Research Output per year
Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage, such as leading edge erosion, is essential for optimizing maintenance planning. This requirement prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate wind turbine annual energy production losses due to blade leading edge damage. The power curve of a turbine with nominal or 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, consists of 6000+ airfoils, 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 focus on a utility-scale offshore wind turbine and demonstrate that realistic estimates of the annual energy production loss due to leading edge damage can be obtained in just a few seconds using a desktop computer, highlighting the viability and the industrial impact of this new technology for wind farm operation and maintenance.
- wind turbine aerodynamics
- artificial neural networks
- wind turbine design
Minisci, E. & Campobasso, M. S., 13 Dec 2019, ASME 2019 2nd International Offshore Wind Technical Conference. [S.I.], 14 p. IOWTC2019-7578
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book