Machine Learning Strategies for Power Grid Resilience: A Functional and Bibliometric Review

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper presents a structured review of ML strategies for power-grid resilience applications using a four-phase resilience lens (Prevention and Improvement, Control and Mitigation, Restoration, and Cyber Resilience). The literature is organized through a functional taxonomy that includes fault diagnosis, event prediction, control and stability support, restoration, and cyber resilience. In addition to the qualitative synthesis, a quantitative analysis of a dataset of 13,647 peer-reviewed publications (2015–2026) is conducted to characterize research activity across resilience functions and implementation contexts. This analysis is used to illustrate the increasing adoption of machine learning approaches and to distinguish between simulation-based and real-world applications. The results indicate a methodological shift toward Deep Learning and Reinforcement Learning for complex tasks, while federated and edge-based approaches are gaining attention for privacy preserving and real-time applications. These findings provide a structured view of current research directions and support the growing relevance of machine learning in resilience-oriented power system applications, offering a foundation for the development of intelligent and scalable cyber-physical energy systems.