Conference programme

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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Samuel Davoust General Electric , Germany
Samuel Davoust (1) F Dale Mashtare (2) T Stephen Markham (3) Conner Shane (3) Thomas Velociter (4) Raghavendra Krishna Murthy (4)
(1) GRC General Electric, Garching, Germany (2) General Electric Renewables, Greenville, Afghanistan (3) GRC General Electric, NIskayuna, United States of America (4) Avent Lidar Technology , Orsay, France

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Dr. Samuel Davoust is a Research Engineer in the Aerodynamics and Acoustics Lab at the General Electric Global Research Center.


Poster Download poster (10.58 MB)


Evaluation of LIDAR Performance for Practical Turbine Control Implementation


Wind field prediction ahead of a single wind turbine has been proven to provide preview time for pre-emptive control [1]. Thus, integrating an upwind facing Lidar in turbine control algorithms has the potential to improve the turbine performance and reliability [2].


A pulsed Lidar is placed on top of a wind turbine and the measurements from the Lidar are integrated into the turbine controller. This paper describes design considerations and field analysis results derived from turbine tests occurring in differing environments over periods of several months.

Main body of abstract

Predicting the wind field measurement with a turbine mounted LIDAR for use in real-time control systems can provide substantial loads reduction and AEP benefits. However, several practical challenges must be solved to insure a robust sensing capability is provided for turbine platform implementation [3]. First, LIDAR signal availability must be greater than 99% for continual control operation, and impacting factors, such as atmospheric conditions and turbine interactions must be overcome. Second, the reliability of the LIDAR sensor wind outputs including predictive wind models is critical as the control system employs this predictive wind information to preemptively act to mitigate loads and improve turbine performance. Various design considerations for optimal Lidar operation for turbine control along with field test results will be shown.

[1]: Koerber, A., & King, R. (2011). Nonlinear model predictive control for wind turbines.
[2]: Schlipf, D., et. al. (2014). Field testing of feedforward collective pitch control on the CART2 using a nacelle-based lidar scanner.
[3]: Davoust, S. et. al. Assessment and optimization of Lidar measurement availability for wind turbine control.


During the field tests, the Lidar system achieved encouraging measurement performance with respect to the application. These results warrant further work on of the use of Lidar for wind turbine control.

Learning objectives
1. Insights into practical Lidar field testing
2. Key Lidar parameters for turbine control