A model predictive wind farm controller with linear parameter-varying models

Sjoerd Boersma, Vahab Rostampour, Bart Doekemeijer, Jan-Willem van Wingerden, Tamás Keviczky

Research output: Contribution to journalConference articleScientificpeer-review

2 Citations (Scopus)
91 Downloads (Pure)


In this paper, we present an implementation of a model predictive controller (MPC) for wind farm power tracking problem. The controller is evaluated in the high-fidelity PAral-lelized Large-eddy simulation Model (PALM). By taking measurements from PALM, we show that the closed-loop MPC can provide power reference tracking while reducing force variations on a farm level by solving a constrained optimization problem at each time step. A six turbine wind farm case study is presented in which the controller operates with yawed turbines that increases the potential power that can be harvested with the wind farm, and we show that it is possible to track a reference power signal that temporarily exceeds the power harvested when operating under the so-called greedy control settings.

Original languageEnglish
Pages (from-to)241-246
Issue number20
Publication statusPublished - 2018
EventNMPC 2018: 6th IFAC Conference on Nonlinear Model Predictive Control - Madison, United States
Duration: 19 Aug 201822 Aug 2018


  • Large-eddy simulations
  • Model Predictive Control
  • Wind farm


Dive into the research topics of 'A model predictive wind farm controller with linear parameter-varying models'. Together they form a unique fingerprint.

Cite this