A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0003-2371-8173en_US
dc.authorid0000-0002-6624-6148en_US
dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0001-6035-5733en_US
dc.contributor.authorSindi, Hatem
dc.contributor.authorNour, Majid
dc.contributor.authorRawa, Muhyaddin
dc.contributor.authorÖztürk, Şaban
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-06-16T08:29:05Z
dc.date.available2023-06-16T08:29:05Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis project was funded by the Deanship of Scientific Research (DSR) , King Abdulaziz University, Jeddah, Saudi Arabia, under grant No. (RG1713541) . The authors, therefore, gratefully acknowledge DSR technical and financial support.en_US
dc.description.abstractAs a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.en_US
dc.identifier.citationSindi, H., Nour, M., Rawa, M., Öztürk, Ş., & Polat, K. (2021). A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification. Expert Systems with Applications, 174, 114785.en_US
dc.identifier.doi10.1016/j.eswa.2021.114785
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85102262779en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2021.114785
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11132
dc.identifier.volume174en_US
dc.identifier.wosWOS:000663146900009en_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.subjectPQDen_US
dc.subject1D CNNen_US
dc.subject2D CNNen_US
dc.subjectClassificationen_US
dc.subjectPower Signalen_US
dc.subjectSignal Disturbanceen_US
dc.titleA novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classificationen_US
dc.typeArticleen_US

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