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Cyber–physical event reasoning for distributed energy resources

Müller, Nils ; Bao, Kaibin ORCID iD icon 1; Heussen, Kai
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

The widespread adoption of internet-connected and remotely controllable solar plants and energy storages renders coordinated cyber-physical attacks against \glspl{DER} an emerging risk for power systems.
Effective incident response can be facilitated by online \gls{DER} monitoring providing real-time information on event root causes and physical impacts.
Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies.
The \textbf{Cy}ber-\textbf{Ph}ysical \textbf{E}vent \textbf{R}easoning \textbf{S}ystem CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need to be trained on historical attack samples.
To date, CyPhERS has only been demonstrated on a laboratory water distribution testbed of limited complexity, considering human evaluation of event signatures.
This work methodologically adapts CyPhERS to specificities of \gls{DER} operation such as weather and consumer-induced volatility, and introduces an automated signature evaluation system.
The feasibility of applying CyPhERS for automated \gls{DER} monitoring is investigated on a dataset recorded from a real photovoltaic-battery system targeted by several cyber and cyber-physical attack types. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000171363/pub
Veröffentlicht am 05.06.2024
Preprint §
DOI: 10.5445/IR/1000171363
Veröffentlicht am 05.06.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.09.2024
Sprache Englisch
Identifikator ISSN: 2352-4677
KITopen-ID: 1000171363
HGF-Programm 37.12.01 (POF IV, LK 01) Digitalization & System Technology for Flexibility Solutions
Erschienen in Sustainable Energy, Grids and Networks
Verlag Elsevier
Band 39
Seiten Art.-Nr.: 101400
Vorab online veröffentlicht am 29.04.2024
Schlagwörter Distributed energy resources, PV-battery systems, Attack detection, Cyber–physical monitoring, Machine learning
Nachgewiesen in Dimensions
Web of Science
Scopus
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