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Title: A New Framework for Quantifying Lidar Uncertainty

Abstract

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. Inmore » this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.« less

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1349214
Report Number(s):
NREL/PR-5000-67978
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the Eighth Conference on Weather, Climate, Water and the New Energy Economy, 22-26 January 2017, Seattle, Washington
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; remote sensing; lidar; uncertainty; wind energy; wind resource assessment; wind turbine; performance testing

Citation Formats

Newman, Jennifer, F., Clifton, Andrew, Bonin, Timothy A., and Churchfield, Matthew J. A New Framework for Quantifying Lidar Uncertainty. United States: N. p., 2017. Web.
Newman, Jennifer, F., Clifton, Andrew, Bonin, Timothy A., & Churchfield, Matthew J. A New Framework for Quantifying Lidar Uncertainty. United States.
Newman, Jennifer, F., Clifton, Andrew, Bonin, Timothy A., and Churchfield, Matthew J. Fri . "A New Framework for Quantifying Lidar Uncertainty". United States. doi:. https://www.osti.gov/servlets/purl/1349214.
@article{osti_1349214,
title = {A New Framework for Quantifying Lidar Uncertainty},
author = {Newman, Jennifer, F. and Clifton, Andrew and Bonin, Timothy A. and Churchfield, Matthew J.},
abstractNote = {As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 24 00:00:00 EDT 2017},
month = {Fri Mar 24 00:00:00 EDT 2017}
}

Conference:
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