Anomaly Detection in Power System State Estimation: Review and New Directions

Austin Cooper, Arturo Bretas () and Sean Meyn
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Austin Cooper: Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USA
Arturo Bretas: Distributed Systems Group, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Sean Meyn: Electrical and Computer Engineering Department, University of Florida, Gainesville, FL 32603, USA

Energies, 2023, vol. 16, issue 18, 1-15

Abstract: Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

Keywords: anomaly detection; cyber-security; false data injection; hypothesis testing; machine learning; power system monitoring; quickest-change detection; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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