A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station

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dc.authorid0000-0002-5136-0829
dc.authorid0000-0003-3736-3668
dc.contributor.authorBayrak, Gokay
dc.contributor.authorYilmaz, Alper
dc.contributor.authorCakmak, Recep
dc.date.accessioned2026-02-12T21:04:50Z
dc.date.available2026-02-12T21:04:50Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary condi-tions in threshold value methods are avoided and at the same time, cost and computa-tional burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20-40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.description.sponsorship1002-fast support program of TUBITAK, Ankara, Turkey [121E023]
dc.description.sponsorshipThis work was supported in part by the 1002-fast support program of TUBITAK, Ankara, Turkey, under Grant numbers: 121E023.
dc.identifier.doi10.1016/j.ijhydene.2022.08.067
dc.identifier.endpage6870
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85137729009
dc.identifier.scopusqualityQ1
dc.identifier.startpage6855
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2022.08.067
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6665
dc.identifier.volume48
dc.identifier.wosWOS:000973791300001
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.subjectPower quality
dc.subjectDistributed generation
dc.subjectFuzzy logic decision making
dc.subjectAutomated fault detection
dc.subjectEV charge Stations
dc.titleA new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station
dc.typeArticle

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