Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity

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dc.authorid0000-0001-9412-5223
dc.authorid0000-0003-3736-3668
dc.authorid0000-0002-5136-0829
dc.contributor.authorYilmaz, Alper
dc.contributor.authorKucuker, Ahmet
dc.contributor.authorBayrak, Gokay
dc.date.accessioned2026-02-12T21:05:29Z
dc.date.available2026-02-12T21:05:29Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.ijhydene.2022.02.033
dc.identifier.endpage19809
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue45
dc.identifier.scopus2-s2.0-85125436060
dc.identifier.scopusqualityQ1
dc.identifier.startpage19797
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2022.02.033
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6973
dc.identifier.volume47
dc.identifier.wosWOS:000810181600002
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.subjectHydrogen energy
dc.subjectPower quality
dc.subjectMachine learning
dc.subjectSOFC
dc.subjectDistributed generation
dc.titleAutomated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity
dc.typeArticle

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