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Power Grid AC-Based State Estimation: Vulnerability Analysis Against Cyber Attacks
Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA..
Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-9940-5929
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 5, p. 1784-1799Article in journal (Refereed) Published
Abstract [en]

To ensure grid efficiency and reliability, power system operators continuously monitor the operational characteristics of the grid through a critical process called state estimation (SE), which performs the task by filtering and fusing various measurements collected from grid sensors. This study analyzes the vulnerability of the key operation module, namely ac-based SE, against potential cyber attacks on data integrity, also known as false data injection attack (FDIA). A general form of FDIA can be formulated as an optimization problem, whose objective is to find a stealthy and sparse data injection vector on the sensor measurements with the aim of making the state estimate spurious and misleading. Due to the nonlinear ac measurement model and the cardinality constraint, the problem includes both continuous and discrete nonlinearities. To solve the FDIA problem efficiently, we propose a novel convexification framework based on semidefinite programming (SDP). By analyzing a globally optimal SDP solution, we delineate the "attackable region" for any given set of measurement types and grid topology, where the spurious state can be falsified by FDIA. Furthermore, we prove that the attack is stealthy and sparse, and derive performance bounds. Simulation results on various IEEE test cases indicate the efficacy of the proposed convexification approach. From the grid protection point of view, the results of this study can be used to design a security metric for the current practice against cyber attacks, redesign the bad data detection scheme, and inform proposals of grid hardening. From a theoretical point of view, the proposed framework can be used for other nonconvex problems in power systems and beyond.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 64, no 5, p. 1784-1799
Keywords [en]
Convexification, cyber attack, false data injection attack (FDIA), nonconvex optimization, power system, resilience, security, semidefinite programming (SDP), state estimation (SE)
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-254113DOI: 10.1109/TAC.2018.2852774ISI: 000466226500002Scopus ID: 2-s2.0-85049487678OAI: oai:DiVA.org:kth-254113DiVA, id: diva2:1329001
Note

QC 20190624

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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Johansson, Karl Henrik

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