Anomaly Detection in Smart Grids Using Artificial Intelligence A Survey of Techniques and Tools

Abstract

The rapid advancement of smart grids has revolutionized modern energy distribution by integrating renewable energy resources, distributed generation, and advanced communication technologies. However, the growing complexity, interconnectivity, and cyber-physical integration have made smart grids increasingly vulnerable to various anomalies, including equipment failures, cyber-attacks, load forecasting errors, and sensor malfunctions. This paper presents a comprehensive survey of state-of-the-art techniques for anomaly detection in smart grids, with a focus on artificial intelligence (AI), hybrid frameworks, and semantic modeling approaches. A wide spectrum of AI-driven methodologies—such as cross-modal collaborative learning, hierarchical semantic representations, physics-informed hybrid algorithms, and deep learning architectures—are systematically reviewed to highlight their roles in improving cybersecurity, fault diagnosis, energy theft prevention, and real-time monitoring. The study also examines the inherent challenges posed by heterogeneous grid infrastructures, diverse data sources, and evolving threat landscapes. Furthermore, the survey identifies emerging directions for designing scalable, explainable, and adaptive anomaly detection frameworks integrated with real-time analytics, privacy-preserving mechanisms, and standardized benchmarking datasets. By synthesizing current advancements and open challenges, this work provides a structured foundation to guide future innovations in secure, resilient, and intelligent smart grid ecosystems.