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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Remote sensing: From toys to tools?' taking place on Wednesday, 12 March 2014 at 14:15-15:45. The meet-the-authors will take place in the poster area.

Florian Haizmann University of Stuttgart, Germany
Florian Haizmann (1) F P David Schlipf (1) Nicolai Cosack (2) Dennis Neuhaus (2) Steffen Raach (1) Timo Maul (1) Po Wen Cheng (1)
(1) University of Stuttgart, Stuttgart, Germany (2) KENERSYS GmbH, Münster, Germany

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Mr. Haizmann is working toward his PhD degree at the Stuttgart Chair of Wind Energy (SWE) at the University of Stuttgart with the focus on advanced LiDAR assisted control of wind turbines. He studied Engineering Cybernetics (control engineering) at the University of Stuttgart. He spent three months during his diploma thesis at the National Renewable Energy Laboratory’s (NREL) National Wind Technology Center (NWTC) in Colorado working on field-testing of LiDAR assisted control.


Field testing of lidar assisted feed-forward control on a large commercial wind turbine


The successful field testing of a LiDAR (Light Detecting and Ranging) assisted feed-forward collective pitch controller on two smaller sized research turbines at the National Renewable Energy Laboratory in 2012 showed a promising proof-of-concept. This work presents the next step in transforming LiDAR systems from academic toys to engineering tools with high potential for advanced control of wind turbines: SWE together with KENERSYS implemented and successfully tested the SWE feed-forward controller on a large commercial 2.4MW K110 wind turbine in northern Germany using a research LiDAR system.


The testing campaign involves the setup of a software-in-the-loop testing environment in order to thoroughly test the communication interface between the turbine’s control system and the feed-forward controller. This is necessary to ensure the safety of the turbine during operation. It is followed by the installation of the scanning LiDAR system on the nacelle and the implementation of a separated feed-forward controller that communicates with the turbine’s control system. Finally, the LIDAR assisted control is tested in a real operating environment of a large, commercial wind turbine.

Main body of abstract

The integration of the feed-forward controller is realized on a gateway computer which establishes the connection between the LiDAR and the turbine’s control system. On this gateway the signals from the LiDAR are fed into the actual feed-forward controller DLL performing the preprocessing of the LiDAR raw signals and calculating a pitch rate update signal, which is then sent to the turbine’s control system. The use of a pitch rate update allows this signal to be added simply before the integrator in the existing feedback controller without the need of any further changes and without running into stability problems. It is calculated by the feed-forward controller from the wind turbine’s inverted pitch curve after filtering the rotor effective wind speed measured by the LiDAR system.
In order to determine the correct parameters for this low-pass filter, the correlation between the rotor effective wind speed measured by the LiDAR system and its estimate from SCADA turbine signals is analyzed at the beginning of the testing campaign. The nonlinear estimator is designed based on basic turbine information such as rotor inertia and power coefficients.
The rotor effective wind speed is an average over the points of a circular trajectory of the scanning LiDAR system and an adaptive combination over several measurement distances in front of the rotor depending on the mean wind speed. By improving this circular trajectory with regards to the scanning strategy, it was possible to increase its temporal resolution and thereby increase the correlation.


The first main achievement of this field testing campaign is the successful set-up and utilization of a software-in-the-loop test environment for the feed-forward controller. Furthermore, the successful integration of a LiDAR system together with a tailored online data preprocessing and an add-on adaptive feed-forward controller on a commercial multi-megawatt wind turbine has been accomplished. No structural changes in the existing feedback controller are required and a stable communication between the two systems has been implemented. The results showed that improvements of the speed regulation can be achieved in the expected frequency range.

Learning objectives
The encouraging result of the current work shows that there is a large potential to reduce the fatigue loads using the LiDAR assisted control. Further cooperation between the turbine and LiDAR manufacturers is necessary in order to optimize the systems and to make them commercially viable. The results confirmed the viability of the LiDAR assisted control using a commercial multi-megawatt wind turbine under real operating conditions.