Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey
Giovanni Battista Gaggero, P. Girdinio, Mario Marchese
Abstract
The integration of advanced communication systems and distributed resources has transformed power systems, enhancing control and efficiency in the Smart Grid. However, this increased complexity introduces new vulnerabilities, heightening risks of cyber-attacks, equipment failures, and other anomalies. Anomaly detection methods increasingly rely on Machine Learning techniques, that represent a game-changer tool for data analysis. The aim of this survey is to review anomaly detection techniques in the Smart Grid, focusing on methods that combine Artificial Intelligence and physics-based modeling. This work systematically examines the current state of research, evaluating the investigated use cases, the algorithms, the performances and the validation of the papers, identifying key gaps, and offering insights for advancing in this research field.