Model-based closed-loop wind farm control for power maximization using Bayesian optimization

A large eddy simulation study

Conference paper (2019)

Authors

B.M. Doekemeijer Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
D.C. van der Hoek Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 B.M. Doekemeijer, D.C. van der Hoek, J.W. van Wingerden
DOI: https://doi.org/10.1109/CCTA.2019.8920587
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Published Date

2019

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

Modern wind farm control (WFC) methods in the literature typically rely on a surrogate model of the farm dynamics that is computationally inexpensive to enable real-time computations. As it is very difficult to model all the relevant wind farm dynamics accurately, a closed-loop approach is a prerequisite for reliable WFC. As one of the few in its field, this paper showcases a closed-loop wind farm control solution, which leverages a steady-state surrogate model and Bayesian optimization to maximize the wind-farm-wide power production. The estimated quantities are the time-averaged ambient wind direction, wind speed and turbulence intensity. This solution is evaluated for a wind farm with nine 10 MW wind turbines in large-eddy simulation, showing a time-averaged power gain of 4.4%. This is the first WFC algorithm that is tested for wind turbines of such scale in high fidelity.

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