An improved automated PQD classification method for distributed generators with hybrid SVM-based approach using un-decimated wavelet transform

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dc.authorid0000-0003-3736-3668en_US
dc.authorid0000-0002-5136-0829en_US
dc.authorid0000-0003-2234-3453en_US
dc.contributor.authorYılmaz, Alper
dc.contributor.authorKucuker, Ahmet
dc.contributor.authorBayrak, Gökay
dc.contributor.authorErtekin, Davut
dc.contributor.authorShafie-Khah, Miadreza
dc.contributor.authorGuerrero, Josep M.
dc.date.accessioned2022-08-05T13:28:48Z
dc.date.available2022-08-05T13:28:48Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractArtificial intelligence (AI) approaches are usually coupled with the wavelet transform (WT) for feature extraction to classify the power quality disturbances (PQDs). Therefore, selecting a useful WT-based signal processing approach is required for a reliable classification, especially in real-time applications. In this study, a new hybrid, un-decimated wavelet-transform (UWT)-based feature extraction method using a support vector machine (SVM) with a "' a trous" algorithm is proposed to classify PQDs in distributed generators (DGs). The proposed method was performed in a real-time application of a DG system to classify PQDs. The derived features were tested on five different machine learning (ML) models by determining the most appropriate classification technique for the proposed UWT-based feature extraction method. An experimental DG system is constituted in the laboratory using a LabVIEW environment, and the proposed method is tested under different grid conditions. Besides, other well-known and studied conventional ML methods were also tested under 25 dB, 30 dB, and 40 dB noise and compared to the developed method. The experimental and simulation results show that the features extracted with the proposed UWT-based method provide much more successful results in classification than the existing wavelet methods in the literature. Furthermore, the proposed method's noise sensitivity performance is much better than other conventional wavelet algorithms, especially in real-time applications.en_US
dc.identifier.doi10.1016/j.ijepes.2021.107763en_US
dc.identifier.issn0142-0615
dc.identifier.issn0142-0615
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/2032
dc.identifier.volume136en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorYılmaz, Alper
dc.institutionauthorBayrak, Gökay
dc.institutionauthorErtekin, Davut
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPower quality disturbancesen_US
dc.subjectUn-decimated wavelet transformen_US
dc.subjectMachine learningen_US
dc.subjectDistributed generationen_US
dc.titleAn improved automated PQD classification method for distributed generators with hybrid SVM-based approach using un-decimated wavelet transformen_US
dc.typeArticleen_US

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