency measurements while the spatial signatures are derived by using statistical method. Three FDIA scenarios, i.e., 'Source ID Mix', time mirroring and time dilation attacks are simulated. Experimental results by using synchrophasor measurements archived in FNET /GridEye demonstrate the practicability of the proposed methodology for mitigating FDIA on frequency measurements of power systems. |
Keywords | Cyber-physical attack; smart grid; spatiotemporal signature; synchrophasor; wide area monitoring systems |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
460308. Pattern recognition | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Tennessee, United States |
Oak Ridge National Laboratory, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w17/spatio-temporal-synchrophasor-data-characterization-for-mitigating-false-data-injection-in-smart-grids
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