Article
Experimental Testing of a Preview-enabled Model Predictive Controller for Blade Pitch Control of Wind Turbines Public Deposited
https://scholar.colorado.edu/concern/articles/ht24wk89b
- Abstract
- Model predictive control (MPC) is a control method that involves determining the input to a dynamical system as the solution to an optimization problem that is solved online. In the wind turbine research literature, MPC has received considerable attention for its ability to handle both actuator constraints and preview disturbance information about the oncoming wind, which can be provided by a lidar scanner. However, while many studies simulate the wind turbine response under MPC, very few physical tests have been carried out, likely due in part to the difficulties associated with solving the MPC problem in real time. In this work, we implement MPC on an experimental, scaled wind turbine operating in a wind tunnel testbed, using an active grid to create reproducible wind sequences and a hot-wire anemometer to generate upstream wind measurements. To our knowledge, this work presents the first physical test of MPC for blade pitch control of a scaled wind turbine. We compare two MPC strategies: one including preview disturbance information and one without. Our results provide further evidence that feedforward control can improve wind turbine performance in transition and above-rated conditions without increasing actuation requirements, which we hope will encourage industry experimentation and uptake of feedforward control methods. We also provide a high-level analysis and interpretation of the computational performance of the chosen approach. This work builds upon the results of an earlier study, which considered unconstrained optimal blade pitch control.
- Creator
- Date Issued
- 2022
- Academic Affiliation
- Journal Title
- Journal Issue/Number
- 2
- Journal Volume
- 30
- Last Modified
- 2024-07-17
- Resource Type
- Rights Statement
- DOI
- ISSN
- 1558-0865
- Language
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SinnerEtAl-PreviewEnabledMPC-Experiments-IEEE-TCST2021-SelfGeneratedFile-Jun2024.pdf | 2024-07-17 | Public | Download |