High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm

Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements

“…We distinguish our method with traditional numerical methods and existing machine learning based wind prediction methods as follows: 1) Various numerical models e.g. [36,37,38] are widely used for wind simulations and the detailed wind field can be obtained by solving the NS equations numerically with properly-defined boundary conditions or the input conditions estimated from measurement data [39]. However, these models are mainly designed for forward simulations of wind flows.…”

Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning

“…Numerical approaches have been investigated to obtain the detailed spatiotemporal wind field information [13,6], by numerically solving the Navier-Stokes (NS) equations. The integration of wind measurements and numerical simulations has also been explored recently [14]. However, numerical models were mainly designed for forward simulations thus cannot take real-time wind measurements into account.…”

Dynamic Equivalent Modeling of Wind Farm Based on Dominant Variable Hierarchical Clustering Algorithm

“…Literature [5] proposed a method of reduced-order model computation for planning grid connection of a large-scale wind farm, and the effectiveness of the proposed method is demonstrated and evaluated by an illustrative simple example wind farm and an example large-scale wind farm with 200 WTGs (wind turbine generators). Literature [6] used the integrating field measurement method to characterize the monthly wind speed and wind direction distributions and investigate the wind characteristics in turbine wakes.…”