A new intelligent power quality disturbance classification in renewable and decentralized hydrogen-based energy systems using SwResNET hybrid model

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dc.authorid0000-0002-5136-0829
dc.authorid0000-0001-9613-6620
dc.authorid0000-0001-9412-5223
dc.authorid0000-0002-7947-2312
dc.contributor.authorKucuker, Ahmet
dc.contributor.authorBarakli, Burhan
dc.contributor.authorBayrak, Gokay
dc.contributor.authorBasaran, Kivanc
dc.contributor.authorBalaban, Georgiana
dc.date.accessioned2026-02-08T15:15:25Z
dc.date.available2026-02-08T15:15:25Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, a scalogram image-based Swin-Residual Network (SwResNET) hybrid method is proposed for the identification of power quality disturbances (PQDs) in a hydrogen energy-based distributed generator (HEBDGs). The proposed approach involves the creation of PQD scalogram images by applying spectrogram analysis to power signal data. This process generates a two-dimensional image that represents the frequency and time characteristics of the signal. These spectrogram images are then input into a SwResNET hybrid model for learning. The SwResNET hybrid model extracts features from the scalogram images and classifies the input signal based on the presence or absence of power quality disturbances. This paper used 21 different PQD events in HEBDGs for classification purposes. Furthermore, the proposed method was tested under noisy conditions. The data achieved from simulated results of the HEBDG system in Matlab/Simulink and empirical data collected in the laboratory collectively demonstrate that the proposed methodology exhibits exceptional performance in terms of 98.22 % accuracy and resistance to noise, surpassing existing state-of-the-art approaches.
dc.description.sponsorshipNational Center for High Performance Computing of Turkey (UHeM) [1011402021]; Ministry of Research, Innovation and Digitalization of Romonia Romania [PNRR-C9-I8-760089/23.05.2023, CF31/14.11.2022]
dc.description.sponsorshipComputing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant number: 1011402021. Also, thanks to Bursa Technical University Smart Grid Lab. for laboratory supports.This work was supported by the grants of the Ministry of Research, Innovation and Digitalization of RomoniaRomania, project number PNRR-C9-I8-760089/23.05.2023, code CF31/14.11.2022.
dc.identifier.doi10.1016/j.renene.2025.123251
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-105004184642
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.renene.2025.123251
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5774
dc.identifier.volume250
dc.identifier.wosWOS:001489108500002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofRenewable Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectHydrogen energy-based distributed generation
dc.subjectPower quality disturbances
dc.subjectSwin transformer learning
dc.subjectResidual networks
dc.subjectVision transformers
dc.titleA new intelligent power quality disturbance classification in renewable and decentralized hydrogen-based energy systems using SwResNET hybrid model
dc.typeArticle

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