dc.contributor.author | Bustamante Sánchez, Sergio | |
dc.contributor.author | Mañana Canteli, Mario | |
dc.contributor.author | Arroyo Gutiérrez, Alberto | |
dc.contributor.author | Laso Pérez, Alberto | |
dc.contributor.author | Martínez Torre, Raquel | |
dc.contributor.other | Universidad de Cantabria | es_ES |
dc.date.accessioned | 2021-01-25T13:29:51Z | |
dc.date.available | 2021-01-25T13:29:51Z | |
dc.date.issued | 2020-12-13 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.other | RTC-2017-6782-3 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10902/20535 | |
dc.description.abstract | Power 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.sponsorship | This 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.extent | 19 p. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_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.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.source | Applied Sciences, 2020, 10(24), 8897 | es_ES |
dc.subject.other | Communicating OLTC | es_ES |
dc.subject.other | Dissolved gas analysis | es_ES |
dc.subject.other | Fault location | es_ES |
dc.subject.other | Machine learning | es_ES |
dc.subject.other | Maintenance management | es_ES |
dc.subject.other | Oil insulation | es_ES |
dc.subject.other | OLTC contamination | es_ES |
dc.subject.other | Power transformer | es_ES |
dc.title | Determination of transformer oil contamination from the OLTC gases in the power transformers of a distribution system operator | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/864579/EU/Interoperable solutions for implementing holistic FLEXIbility services in the distribution GRID/FLEXIGRID/ | es_ES |
dc.identifier.DOI | 10.3390/app10248897 | |
dc.type.version | publishedVersion | es_ES |