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

Basit Öğe Kaydı
dc.authorid0000-0002-1358-8806
dc.authorid0000-0002-6119-0886
dc.authorid0000-0001-8537-5968
dc.contributor.authorShahzad, Sulman
dc.contributor.authorAlsenani, Theyab R.
dc.contributor.authorAlrumayh, Ahmed Nasser
dc.contributor.authorAlmutairi, Abdulaziz
dc.contributor.authorKılıç, Heybet
dc.date.accessioned2025-02-22T14:10:54Z
dc.date.available2025-02-22T14:10:54Z
dc.date.issued2024
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractThis 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 LLCen_US
dc.description.sponsorshipPrince Sattam bin Abdulaziz University, PSAU, (PSAU/2024/R/1446); Prince Sattam bin Abdulaziz University, PSAUen_US
dc.identifier.citationShahzad, 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.
dc.identifier.doi10.1016/j.ijhydene.2024.12.251
dc.identifier.issn0360-3199
dc.identifier.scopus2-s2.0-85212335474en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2024.12.251
dc.identifier.urihttps://hdl.handle.net/11468/29860
dc.indekslendigikaynakScopus
dc.institutionauthorKılıç, Heybet
dc.institutionauthorid0000-0002-6119-0886
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofInternational Journal of Hydrogen Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250222
dc.subjectDeep learningen_US
dc.subjectDistributed generatoren_US
dc.subjectHydrogen energyen_US
dc.subjectReactive poweren_US
dc.subjectRegulationen_US
dc.titleFault ride-through capability improvement in hydrogen energy-based distributed generators using STATCOM and deep-Q learningen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Fault ride-through capability improvement in hydrogen energy-based distributed generators using STATCOM and deep-Q learning.pdf
Boyut:
6.28 MB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Diyarbakır Teknik Bilimler Meslek Yüksekokulu Koleksiyonu