A Real-Time Improved ML Method for PQD Classification of a PV-Powered EV Charging Station

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dc.authorid0000-0002-8093-4078
dc.authorid0000-0002-5136-0829
dc.authorid0009-0002-7473-1108
dc.authorid0000-0003-3736-3668
dc.contributor.authorYilmaz, Alper
dc.contributor.authorAtesci, Tolga
dc.contributor.authorMeral, Hasan
dc.contributor.authorBayrak, Gokay
dc.date.accessioned2026-02-08T15:15:42Z
dc.date.available2026-02-08T15:15:42Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe installation of electric vehicle charging stations (EVCSs) that are powered by renewable energy sources has been growing rapidly. However, this has raised a crucial issue regarding the quality of power supplied to these stations. Due to the intermittent nature of renewable energy sources and the high-power requirements of EV charging, power quality disturbances (PQDs) occur more. This study proposes a new intelligent PQD classification method that considers feature extraction/selection based on pyramidal undecimated wavelet transform (p-UWT) and minimum redundancy maximum relevance (mRMR). The feature vector, derived through the application of mRMR, comprises a mere ten elements. The p-UWT-mRMR combination overcomes the problem of noise sensitivity inWTs. In addition, Bayesian optimization and UWT-mRMR have addressed hyperparameter selection difficulties and overfitting in support vector machine models. The proposed method demonstrated an impressive classification accuracy of 99.55% when faced with 30-dB noise. A prototype test platform is developed with EVCS-integrated PV systems in the laboratory to verify the performance of the proposed method in real-time cases. Dynamic analysis revealed that all PQDs have runtimes ranging from 5 to 10ms in experiments. The proposed method has been validated on a dataset of over 20 000 real-world signals with a test accuracy of 99.11%.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) [TUBITAK1501, 3210715]
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK), with the TUBITAK1501 Industrial R&D Projects Support Program, Ankara, Turkey, under Grant 3210715.
dc.identifier.doi10.1109/TIE.2024.3436549
dc.identifier.endpage2632
dc.identifier.issn0278-0046
dc.identifier.issn1557-9948
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85218338679
dc.identifier.scopusqualityQ1
dc.identifier.startpage2622
dc.identifier.urihttps://doi.org/10.1109/TIE.2024.3436549
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5912
dc.identifier.volume72
dc.identifier.wosWOS:001292769800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Industrial Electronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectFeature extraction
dc.subjectVectors
dc.subjectTransforms
dc.subjectReal-time systems
dc.subjectSupport vector machines
dc.subjectNoise
dc.subjectDiscrete wavelet transforms
dc.subjectElectric vehicle charging station (EVCS)
dc.subjectmachine learning
dc.subjectminimum redundancy maximum relevance (mRMR)
dc.subjectpower quality (PQ)
dc.subjectundecimated wavelet transform (UWT)
dc.titleA Real-Time Improved ML Method for PQD Classification of a PV-Powered EV Charging Station
dc.typeArticle

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