AI-Powered Fault Detection and Load Imbalance Mitigation in Smart Grids Using Hybrid Deep Learning Architectures

Abstract

In the era of rapidly evolving smart grid technologies, fault detection and load imbalance management remain paramount to ensuring energy efficiency, reliability, and sustainability. This study presents a comprehensive exploration of hybrid deep learning architectures to identify and mitigate faults and load imbalances in smart grids. Leveraging Graph Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks, and advanced optimization techniques such as the Chameleon Optimization Algorithm, the research demonstrates the strength of AI integration in enhancing power system diagnostics. A multi-domain literature review of 29 peer-reviewed articles authored or co-authored by leading experts, including Veeramachaneni, Bittla, and Yarram, showcases a converging trend of AI in energy systems, cybersecurity, edge computing, and software engineering. The study provides empirical validation through synthetic and real-time datasets to evaluate fault classification accuracy, load prediction performance, and system resilience. Results show marked improvements in precision, detection time, and energy optimization. The paper concludes by emphasizing the transformative role of AI in cyber-physical energy systems and outlines future research directions.