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DOE OSTI · 2000806

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

Cooper, Austin (ORCID:0000000190320750)·Bretas, Arturo (ORCID:0000000340060764)·Meyn, Sean (ORCID:000000028558365X)
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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.

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42 ENGINEERINGanomaly detectioncyber-securityfalse data injectionhypothesis testingmachine learningpower system monitoringquickest-change detectionstate estimation

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Cooper, Austin (ORCID:0000000190320750), Bretas, Arturo (ORCID:0000000340060764), Meyn, Sean (ORCID:000000028558365X). 2023-09-18. Anomaly Detection in Power System State Estimation: Review and New Directions. https://doi.org/10.3390/en16186678

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