Intelligent Observer for Fault Detection and Data Recovery for Small Wind Turbine

UDC.coleccionInvestigación
UDC.departamentoEnxeñaría Industrial
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.grupoInvCiencia e Técnica Cibernética (CTC)
UDC.grupoInvLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.issue1
UDC.journalTitleLogic Journal of the IGPL
UDC.startPagejzaf026
UDC.volume34
dc.contributor.authorDíaz-Longueira, Antonio
dc.contributor.authorÁlvarez-Crespo, Marta María
dc.contributor.authorMichelena, Álvaro
dc.contributor.authorTimiraos, Míriam
dc.contributor.authorFontenla-Romero, Óscar
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2026-04-10T06:29:11Z
dc.date.available2026-04-10T06:29:11Z
dc.date.issued2026-01-28
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUG
dc.description.abstract[Abstract] In this work, an intelligent observer is developed and implemented for fault detection and information retrieval in a small wind turbine. The intelligent observer will allow the detection of faults in the turbine’s energy generation, thanks to the estimation of the active power generated. This estimate will be compared with the real active power, measured by a real sensor, so that, if there is not a very high deviation between both values, the sensor measurement will be considered valid. However, when the difference between the estimated value and the real value exceeds a threshold, the sensor measurement will be replaced with the estimate. This threshold will be determined based on the operating point. The results obtained are satisfactory, with a correct estimation and detection of anomalies.
dc.description.sponsorshipXunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49). CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the “Axudas á etapa predoutoral” grant with reference: ED481A-2023-072. Míriam Timiraos’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to industrial Ph.D. (http://gain.xunta.gal), under the Doutoramento Industrial 2022 grant with reference: 04_IN606D_2022_2692965⁠. Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932.
dc.description.sponsorshipXunta de Galicia; ED431B 2023/49
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01
dc.description.sponsorshipXunta de Galicia; ED481A-2023-072
dc.description.sponsorshipXunta de Galicia; 04_IN606D_2022_2692965
dc.identifier.citationAntonio Díaz-Longueira, Marta-María Álvarez-Crespo, Álvaro Michelena, Míriam Timiraos, Óscar Fontenla-Romero, José Luis Calvo-Rolle, Intelligent observer for fault detection and data recovery for small wind turbine, Logic Journal of the IGPL, Volume 34, Issue 1, February 2026, jzaf026, https://doi.org/10.1093/jigpal/jzaf026
dc.identifier.doi10.1093/jigpal/jzaf026
dc.identifier.issn1368-9894
dc.identifier.urihttps://hdl.handle.net/2183/47928
dc.language.isoeng
dc.publisherOxford University Press
dc.relation.projectIDinfo:eu-repo/grantAgreement/MUNI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F00932/ES
dc.relation.urihttps://doi.org/10.1093/jigpal/jzaf026
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFault detection
dc.subjectAnomaly detection
dc.subjectGreen energy
dc.subjectHybrid model
dc.titleIntelligent Observer for Fault Detection and Data Recovery for Small Wind Turbine
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
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