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.
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.
the Creative Commons Attribution 4.0 License.
Review article
| 
05 Jun 2023
Review article |
| 05 Jun 2023
| 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
CORRESPONDING AUTHOR
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Timothy Verstraeten
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Pieter-Jan Daems
AVRG, Vrije Universiteit Brussel, Pleinlaan 3, 1050 Brussels, Belgium
Ann Nowé
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Jan Helsen
AVRG, Vrije Universiteit Brussel, Pleinlaan 3, 1050 Brussels, Belgium
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Short summary
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.
This paper reviews and implements several techniques that can be used for condition monitoring...
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