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Performance of a Wind-Profiling Lidar in the Region of Wind Turbine Rotor Disks
Author(s) -
Matthew L. Aitken,
Michael E. Rhodes,
Julie K. Lundquist
Publication year - 2012
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech-d-11-00033.1
Subject(s) - lidar , ceilometer , environmental science , remote sensing , wind speed , meteorology , turbine , wind power , observatory , geology , aerospace engineering , physics , engineering , electrical engineering , astrophysics
As the wind energy sector continues to grow, so does the need for reliable vertical wind profiles in the assessment of wind resources and turbine performance. In situ instrumentation mounted on meteorological towers can rarely probe the atmosphere across the full span of modern turbine rotor disks, which typically extend from 40 to 120 m above the surface. However, by measuring the Doppler shift of laser light backscattered by particles in the atmosphere, remote sensing lidar is capable of estimating wind speeds and turbulence at several altitudes in this range and above. Consequently, lidar has proven a promising technology for both wind resource assessment and turbine response characterization. The aim of this study is to quantify data availability for a coherent detection wind-profiling lidar—namely, the Leosphere Windcube. To determine situations of suitable data return rates, a Windcube, collocated with a Vaisala CL31 ceilometer, was deployed as part of the Skywatch Observatory at the University of Colorado at Boulder. Aerosol backscatter, as measured by the ceilometer, and lidar carrier-to-noise ratio (CNR) are strongly correlated. Additionally, lidar CNR was found to depend on atmospheric turbulence characteristics and relative humidity in another deployment at a location in the United States Great Plains. These relationships suggest an ability to predict lidar performance based on widely available air quality assessments (such as PM2.5 concentration) and other climatic conditions, thus providing guidance for determining the utility of lidar deployments at wind farms to characterize turbine performance.

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