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Abstract. Establishing a clear correlation between blade leading-edge erosion (LEE) and the performance of operational wind turbines is challenging due to the complex interaction of various factors. This study aims to improve the understanding and analysis of real wind turbine measurements by employing aeroelastic simulations to investigate the combined effects of LEE, turbulence intensity (TI), and time averaging as a data processing technique and to show how they obscure the effects of erosion. The study does not aim to investigate each contributing factor in detail but seeks to provide insights through selected examples, thereby illustrating how these conditions hinder the detection of blade erosion’s effects on power loss. An aeroelastic model provided by an offshore original-equipment manufacturer (OEM) was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds, and the aerofoil characteristics for the blade were modified to simulate different degrees of erosion, represented by varying levels of roughness. For a given site, findings reveal that even mild simulated erosion can reduce the annual energy production (AEP) by 0.82 % at 6 % TI, while more severe erosion leads to a 1.46 % decrease. Furthermore, increasing TI exacerbates these losses, with 15 % TI causing up to 2.14 % AEP reduction for eroded blades, making it increasingly difficult to distinguish between the effects of blade erosion and turbulence intensity on turbine performance. These effects are most pronounced at sites with lower average wind speeds. Moreover, the interaction between TI levels and longer time-averaging periods, which vary with wind speed, can obscure the true magnitude of LEE's impact on short-term power fluctuations. This study suggests that 10 min time-averaging periods can mask performance and that analysing unsteady-rotor data with shorter time periods, such as 1 s periods, is preferable. The work emphasises the importance of considering the blade condition's impact in the context of various influencing factors for accurate AEP assessments, performance monitoring, and improved wind turbine design for operational wind turbines.
Malik et al. (Thu,) studied this question.