Smart Grid Management Enhanced by Large Language Models: From Fault Detection to Real‐Time Optimization

The rapid advancement in digitalization has led to significant innovations in smart grid systems, enhancing energy management, fault detection, and operational optimization. The integration of artificial intelligence (AI), particularly large language models (LLMs), plays a pivotal role in transforming smart grid functionalities. This study explores the potential of LLMs in optimizing real‐time operations and improving fault detection and predictive maintenance in smart grids. LLMs, known for their ability to process vast amounts of unstructured textual data, can offer superior contextual understanding and reasoning, making them suitable for managing the complex, dynamic data produced by smart grids. This paper evaluates how LLMs can address the challenges posed by data heterogeneity, quality, and interpretability in fault detection systems. Furthermore, the research identifies the key challenges in integrating LLMs into smart grid systems, including scalability issues, security vulnerabilities, and model transparency. Through a detailed analysis of existing solutions, this paper outlines the current advancements in LLM‐based models for smart grid optimization and fault detection, emphasizing their potential to enhance grid resilience, operational efficiency, and cybersecurity. Finally, this work highlights the need for further research to overcome the challenges and unlock the full potential of LLMs in smart grid management, providing a roadmap for future developments in AI‐powered energy systems.