Improving Lidar Turbulence Estimates for Wind Energy

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; Klein, Petra

doi:10.1088/1742-6596/753/7/072010

Title: Improving Lidar Turbulence Estimates for Wind Energy

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Abstract

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.

Authors:

Newman, Jennifer F. ^[1]; Clifton, Andrew ^[1]; Churchfield, Matthew J. ^[1]; Klein, Petra ^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Oklahoma, Norman, OK (United States). School of Meteorology

Publication Date:: Mon Oct 03 00:00:00 EDT 2016

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

OSTI Identifier:: 1335579

Report Number(s):: NREL/JA-5000-67547
Journal ID: ISSN 1742-6588

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Physics. Conference Series

Additional Journal Information:: Journal Volume: 753; Journal ID: ISSN 1742-6588

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Subject:: 17 WIND ENERGY; lidar; turbulence; wind energy; L-TERRA; turbulence intensity

Citation Formats


                    Newman, Jennifer F., Clifton, Andrew, Churchfield, Matthew J., and Klein, Petra. Improving Lidar Turbulence Estimates for Wind Energy.  United States: N. p., 2016. 
Web.  doi:10.1088/1742-6596/753/7/072010.

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                    Newman, Jennifer F., Clifton, Andrew, Churchfield, Matthew J., & Klein, Petra. Improving Lidar Turbulence Estimates for Wind Energy.  United States.  https://doi.org/10.1088/1742-6596/753/7/072010

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                    Newman, Jennifer F., Clifton, Andrew, Churchfield, Matthew J., and Klein, Petra. Mon .  
"Improving Lidar Turbulence Estimates for Wind Energy".  United States.  https://doi.org/10.1088/1742-6596/753/7/072010.  https://www.osti.gov/servlets/purl/1335579.

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@article{osti_1335579,

  title        = {Improving Lidar Turbulence Estimates for Wind Energy},

  author       = {Newman, Jennifer F. and Clifton, Andrew and Churchfield, Matthew J. and Klein, Petra},

  abstractNote = {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.},

  doi          = {10.1088/1742-6596/753/7/072010},

  journal      = {Journal of Physics. Conference Series},

  number       = ,

  volume       = 753,

  place        = {United States},

  year         = {Mon Oct 03 00:00:00 EDT 2016},

  month        = {Mon Oct 03 00:00:00 EDT 2016}

}

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