Uncertainty propagation and sensitivity analysis of an artificial neural network used as wind turbine load surrogate model

Laura Schröder*, Nikolay Krasimirov Dimitrov, John Aasted Sørensen

*Corresponding author for this work

    Research output: Contribution to journalConference articleResearchpeer-review

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    Recent studies have shown the advantage of replacing aeroelastic simulations with regression models based on Artificial Neural Networks (ANNs), which can be used as surrogate models for fast and efficient wind turbine load assessments. Once trained on a high-fidelity load simulation database covering a broad range of conditions, the surrogate model can be applied to predict loads for any site with wind climate falling within the range covered by the database. The aim of this study is to quantify the uncertainty propagation through such an ANN and to analyse how much the selected input variables influence the variance of the fatigue blade load estimations by means of a global sensitivity analysis. Results confirm that the selected ANN architecture seems suitable for this task resulting in small output uncertainties. Furthermore, the sensitivity analysis shows that the turbulence is mainly responsible for the blade load estimation, followed by the wind shear and the wind speed. The contributions of the turbulence length scale, turbulence anisotropy factor and wind veer angle are comparatively low. Comparing three different methods for sensitivity analysis shows that the partial derivative algorithm, Sobol variance decomposition and Shapley effect result in similar sensitivity measures.
    Original languageEnglish
    Article number042040
    Book seriesJournal of Physics: Conference Series
    Issue number4
    Number of pages12
    Publication statusPublished - 2020
    EventTORQUE 2020 - Online event, Netherlands
    Duration: 28 Sept 20202 Oct 2020


    ConferenceTORQUE 2020
    LocationOnline event
    Internet address


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