Anomaly Detection in Power System State Estimation: Review and New Directions
Austin Cooper, Arturo S. Bretas, Sean Meyn
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.
Topics & Concepts
Anomaly detectionResidualState (computer science)Computer scienceEstimationAnomaly (physics)Data miningSmart gridElectric power systemPower (physics)Artificial intelligenceEngineeringSystems engineeringAlgorithmElectrical engineeringQuantum mechanicsCondensed matter physicsPhysicsSmart Grid Security and ResiliencePower System Optimization and StabilityAnomaly Detection Techniques and Applications