Fault ride-through capability improvement in hydrogen energy-based distributed generators using STATCOM and deep-Q learning

This study explores the enhancement of Fault Ride-Through (FRT) capabilities in hydrogen energy-based distributed generators (HEDGs) by integrating Static Synchronous Compensators (STATCOM) with a novel Deep Q-Learning (DQL) control technique. Hydrogen energy systems face challenges like voltage instability during grid disturbances, which conventional Proportional-Integral (PI) controllers fail to address due to their linear operation constraints. Advanced controllers, such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), offer better adaptability but lack real-time optimization capabilities. The proposed DQL framework leverages reinforcement learning, achieving superior results by dynamically optimizing reactive power compensation and minimizing system instability. Simulation results demonstrate that the DQL-based STATCOM achieves a 35% faster settling time and reduces overshoot by 50% compared to ANFIS and PI controllers. Additionally, the DQL system maintains voltage stability within ±5% during critical faults, improving energy efficiency by 8%. This innovative approach ensures cost-effective, sustainable integration of HEDGs into modern power grids, significantly advancing intelligent control strategies for renewable energy systems.