Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks

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A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

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The Dimensioning Sea Loads (DIMSELO) project:Paper

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Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

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Dependence of wind turbine loads on inlet flow field

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Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures

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Dependence of wind turbine loads on inlet flow field

In wind farm simulations, the inflow wind field plays a crucial role in the accuracy of both power production, structural load predictions and the turbulent wake development behind wind turbines. Three different inflow wind field generation techniques, namely the Mann model, a reduced order based model described herein and LES data, are used in this study to characterise the relation between the inflow and the structural response of the wind turbine. In addition, the wake development under different inflow conditions are studied. The turbulence statistics of the reduced-order model and the LES data are similar to each other while the Mann turbulence has different turbulence profiles and spectral characteristics. An in-house developed aeroelastic code, 3Dfloat, is used for structural response analysis. The differences between the inflow fields are mainly attributed to the turbulence intensity profiles, and differences in their spectral characteristics