Articles | Volume 6, issue 2
Wind Energ. Sci., 6, 539–554, 2021
https://doi.org/10.5194/wes-6-539-2021
Wind Energ. Sci., 6, 539–554, 2021
https://doi.org/10.5194/wes-6-539-2021
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 et al.

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Latest update: 27 Nov 2022
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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.