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Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG

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Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG

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dc.contributor.author Artigao, Estefania es_ES
dc.contributor.author Sapena-Bano, Angel es_ES
dc.contributor.author Honrubia-Escribano, Andrés es_ES
dc.contributor.author Martinez-Roman, Javier es_ES
dc.contributor.author Puche-Panadero, Rubén es_ES
dc.contributor.author Gómez-Lázaro, Emilio es_ES
dc.date.accessioned 2020-04-29T07:05:25Z
dc.date.available 2020-04-29T07:05:25Z
dc.date.issued 2019-01-29 es_ES
dc.identifier.uri http://hdl.handle.net/10251/141971
dc.description (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. es_ES
dc.description.abstract [EN] While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors to failure rates and downtime of WTs, where the doubly fed induction generator (DFIG) is the dominant technology among variable speed WTs. Thus, the early detection of generator faults, which can be achieved through predictive maintenance, is vital in order to reduce O&M costs. The goal of this paper is to analyze a long-term monitoring campaign of an in-service WT equipped with a DFIG. A novel method named the harmonic order tracking analysis is used with two main objectives: first, to facilitate the data interpretation for non-trained maintenance personnel, and second, to reduce the amount of data that must be stored and transferred for the diagnosis of the DFIG. This method is applied and validated for the first time on an operating WT. es_ES
dc.description.sponsorship This work was supported in part by the Agreement signed between the UCLM and the Council of Albacete to promote research in the Campus of Albacete, and in part by the European Union Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie Grant (AWESOME Project) under Grant 642108. The authors would like to thank Ingeteam Power Technology S.A. UP Service, part of the AWESOME Project Consortium providing the wind turbine data. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Access es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Doubly fed induction generators es_ES
dc.subject Harmonic analysis es_ES
dc.subject Stators es_ES
dc.subject Maintenance engineering es_ES
dc.subject Rotors es_ES
dc.subject Current measurement es_ES
dc.subject Wind turbines es_ES
dc.subject Condition monitoring es_ES
dc.subject Current signature analysis es_ES
dc.subject DFIG es_ES
dc.subject HOTA es_ES
dc.subject Wind turbine es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/ACCESS.2019.2895999 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Artigao, E.; Sapena-Bano, A.; Honrubia-Escribano, A.; Martinez-Roman, J.; Puche-Panadero, R.; Gómez-Lázaro, E. (2019). Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG. IEEE Access. 7:17896-17906. https://doi.org/10.1109/ACCESS.2019.2895999 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/ACCESS.2019.2895999 es_ES
dc.description.upvformatpinicio 17896 es_ES
dc.description.upvformatpfin 17906 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7 es_ES
dc.identifier.eissn 2169-3536 es_ES
dc.relation.pasarela S\378157 es_ES
dc.contributor.funder Ayuntamiento de Albacete es_ES
dc.contributor.funder Universidad de Castilla-La Mancha es_ES


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