Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Yoshiaki Sakagami (1) F P Pedro Santos (2) Reinaldo Haas (2) Julio Passos (2) Frederico Freitas (3)
(1) IFSC, Florianopolis, Brazil (2) UFSC, Florianópolis, Brazil (3) Tractebel GDF SUEZ, Florianópolis, Brazil
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Presenter's biographyBiographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited
Yoshiaki Sakagami received his Bsc degree in meteorology at University of Sao Paulo, and Master in mechanical engineering from the Department of Mechanical Engineering at University of Santa Catarina State, Brazil. Mr. Yoshiaki is professor at Federal Institute of Santa Catarina State, PhD student at Federal University of Santa Catarina State, and has long experience with field experiments. His expertise is mainly in meteorological instruments and wind energy. His present work focuses on wind energy and he is concerned with the effects of atmospheric stability and turbulence in the wind turbine power performance.
A simple method to estimate atmospheric stability using lidar wind profiler
The wind energy industry has increased the power capacity by developing tall wind turbines reaching up to 200m height. As consequence, the effects of wind shear can be significant on power performance, turbine loads and noise at this height. Therefore, the study atmospheric stability is necessary to understand the variation of wind shear along the large rotor. The atmospheric stability is not an easy task to determine, because the high frequency measurements are required, and most of wind farms do not have it available. This paper proposes a simple method to estimate the atmospheric stability using only measurements by LiDAR.
The method is based on Monin-Obukhov similarity theory (MOST) , where the dimensionless shear parameter (Φm) increases with the height (z) in stable condition, decreases with the height in unstable condition and it is constant in neutral condition. Assuming that Φm and wind shear coefficient from power law (α) have similar dependence; it is possible to estimate the atmospheric stability considering only the variation of wind shear with the height.
Main body of abstract
This study uses one-year LiDAR data obtained in Pedra do Sal Wind Farm from September 2013 to August 2014. The site is onshore, only 500m away from shoreline and located in the northeast coast of Brazil. The wind farm has 20 wind turbines with 55m hub height, 18MW of total power installed, and aligned with the coast (northwest-southeast). The LiDAR, model windcub8, is installed about 150m upwind from wind turbines array and is configured to measure every 20m from 40m to 500m height, and to process 10-min average data for each level. The trade winds and sea breezes strongly influence the local winds, and the fetch is typically marine as 92.7% of wind blows from the ocean. The method is applied in Pedra do Sal using data from LiDAR and the results show that the site is dominated by near-neutral condition with 46.2%. The unstable and stable conditions have 18.4% and 12.2% respectively, and the extreme conditions have 8.1% and 15.1% for very unstable and very stable condition respectively. On the summer season (south hemisphere), the extreme conditions (very stable and very unstable) is more often than in the winter. In the diurnal cycle, the condition is more stable in the morning (8h) and more unstable in the afternoon and early night (16h to 23h).
A simple method to estimate the atmospheric stability is proposed using only data from LiDAR. It has an advantage to be easy to implement when compare with traditional method by Obukhov length (L). The results of atmospheric stability in Pedra do Sal, using this method, show good agreement with preview studies in marine condition. A comparison with the other methods and different sites are still necessary to validate it.
 A. S. Monin, A. M. Obukhov, Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geofiz. Inst. 24 (1954).
-Understand the atmoshperic stablity and how wind shear change with the heigth.
-Estimate the atmospheric stability using a simple method and LiDAR data
-Understand the stability condition on diurnal and seasonal cycle.