Blade Effective Wind Speed Estimation
A Subspace Predictive Repetitive Estimator Approach
Y. Liu (TU Delft - Team Riccardo Ferrari)
Atindriyo K. Pamososuryo (TU Delft - Team Jan-Willem van Wingerden)
Riccardo Maria Giorgio Ferrari (TU Delft - Team Riccardo Ferrari)
Tobias Gybel Hovgaard (Vestas Technology R&D)
J.W. Van Wingerden (TU Delft - Team Jan-Willem van Wingerden)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Modern wind turbine control algorithms typically utilize rotor effective wind speed measured from an anemometer on the turbine’s nacelle. Unfortunately, the measured wind speed from such a single measurement point does not give a good representation of the effective wind speed over the blades, as it does not take the varying wind condition within the entire rotor area into account. As such, Blade Effective Wind Speed (BEWS) estimation can be seen as a more accurate alternative. This paper introduces a novel Subspace Predictive Repetitive Estimator (SPRE) approach to estimate the BEWS using blade load measurements. In detail, the azimuth-dependent cone coefficient is firstly formulated to describe the mapping between the out-of-plane blade root bending moment and the wind speed over blades. Then, the SPRE scheme, which is inspired by Subspace Predictive Repetitive Control (SPRC), is proposed to estimate the BEWS. Case studies exhibit the proposed method’s effectiveness at predicting BEWS and identifying wind shear in varying wind speed conditions. Moreover, this novel technique enables complicated wind inflow conditions, where a rotor is impinged and overlapped by wake shed from an upstream turbine, to be estimated.