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dc.contributor.authorBustamante Sánchez, Sergio 
dc.contributor.authorMañana Canteli, Mario 
dc.contributor.authorArroyo Gutiérrez, Alberto 
dc.contributor.authorLaso Pérez, Alberto 
dc.contributor.authorMartínez Torre, Raquel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2021-01-25T13:29:51Z
dc.date.available2021-01-25T13:29:51Z
dc.date.issued2020-12-13
dc.identifier.issn2076-3417
dc.identifier.otherRTC-2017-6782-3es_ES
dc.identifier.urihttp://hdl.handle.net/10902/20535
dc.description.abstractPower transformers are considered to be the most important assets in power substations. Thus, their maintenance is important to ensure the reliability of the power transmission and distribution system. One of the most commonly used methods for managing the maintenance and establishing the health status of power transformers is dissolved gas analysis (DGA). The presence of acetylene in the DGA results may indicate arcing or high-temperature thermal faults in the transformer. In old transformers with an on-load tap-changer (OLTC), oil or gases can be filtered from the OLTC compartment to the transformer?s main tank. This paper presents a method for determining the transformer oil contamination from the OLTC gases in a group of power transformers for a distribution system operator (DSO) based on the application of the guides and the knowledge of experts. As a result, twenty-six out of the 175 transformers studied are defined as contaminated from the OLTC gases. In addition, this paper presents a methodology based on machine learning techniques that allows the system to determine the transformer oil contamination from the DGA results. The trained model achieves an accuracy of 99.76% in identifying oil contamination.es_ES
dc.description.sponsorshipThis work was partially financed by the EU Regional Development Fund (FEDER) and the Spanish Government under RETOS-COLABORACIÓN RTC-2017-6782-3 and by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 864579 (FLEXIGRID).es_ES
dc.format.extent19 p.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceApplied Sciences, 2020, 10(24), 8897es_ES
dc.subject.otherCommunicating OLTCes_ES
dc.subject.otherDissolved gas analysises_ES
dc.subject.otherFault locationes_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherMaintenance managementes_ES
dc.subject.otherOil insulationes_ES
dc.subject.otherOLTC contaminationes_ES
dc.subject.otherPower transformeres_ES
dc.titleDetermination of transformer oil contamination from the OLTC gases in the power transformers of a distribution system operatores_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/864579/EU/Interoperable solutions for implementing holistic FLEXIbility services in the distribution GRID/FLEXIGRID/es_ES
dc.identifier.DOI10.3390/app10248897
dc.type.versionpublishedVersiones_ES


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Mostrar el registro sencillo

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Excepto si se señala otra cosa, la licencia del ítem se describe como © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.