Posters

Abstract

This study presents the categorisation scheme developed in the Leading Edge Roughness categorisation project (LERCat), funded by the Danish Energy Agency (EUDP). The categorisation scheme links inspections of wind turbine blades to sectional aerodynamic and aeroacoustic losses due to leading edge roughness (LER). This enables the estimation of the Annual Energy Production (AEP) losses for wind turbines and allows the determination of the optimal time of repair based on a data-driven and objective workflow, reducing the maintenance cost due to LER on wind turbine blades. Initial investigations show that a one-year offset from the optimal time of repair amounts to 2500 DKK/MW/year and a two-year offset amounts to 4000 DKK/MW/year. As typical turbine sizes are above 4MW onshore and offshore beyond 12MW, a substantial amount can be gained. The presented scheme is the first of its kind to be freely available and developed transparently in co-operation between four of the world-leading wind turbine blade manufacturers (Vestas, LM, SiemensGamesa, and Suzlon) and an independent service provider (PowerCurve), with DTU as the overall project manager. The categorisation scheme is based on wind tunnel measurements and numerical simulations of three different wind turbine aerofoils subjected to different LER representations. Going beyond the established methods for simulating LER, i.e. zigzag tape and sandpaper, this included high-resolution LER topographies obtained from scans of wind turbine blades. The workflow for estimating AEP losses on a wind turbine with the categorisation scheme is presented. It shows how to link inspection data to sectional aerodynamic losses and modify the original aerodynamic data to account for losses due to LER. Using the workflow and the categorisation scheme together with a representative LER blade distribution and simulating turbine response in the aeroelastic code HAWC2, the AEP loss is shown to be 1.2%.