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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks
AU - Campobasso, Sergio
AU - Cavazzini, Anna
AU - Minisci, Edmondo
PY - 2020/6/26
Y1 - 2020/6/26
N2 - Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier-Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion.
AB - Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier-Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion.
KW - wind turbine blade leading edge erosion
KW - wind farm efficiency
KW - wind farm operation and maintenance costs
KW - machine learning
KW - computational fluid dynamics
KW - wind turbine control
KW - wind turbine engineering codes
U2 - 10.1115/1.4047186/1083745
DO - 10.1115/1.4047186/1083745
M3 - Journal article
VL - 142
JO - Journal of Turbomachinery
JF - Journal of Turbomachinery
SN - 0889-504X
IS - 7
M1 - 071002
ER -