An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events

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dc.authorid0000-0002-6624-6148en_US
dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0001-6035-5733en_US
dc.authorid0000-0003-2371-8173en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorSindi, Hatem
dc.contributor.authorNour, Majid
dc.contributor.authorRawa, Muhyaddin
dc.contributor.authorÖztürk, Şaban
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-07-03T08:15:49Z
dc.date.available2023-07-03T08:15:49Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionDeanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabiaen_US
dc.description.abstractDistributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection and classification of these problems increase efficiency by both the producer and the consumer. In the literature, incredibly singular and some composite power quality disturbance (PQD) detection is performed effectively. However, the multitude of composite PQD variations degrades the performance of existing algorithms. In this study, the classification of all PQD variations that may occur is performed by using singular PQD and some composite PQD signals. A different number of subcomponents representing the signal are created according to each signal characteristic. Instantaneous energies from these subcomponents are used as deep learning (DL) input. Deep learning cycles are created as much as the instantaneous energy number of each signal. Each cycle has specific features of defining a single event. Therefore, the proposed approach is able to classify composite PQD signals that it has not encountered before. The proposed method's performance is first evaluated with the known PQD events and compared with the current state-of-the-art methods in the literature. Then, a dataset containing the combinations of different events not encountered during the training is created, and the performance is evaluated on this dataset. In the experiments performed, it is revealed that the proposed framework produces higher performance than other state-of-the-art methodsen_US
dc.description.sponsorshipDeanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia [RG-17-135-41]en_US
dc.identifier.citationSindi, H., Nour, M., Rawa, M., Öztürk, Ş., & Polat, K. (2021). An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events. Expert Systems with Applications, 178, 115023.en_US
dc.identifier.doi10.1016/j.eswa.2021.115023
dc.identifier.endpage13en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85110265063en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2021.115023
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11221
dc.identifier.volume178en_US
dc.identifier.wosWOS:000696711100004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPower Quality Disturbance (PQD)en_US
dc.subjectDeep Learningen_US
dc.subjectCNNen_US
dc.subjectHilbert-Huang Transformen_US
dc.subjectS-Transformen_US
dc.subjectWavelet Transformen_US
dc.titleAn adaptive deep learning framework to classify unknown composite power quality event using known single power quality eventsen_US
dc.typeArticleen_US

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