Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

Conference paper (2023)

Authors

R.T.O. Dinkla Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Tom Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering , Eindhoven University of Technology
J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
S.P. Mulders Team Mulders - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 R.T.O. Dinkla, T.A.E. Oomen, J.W. van Wingerden, S.P. Mulders
DOI: https://doi.org/10.1109/CCTA54093.2023.10252439
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.

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