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dc.contributor.author
Zaldivar Sanchez, Diego Armando  
dc.contributor.author
Sanchez, Angel Manuel  
dc.contributor.author
Romero Quete, Andrés Arturo  
dc.date.available
2023-11-28T15:59:06Z  
dc.date.issued
2023-07  
dc.identifier.citation
Zaldivar Sanchez, Diego Armando; Sanchez, Angel Manuel; Romero Quete, Andrés Arturo; A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets; Elsevier Science SA; Electric Power Systems Research; 220; 109374; 7-2023; 1-9  
dc.identifier.issn
0378-7796  
dc.identifier.uri
http://hdl.handle.net/11336/218708  
dc.description.abstract
Power transformers (PT) are critical assets in power systems. Electric utilities and power companies that manage PT fleets require a large amount of information to inform decision-making processes related to acquisition, maintenance, operation, and unit availability. Processing such information poses a significant challenge, as misinterpretation could lead to incorrect decisions that compromise unit condition, ultimately impacting power system operation. Considering the above-mentioned, in this paper, a methodology for asset management on PT fleets is proposed. The first step of the methodology involves determining the technical condition of individual equipment and its strategic importance. PT units are then clustered according to related risk, and for each cluster, the status of the units and economic factors are analyzed to determine the optimal inspection strategy for condition-based maintenance. The proposed methodology was applied to a case study of a fleet of fifty-eight PT. The obtained results demonstrate the suitability of the methodology for practical application. Specifically, the methodology enables the asset manager to apply condition-based maintenance strategies effectively to PT fleets.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science SA  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
ASSET MANAGEMENT  
dc.subject
CLUSTERING TECHNIQUES  
dc.subject
FUZZY INFERENCE SYSTEM  
dc.subject
HEALTH INDEX  
dc.subject
MAINTENANCE  
dc.subject
POWER TRANSFORMER  
dc.subject
RISK ASSESSMENT  
dc.subject.classification
Ingeniería Eléctrica y Electrónica  
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
A comprehensive methodology for the optimization of condition-based maintenance in power transformer fleets  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-11-28T14:46:05Z  
dc.journal.volume
220  
dc.journal.number
109374  
dc.journal.pagination
1-9  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Zaldivar Sanchez, Diego Armando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina  
dc.description.fil
Fil: Sanchez, Angel Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina  
dc.description.fil
Fil: Romero Quete, Andrés Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina  
dc.journal.title
Electric Power Systems Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378779623002638  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.epsr.2023.109374