Universidad de Zaragoza Repository

Resumen: 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.
Idioma: Inglés
DOI: 10.1088/1742-6596/926/1/012009
Año: 2017
Publicado en: Journal of physics. Conference series 926 (2017), 012009 [14 pp]
ISSN: 1742-6588
Factor impacto SCIMAGO: 0.241 - Physics and Astronomy (miscellaneous) (Q3)

Financiación: info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)

You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Exportado de SIDERAL (2019-07-09-11:58:54)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
Articles