Abstract

Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in operation is needed to optimise turbine settings and, consequently, to reduce alternate bending. In our approach, an acceleration model was used to analyse periodically occurring deviations from uniform bending. By using hierarchical clustering, significant bending patterns could be extracted and patterns were analysed with regard to reference data. In a simulation of alternate bending effects, various effects were successfully represented by different bending patterns. A real data experiment with accelerometers mounted at the blade tip of turbine blades demonstrated a clear relation between the rotation frequency and the resulting bending patterns. Additionally, the markedness of bending shapes could be used to assess the amount of alternate bending of the blade in both simulations and experiment.s The results demonstrate that model-based bending shapes provide a strong indication for alternate bending and, consequently, can be used to optimise turbine settings.

Highlights

  • With a newly installed wind energy capacity of 60.4 GW worldwide, installations increased by19% in comparison to 2018 and started to contribute to the global demand to reduce carbon emissions [1]

  • The minimum number of elements per class was set to Nc = 3; each class does not correspond to a one-time event but represents certain operational and environmental conditions, e.g., pitch, yaw, and wind profile, which lead to alternate bending

  • The current trend of increasing the blade length for maximum energy capture results in larger loads and forces acting on the blades

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Summary

Introduction

With a newly installed wind energy capacity of 60.4 GW worldwide, installations increased by19% in comparison to 2018 and started to contribute to the global demand to reduce carbon emissions [1]. Maximising the energy capture per turbine results in fewer turbines per farm, thereby leading to a reduction in the levelised costs of energy. This aspect even comes into effect considering the higher costs of the components of larger turbines [2]. During the last 20 years, the energy capture per turbine could significantly be enhanced by increasing the height and diameter of turbines. The average rated newly installed capacity in the United States of 2.43 MW at an average rotor diameter of 115.6 m in 2018 corresponds to an increase of 239% in capacity and of 41% in blade length in comparison to the year 1998 [3]. The largest prototype turbine in the world started to operate with a rotor diameter of 220 m in 2019 [4]

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