Abstract
Blade leading edge erosion is acknowledged to significantly reduce the energy yield of wind turbines. The problem is particularly severe for offshore installations, due to faster erosion progression boosted by harsh environmental conditions. This study presents and demonstrates an experimentally validated simulation-based technology for rapidly and accurately estimating wind turbine energy yield losses due to general leading edge erosion. The technology combines the predictive accuracy of two- and three-dimensional Navier–Stokes computational fluid dynamics with the runtime reductions enabled by artificial neural networks and wind turbine engineering codes using the blade element momentum theory. The main demonstration is based on the assessment of the annual energy yield of the National Renewable Energy Laboratory 5 MW reference turbine affected by leading edge erosion damage of increasing severity, considering damages based on available laser scans and previous leading edge erosion analysis. Results also include sensitivity studies of the energy loss to the wind characteristics of the installation site. It is found that the annual energy loss varies between about 0.3 and 4%, depending on the damage severity and the site wind characteristics. The study also illustrates the necessity of resolving the geometry of eroded leading edges rather than accounting for the effects of erosion with surrogate models, since, after an initial increase of distributed surface roughness, erosion yields leading edge geometry alterations causing aerodynamic losses exceeding those due to the loss of boundary layer laminarity consequent to roughness-induced transition. The presented technology enables estimating in a few minutes the amount of energy lost to erosion for many-turbine wind farms, and offers a key tool for predictive maintenance.
Highlights
Blade leading edge erosion (LEE) of wind turbines is an important and unresolved problem in the wind energy industry
Blade LEE spoils the aerodynamic performance of wind turbine rotors, causing reduced turbine power and, losses of annual energy production (AEP)
The first subsection describes the geometry parametrization and computational fluid dynamics (CFD) grid generation of the airfoils featuring LE erosion cavities included in the ALPS databases created for this study; the second sub section provides the values of the geometric parameters defining all considered eroded airfoils, and all key parameters used for the genera tion of the force coefficient Artificial neural networks (ANNs)
Summary
Blade leading edge erosion (LEE) of wind turbines is an important and unresolved problem in the wind energy industry. Schramm et al [18] used 2D CFD to assess the impact of a particular LE delamination pattern on the AEP of the National Renewable Energy Laboratory (NREL) 5 MW reference turbine [19] All these investigations provide a strong foundation for understanding key aspects of the aero dynamics of blades affected by LEE, but do not focus on industrial application, since they refer to specific and/or idealized erosion dam ages, whereas the LEE-induced power reduction and AEP loss of wind turbines and farms depend on significantly more complex and hetero geneous LEE patterns. To demonstrate the application of the generalized ALPS technology, the method is applied to determine the power reduction and AEP loss of a utility-scale wind turbine caused by a set of general LEE damages of increasing severity This enables estimating the growth of AEP losses during operation before blade surface maintenance takes place. A summary of the study and future work are provided in the closing Section 7
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