63423 20190709135524.0 doi 10.1088/1742-6596/926/1/012009 sideral 102527 ART-2017-102527 eng González, E. On the use of high-frequency SCADA data for improved wind turbine performance monitoring 2017 Access copy available to the general public Unrestricted SCADA-based condition monitoring of wind turbines facilitates the move from costly corrective repairs towards more proactive maintenance strategies. In this work, we advocate the use of high-frequency SCADA data and quantile regression to build a cost effective performance monitoring tool. The benefits of the approach are demonstrated through the comparison between state-of-the-art deterministic power curve modelling techniques and the suggested probabilistic model. Detection capabilities are compared for low and high-frequency SCADA data, providing evidence for monitoring at higher resolutions. Operational data from healthy and faulty turbines are used to provide a practical example of usage with the proposed tool, effectively achieving the detection of an incipient gearbox malfunction at a time horizon of more than one month prior to the actual occurrence of the failure. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 642108-AWESOME info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.241 2017 Physics and Astronomy (miscellaneous) 2017 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Stephen, B. Infield, D. (orcid)0000-0003-2360-0845 Melero, J.J. Universidad de Zaragoza 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 926 (2017), 012009 [14 pp] J. Phys.: Conf. Ser. Journal of physics. Conference series 1742-6588 833645 http://zaguan.unizar.es/record/63423/files/texto_completo.pdf Versión publicada 13718 http://zaguan.unizar.es/record/63423/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:63423 articulos driver 2019-07-09-11:58:54 ARTICLE