Simultaneous remote monitoring of transformers' ambient parameters by using IoT

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dc.authoridCEKLİ, Serap/0000-0002-8113-0514en_US
dc.authoriduzunoğlu, cengiz polat/0000-0002-4891-3963en_US
dc.contributor.authorHasır, M.
dc.contributor.authorCekli, S.
dc.contributor.authorUzunoğlu, C. P.
dc.date.accessioned2024-07-12T21:37:28Z
dc.date.available2024-07-12T21:37:28Z
dc.date.issued2021en_US
dc.department[Belirlenecek]en_US
dc.description.abstractTransformers have an important role in the uninterrupted and reliable flow of electrical power in power systems. For this reason, it is necessary to monitor and control the proper and efficient operation of transformers regularly. The increase in the number of transformers, the differentiation of transformer operations, and the prolongation of the transformers' operating time, accelerates the importance of remote monitoring. When remote monitoring structures are compared, even if conventional monitoring methods can perform many tasks, it has become suitable to establish lower cost, safer and simpler structures by using the Internet of Things (IoT). Apart from monitoring the electrical parameters of the transformer, it is possible to obtain information about the operation and fault status of the transformer by monitoring the physical and chemical changes in the environment. In this study, a measurement and remote monitoring system that detects temperature, humidity, light level and gas densities are employed for low and medium voltage transformers in the indoor environment. Tests are conducted to establish a connection between the obtained ambient data and transformer operating voltages. Different classification algorithms such as Bayesian Networks (BN), Multilayer Perceptron (MLP), and Random Forest (RF) are used to classify the operating voltages versus ambient data. (C) 2021 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipIstanbul University-Cerrahpasa Research Fund [FYL-2020-34411]; Istanbul University-Cerrahpasa Research Funden_US
dc.description.sponsorshipThis work was supported by. Istanbul University-Cerrahpasa Research Fund with the project code FYL-2020-34411. The authors would like to thank. Istanbul University-Cerrahpasa Research Fund for this financial support.en_US
dc.identifier.doi10.1016/j.iot.2021.100390
dc.identifier.issn2543-1536
dc.identifier.issn2542-6605
dc.identifier.scopus2-s2.0-85114807090en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.iot.2021.100390
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6790
dc.identifier.volume14en_US
dc.identifier.wosWOS:000695695900043en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInternet of Thingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY04132
dc.subjectIoten_US
dc.subjectTransformeren_US
dc.subjectBayes Networken_US
dc.subjectMultilayer Perceptronen_US
dc.subjectRandom Foresten_US
dc.titleSimultaneous remote monitoring of transformers' ambient parameters by using IoTen_US
dc.typeArticle
dspace.entity.typePublication

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