Shahzad, SulmanAlsenani, Theyab R.Alrumayh, Ahmed NasserAlmutairi, AbdulazizKılıç, Heybet2025-02-222025-02-222024Shahzad, S., Alsenani, T. R., Alrumayh, A. N., Almutairi, A. ve Kılıç, H. (2024). Fault ride-through capability improvement in hydrogen energy-based distributed generators using STATCOM and deep-Q learning. International Journal of Hydrogen Energy, 1-13.0360-3199https://doi.org/10.1016/j.ijhydene.2024.12.251https://hdl.handle.net/11468/29860This 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. © 2024 Hydrogen Energy Publications LLCeninfo:eu-repo/semantics/closedAccessDeep learningDistributed generatorHydrogen energyReactive powerRegulationArticle2-s2.0-8521233547410.1016/j.ijhydene.2024.12.251Q1