Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 1
Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 1
Research article
 | 
22 Jan 2024
Research article |  | 22 Jan 2024

Active trailing edge flap system fault detection via machine learning

Andrea Gamberini and Imad Abdallah

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Short summary
Active trailing edge flaps can potentially reduce wind turbine (WT) loads. To monitor their performance, we present two methods based on machine learning that identify flap health states, including degraded performance, in normal power production and idling condition. Both methods rely only on sensors commonly available on WTs. One approach properly detects all the flap states if a fault occurs on only one blade. The other approach can identify two specific flap states in all fault scenarios.
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