Review
Version 1
Preserved in Portico This version is not peer-reviewed
Anomaly Detection in Power System State Estimation: Review and New Directions
Version 1
: Received: 25 August 2023 / Approved: 29 August 2023 / Online: 30 August 2023 (07:23:42 CEST)
A peer-reviewed article of this Preprint also exists.
Cooper, A.; Bretas, A.; Meyn, S. Anomaly Detection in Power System State Estimation: Review and New Directions. Energies 2023, 16, 6678. Cooper, A.; Bretas, A.; Meyn, S. Anomaly Detection in Power System State Estimation: Review and New Directions. Energies 2023, 16, 6678.
Abstract
Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The 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
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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