Scanning Doppler Lidar for Input into Short-Term Wind Power Forecasts

Abstract

canning Doppler lidar is a promising technology for improvements in short-term wind power forecasts since it can scan close to the surface and produce wind profiles at a large distance upstream (15?30 km) if the atmosphere has sufficient aerosol loading and there are no sizable blockages from terrain or large structures. However, successful measurements require a large spatial sampling domain and new estimation algorithms that can perform well in the very weak signal regime. The maximum likelihood (ML) algorithm in the spectral domain and a faster version based on the minimum mean-square-error (MSE) are investigated by numerical simulation and with actual scanning Doppler lidar data from the Lockheed Martin Coherent Technologies WindTracer lidar. In addition, the maximum range can be extended by simultaneous estimation of the wind speed and wind direction from a larger azimuth sector scan if the atmosphere is well behaved. Real-time operation is possible using the spectral data from the WindTracer lidar and a dedicated computer to interface with a data assimilation system. Analysis of the Doppler lidar data in the first few kilometers can be used to extract the turbulence conditions for improvements in real-time wind farm operations.