On the robustness of a blade load-based wind speed estimator to dynamic pitch control strategies
(2024) Wind Energy Science — (2024)
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- Abstract
- Current implementations of wind turbine pitch controllers for load alleviation or active wake mixing use limited information about the incoming wind. While these pitch controllers could benefit from broader wind condition awareness, the lack of suitable sensing methods is limiting. Blade load-based wind speed estimators are an alternative to cup anemometers or LiDARs. In this paper, we wish to verify how robust such estimators are to the control strategy active on the turbine, as it impacts both operating parameters and loads. We use an Extended Kalman Filter (EKF) to estimate incoming wind conditions based on blade out-of-plane bending moments. The internal model in the EKF relies on the Blade Element Momentum (BEM) theory in which we propose to account for delays between pitch action and blade loads by including dynamic effects. Using Large-Eddy Simulations to test the estimator, we show that accounting for the dynamic effects in the BEM formulation is needed to maintain the estimator accuracy when dynamic wake mixing control is active.
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Citations
Coquelet, M., Lejeune, M., Bricteux, L., van Vondelen, A., van Wingerden, J.-W., & Chatelain, P. (2024). On the robustness of a blade load-based wind speed estimator to dynamic pitch control strategies. Wind Energy Science. Published. https://doi.org/10.5194/wes-9-1923-2024 (Original work published 2024)