DEEP LEARNING-BASED BINARY CLASSIFICATION OF ISLANDING CONDITIONS IN A HYDROGEN ENERGY-BASED DISTRIBUTED GENERATION SYSTEM

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dc.contributor.authorYılmaz, Alper
dc.contributor.authorBayrak, Gökay
dc.date.accessioned2026-02-12T21:02:48Z
dc.date.available2026-02-12T21:02:48Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description23rd World Hydrogen Energy Conference: Bridging Continents by H2, WHEC 2022 -- 2022-06-26 through 2022-06-30 -- Istanbul -- 186176
dc.description.abstractThis paper presents a deep long short-term memory (DLSTM) with a binary-label classifier method proposed for binary classification of islanding and non-islanding events in a hydrogen energy-based distributed generator (DG) system. Deep learning (DL)-based method eliminated the lack of performance of conventional intelligent islanding detection methods that uses feature extraction, feature selection, and event classification. Besides, the proposed method has provided the need for a processing-intensive filtering process to reduce noise from the signal. The proposed islanding detection method has a 98.33% accuracy rate under no-noise, and 97.66% high-level noise conditions. In the proposed method, the non-detection zone (NDZ) is almost zero, and the detection time is under the defined IEEE 929-2000 standards. Experimental and simulative data results show that the LSTM-based islanding detection method outperforms the algorithms in recent studies in terms of noise immunity and accuracy. © 2022 Proceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference: Bridging Continents by H2. All rights reserved.
dc.description.sponsorshipBAU; et al.; INOGEN; Republic of Turkey, Ministry of Energy and Natural Resources; TENMARK; Turkish Airlines
dc.identifier.endpage1065
dc.identifier.isbn9786250008430
dc.identifier.scopus2-s2.0-85147193641
dc.identifier.scopusqualityN/A
dc.identifier.startpage1063
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6539
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInternational Association for Hydrogen Energy, IAHE
dc.relation.ispartofProceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference: Bridging Continents by H2
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.snmzKA_Scopus_20260212
dc.subjectDeep learning
dc.subjectHydrogen energy
dc.subjectIslanding detection
dc.subjectLong-short-term memory
dc.subjectMicrogrid
dc.titleDEEP LEARNING-BASED BINARY CLASSIFICATION OF ISLANDING CONDITIONS IN A HYDROGEN ENERGY-BASED DISTRIBUTED GENERATION SYSTEM
dc.typeConference Object

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