Improving lidar turbulence estimates for wind energy

The Science of Making Torque from Wind

Newman, J.F.^1 ; Clifton, A.^1 ; Churchfield, M.J.^1 ; Klein, P.^2

National Renewable Energy Laboratory, Golden

CO, United States^1

School of Meteorology, University of Oklahoma, Norman

OK, United States^2

关键词: Energy applications;    Error reduction;    Lidar measurements;    Machine learning techniques;    Mean wind speed;    Meteorological tower;    Turbulence intensity;    Unstable conditions;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/753/7/072010/pdf
DOI  :  10.1088/1742-6596/753/7/072010

来源: IOP

【 摘 要 】

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.