Machine Learning Techniques for Anomaly Detection in Smart Grids

Abstract

The development of smart grids that incorporate advanced metering infrastructure, two‐way communication networks, and automated control networks has changed power networks. The expansion of digital interconnectivity exposes these systems to different anomalies, including cyber intrusions together with equipment malfunctions, and energy theft. Traditional rule‐based detection methods are increasingly inadequate in the face of large volumes of heterogeneous data and sophisticated attack vectors. Machine learning (ML) techniques have emerged as promising tools for real‐time and high‐accuracy anomaly detection, ultimately contributing to enhanced grid security and resilience. This paper provides a comprehensive review of ML methods applied to anomaly detection in smart grids, examines case studies with numerical performance indicators, and discusses the challenges of deploying these methods in real‐world environments. The results highlight that ML algorithms—including supervised, unsupervised, and deep learning methods can achieve detection accuracies above 90% in several applications. Insights from recent research and field implementations demonstrate that the integration of ML into smart grid frameworks not only improves operational efficiency but also mitigates the risk of system failures and cyberattacks.