Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Journal Article · 22 October 2023 · Wind Energy

DOI: https://doi.org/10.1002/we.2874 · OSTI ID:2203295

Moss, Coleman  ^[1]; Maulik, Romit ^[2]; Moriarty, Patrick ^[3]; Iungo, Giacomo Valerio  ^[1]

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1. Department of Mechanical Engineering, Wind Fluids and Experiments (WindFluX) Laboratory The University of Texas at Dallas Richardson Texas USA
2. Pennsylvania State University State College Pennsylvania USA, Argonne National Laboratory Lemont Illinois USA
3. National Renewable Energy Laboratory Golden Colorado USA

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 2203295
• Report Number(s): NREL/JA-5000-88030
• Journal Information: Wind Energy, Journal Name: Wind Energy Journal Issue: 11 Vol. 27; ISSN 1095-4244
• Publisher: Wiley Blackwell (John Wiley & Sons)Copyright Statement
• Country of Publication: United Kingdom
• Language: English