Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-8173
Authors: Schlipf, David
Grau, Patrick
Raach, Steffen
Duraiski, Ricardo
Trierweiler, Jorge
Cheng, Po Wen
Title: Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR
Issue Date: 2014
metadata.ubs.publikation.typ: Konferenzbeitrag
metadata.ubs.publikation.source: Proceedings of the American Control Conference, Portland, USA, 2014
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-91217
http://elib.uni-stuttgart.de/handle/11682/8190
http://dx.doi.org/10.18419/opus-8173
Abstract: Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed.
Appears in Collections:15 Fakultätsübergreifend / Sonstige Einrichtung



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