Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

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dc.contributor.authorTuranli, Ozan
dc.contributor.authorYakut, Yurdagul Bentesen
dc.date.accessioned2025-02-22T14:08:41Z
dc.date.available2025-02-22T14:08:41Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractEvery part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system's protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.en_US
dc.identifier.doi10.3390/app14209590
dc.identifier.issn2076-3417
dc.identifier.issue20en_US
dc.identifier.scopus2-s2.0-85207548349en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/app14209590
dc.identifier.urihttps://hdl.handle.net/11468/29568
dc.identifier.volume14en_US
dc.identifier.wosWOS:001341720600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250222
dc.subjecttransmission lineen_US
dc.subjectreal and synthetic dataen_US
dc.subjectfault classificationen_US
dc.subjectdeep learningen_US
dc.subject1D CNNen_US
dc.titleClassification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasetsen_US
dc.typeArticleen_US

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