Wednesday, 26 January 2011
Matthew L. Aitken, Univ. of Colorado, Boulder, CO; and M. E. Rhodes and J. K. Lundquist
As the wind energy sector continues to grow, so does the need for reliable vertical wind profiles for assessing wind turbine performance and diagnosing underperformance issues. In situ instrumentation mounted on meteorological towers can rarely probe the atmosphere at the altitudes of modern turbine rotor disks, up to 200 m above the surface. Remote sensing LIDAR, on the other hand, can quantify winds and turbulence at altitudes throughout the ranges of modern turbine rotor disks (40 m to 200 m above the surface). By measuring the Doppler shift of laser light backscattered by particles in the atmosphere, LIDAR has proven a promising technology for both wind resource assessment and turbine response characterization; to date, however, LIDAR data availability has not been well-quantified.
To determine situations of suitable data return rates, we have deployed a Windcube LIDAR, co-located with a Vaisala CL31 ceilometer, as part of the Skywatch Observatory at the University of Colorado at Boulder. Aerosol backscatter, as measured by the ceilometer, and LIDAR signal-to-noise ratio (SNR) are strongly correlated. Additionally, we find that LIDAR SNR also depends weakly on atmospheric turbulence characteristics and atmospheric relative humidity. This relationship suggests an ability to predict LIDAR performance based on widely available air quality assessments (such as the EPA Air Quality Index), thus providing guidance for useful LIDAR deployments at wind farms to characterize turbine performance.
*Acknowledgments: Skywatch Observatory is funded through NSF grant 0837388.
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