On the use of high-frequency SCADA data for improved wind turbine performance monitoring

Gonzales, E and Stephen, B and Infield, D and Melero, J (2017) On the use of high-frequency SCADA data for improved wind turbine performance monitoring. Journal of Physics: Conference Series, 926. 012009. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/926/1/012009)

[thumbnail of Gonzalez-etal-JPCS-2017-high-frequency-SCADA-data-for-improved-wind-turbine-performance-monitoring]
Text. Filename: Gonzalez_etal_JPCS_2017_high_frequency_SCADA_data_for_improved_wind_turbine_performance_monitoring.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (7MB)| Preview


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 eective performance monitoring tool. The benets 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, eectively 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.


Gonzales, E, Stephen, B ORCID logoORCID: https://orcid.org/0000-0001-7502-8129, Infield, D and Melero, J;