Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion

Blade leading edge erosion is acknowledged to significantly reduce the energy yield of wind turbines. The problem particularly severe for offshore installations, due faster progression boosted by harsh environmental conditions. This study presents and demonstrates an experimentally validated simulation-based technology rapidly accurately estimating turbine losses general erosion. combines predictive accuracy two- three-dimensional Navier–Stokes computational fluid dynamics with runtime reductions enabled artificial neural networks engineering codes using blade element momentum theory. main demonstration based on assessment annual National Renewable Energy Laboratory 5 MW reference affected damage increasing severity, considering damages available laser scans previous analysis. Results also include sensitivity studies loss characteristics installation site. It found that varies between about 0.3 4%, depending severity site characteristics. illustrates necessity resolving geometry eroded edges rather than accounting effects surrogate models, since, after initial increase distributed surface roughness, yields alterations causing aerodynamic exceeding those boundary layer laminarity consequent roughness-induced transition. presented enables in a few minutes amount lost many-turbine farms, offers key tool maintenance.