Print Email Facebook Twitter UKF-based Wind Estimation and Sub-optimal Turbine Control under Waked Conditions Title UKF-based Wind Estimation and Sub-optimal Turbine Control under Waked Conditions Author Gonzalez Silva, J. (TU Delft Team Riccardo Ferrari) Liu, Y. (TU Delft Team Riccardo Ferrari) Ferrari, Riccardo M.G. (TU Delft Team Riccardo Ferrari) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Date 2023 Abstract The knowledge of the Effective wind speed (EWS) allows the designing of wind turbine controllers that regulate power production and reduce loads on turbine components. Traditional single-point measurements are known to suffer from high noise and poor correlation with the EWS. As an alternative to overcome these problems, EWS estimators can be designed. The main challenge is the high non-linearity of the wind speed influence on the drive-train dynamics. Therefore, an estimator based on the unscented Kalman filter (UKF) is proposed and compared against an extended Kalman filter (EKF) and the immersion and invariance (I&I) technique. Simulation results are provided and show the superior performances attained by the UKF. Furthermore, the usefulness of the estimated EWS is demonstrated by designing a sliding mode controller (SMC) that can track a desired power reference. In addition, the controller allows operating in sub-optimal conditions, where load reduction is attained at the expense of power maximization. The proposed estimator's and controller's performances are evaluated under wind farm wake conditions via high-fidelity simulations. The findings show that UKF can outperform the EKF and the controller can reduce loads, except under highly waked conditions. Subject Nonlinear observers and filter designSliding mode controlWind turbine control To reference this document use: http://resolver.tudelft.nl/uuid:5949d072-d89c-424b-bd99-0788173015ad DOI https://doi.org/10.1016/j.ifacol.2023.10.1166 Source IFAC-PapersOnLine, 56 (2), 7662-7667 Event 22nd IFAC World Congress, 2023-07-09 → 2023-07-14, Yokohama, Japan Part of collection Institutional Repository Document type journal article Rights © 2023 J. Gonzalez Silva, Y. Liu, Riccardo M.G. Ferrari, J.W. van Wingerden Files PDF 1-s2.0-S2405896323015690-main.pdf 1.79 MB Close viewer /islandora/object/uuid:5949d072-d89c-424b-bd99-0788173015ad/datastream/OBJ/view