TY - JOUR
T1 - Predicting wind farm operations with machine learning and the P2D-RANS model
T2 - A case study for an AWAKEN site
AU - Moss, Coleman
AU - Maulik, Romit
AU - Moriarty, Patrick
AU - Iungo, Giacomo Valerio
N1 - Publisher Copyright:
© 2023 The Authors. Wind Energy published by John Wiley & Sons Ltd.
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174624673&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174624673&partnerID=8YFLogxK
U2 - 10.1002/we.2874
DO - 10.1002/we.2874
M3 - Article
AN - SCOPUS:85174624673
SN - 1095-4244
VL - 27
SP - 1245
EP - 1267
JO - Wind Energy
JF - Wind Energy
IS - 11
ER -