AI-DRIVEN GRID RESILIENCE: DEEP LEARNING-BASED STRATEGIES FOR PREVENTING AND MITIGATING GRID COLLAPSE IN RENEWABLE ENERGY-DOMINATED NETWORKS

The increasing integration of renewable energy sources into power grids has introduced significant challenges in maintaining grid resilience due to their variability and unpredictability.This study investigates the potential of artificial intelligence [AI] and deep learning to enhance grid stability, focusing on preventing cascading failures and mitigating risks in renewable energy-dominated networks.By leveraging advanced algorithms, the research aims to address key aspects of grid management, including real-time load balancing, demand forecasting, and automated grid restoration.Deep learning-based models are developed to predict energy demands and manage dynamic changes in renewable energy output with precision.Early-warning systems are designed using neural networks to identify signals of grid instability, enabling proactive interventions to prevent cascading failures.Furthermore, deep reinforcement learning techniques are employed to design automated strategies for blackout mitigation, focusing on optimal grid restoration and maintaining supplydemand equilibrium.The role of microgrids and distributed energy resources [DERs] is explored as a complementary solution, enhancing localized resilience and reducing the impact of large-scale grid collapses.This research contributes to the growing field of AI-driven grid management by presenting a comprehensive framework for integrating deep learning models with existing energy infrastructure.The findings underscore the importance of adopting AI technologies to address the complexities of renewable energy systems and ensure grid reliability.By proposing actionable strategies, this study bridges the gap between theoretical advancements and practical implementation, paving the way for a resilient and sustainable energy future.