Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 6
Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-893-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-893-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 6
Review article
 | 
05 Jun 2023
Review article |  | 05 Jun 2023

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms

Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

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Revised manuscript under review for WES
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Cited articles

Bangalore, P. and Tjernberg, L. B.: Self evolving neural network based algorithm for fault prognosis in wind turbines: A case study, in: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 7–10 July 2014, Durham, 1–6, https://doi.org/10.1109/PMAPS.2014.6960603, 2014. a, b
Bangalore, P. and Tjernberg, L. B.: An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings, IEEE T. Smart Grid, 6, 980–987, https://doi.org/10.1109/TSG.2014.2386305, 2015. a, b
Bangalore, P., Letzgus, S., Karlsson, D., and Patriksson, M.: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox, Wind Energy, 20, 1421–1438, 2017. a, b, c, d
Beretta, M., Cárdenas, J., Koch, C., and Cusidó, J.: Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders, Appl. Sci.-Basel, 10, 8649, https://doi.org/10.3390/app10238649, 2020. a, b, c, d, e, f, g
Beretta, M., Julian, A., Sepulveda, J., Cusidó, J., and Porro, O.: An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing, Sensors, 21, 1–20, 2021. a, b, c, d
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This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
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