Print Email Facebook Twitter An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers Title An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers Author Mulders, S.P. (TU Delft Team Mulders) Liu, Y. (TU Delft Team Riccardo Ferrari) Spagnolo, Fabio (Vestas Wind Systems) Christensen, P.B. (Vestas Wind Systems) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Date 2023 Abstract Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set. Subject Control of renewable energy resourcesData-driven controlLearning for controlNonlinear adaptive controlNonlinear observers To reference this document use: http://resolver.tudelft.nl/uuid:44984bc1-485b-4dde-8daa-68a78e231c03 DOI https://doi.org/10.1016/j.ifacol.2023.10.1035 Source IFAC-PapersOnLine, 56 (2), 8406-8413 Event 22nd IFAC World Congress, 2023-07-09 → 2023-07-14, Yokohama, Japan Part of collection Institutional Repository Document type journal article Rights © 2023 S.P. Mulders, Y. Liu, Fabio Spagnolo, P.B. Christensen, J.W. van Wingerden Files PDF 1-s2.0-S2405896323014180-main.pdf 872.12 KB Close viewer /islandora/object/uuid:44984bc1-485b-4dde-8daa-68a78e231c03/datastream/OBJ/view