Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-8419
Authors: Schlipf, David
Fleming, Paul
Raach, Steffen
Scholbrock, Andrew
Haizmann, Florian
Krishnamurthy, Raghu
Boquet, Matthieu
Cheng, Po Wen
Title: An adaptive data processing technique for lidar-assisted control to bridge the gap between lidar systems and wind turbines
Issue Date: 2015
metadata.ubs.publikation.typ: Konferenzbeitrag
metadata.ubs.publikation.source: European Wind Energy Association Annual Event (EWEA), Paris, France, November 2015
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-103950
http://elib.uni-stuttgart.de/handle/11682/8436
http://dx.doi.org/10.18419/opus-8419
Abstract: This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control.
Appears in Collections:15 Fakultätsübergreifend / Sonstige Einrichtung



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