Deep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm

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dc.authorid0000-0001-9412-5223
dc.authorid0000-0003-3736-3668
dc.authorid0000-0002-5136-0829
dc.contributor.authorBayrak, Gokay
dc.contributor.authorKucuker, Ahmet
dc.contributor.authorYilmaz, Alper
dc.date.accessioned2026-02-12T21:05:29Z
dc.date.available2026-02-12T21:05:29Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, new multiple deep classifiers with a modified Weighted Majority Voting (WMV)-based method are proposed to identify power quality disturbances (PQDs) in a hydrogen energy-based microgrid. In the proposed approach, closed-loop deep LSTM (Long Short Time Memory), deep CNN (Convolutional Neural Network), and hybrid (CNN-LSTM) models are used for automatic feature extraction and classification. Then, a modified WMV method is employed to ensemble the outputs of the three deep learning (DL) classifier models. The enhanced WMV mechanism performs an automatically updated weighting based on the validation results of the DL classification models, unlike voting methods in the literature. The improved WMV mechanism eliminates the challenges of using multiple DL classifiers in the voting system. The mathematical data results in LabVIEW, simulation results in Matlab/Simulink, and real data results in the laboratory show that the proposed method shows superior performance in accuracy and noise immunity to state-of-the-art methods.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.ijhydene.2022.05.137
dc.identifier.endpage6836
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85132212834
dc.identifier.scopusqualityQ1
dc.identifier.startpage6824
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2022.05.137
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6972
dc.identifier.volume48
dc.identifier.wosWOS:000973807700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectDeep learning
dc.subjectMicrogrid
dc.subjectHydrogen energy-based distributed
dc.subjectgeneration
dc.subjectPower quality disturbances
dc.subjectWeighted majority voting
dc.titleDeep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm
dc.typeArticle

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