Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 2
Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 2
Research article
 | 
21 Apr 2021
Research article |  | 21 Apr 2021

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Artur Movsessian, Marcel Schedat, and Torsten Faber

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Short summary
The assessment of the structural condition and technical lifetime extension of a wind turbine is challenging due to lack of information for the estimation of fatigue loads. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to correlation analysis, stepwise regression, and principal component analysis.
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