Physics-Assisted Explainable Anomaly Detection in Power Systems

Matthew Lau, Fahad Alsaeed, Kayla Thames, Nano Suresettakul, Saman A. Zonouz, Wenke Lee, Athanasios P. Meliopoulos

Abstract: Detection of cyber-attacks in power systems is crucial for rapid corrective actions like isolation, disinfection and asset restoration. For real-time deployment, detection methods must not only be accurate and computationally efficient, but also interpretable for further action. While physics models can reliably detect cyber-attacks, diagnosing where and how assets were attacked is computationally demanding. To supplement detection models, we propose Physics-Assisted Statistics for Anomaly Localization (PASAL), a domain-informed data-driven method that directly identifies anomalous devices. PASAL leverages domain knowledge of the grid topology and incorporates correlation and variance statistics to model inter-sensor causal relationships. Consequently, PASAL offers inherent interpretability and computational efficiency. Our study demonstrates that PASAL swiftly localizes data integrity attacks with minimal false positives and has the potential to identify the type of attack.