Physics informed neural networks for wind field modeling in wind farms
Pablo Cobelli, Khemraj Shukla, Sergio Nesmachnow, Martín Draper
Abstract
Abstract This article presents an approach for applying Physics-Informed Neural Networks (PINNs) for modeling wind fields in wind farms. The addressed problem is reconstructing the inflow velocity field for a wind turbine. Several PINN variants are implemented and validated over a real-world case study, trained with sparse numerically simulated velocity data. Results demonstrate that the proposed PINNs can accurately assimilate the numerical simulation data, and compute accurate solutions. The proposed approach is a viable alternative for modeling wind fields in wind farms, requiring significantly lower execution times than standard numerical/simulation methods.
Topics & Concepts
TurbineArtificial neural networkInflowField (mathematics)Wind speedVector fieldNumerical modelingComputer simulationMeteorologyWind powerComputer scienceMarine engineeringSimulationAerospace engineeringEngineeringPhysicsMechanicsArtificial intelligenceMathematicsGeophysicsElectrical engineeringPure mathematicsModel Reduction and Neural NetworksFluid Dynamics and Vibration AnalysisWind Energy Research and Development