Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

<p>We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3&thinsp;MW wind turbine operating in free-, partial-, an...

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Main Authors: D. Conti, N. Dimitrov, A. Peña
Format: Article
Language:English
Published: Copernicus Publications 2020-08-01
Series:Wind Energy Science
Online Access:https://wes.copernicus.org/articles/5/1129/2020/wes-5-1129-2020.pdf
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spelling doaj-691b2e680ed54b779e0931d2cebcce772020-11-25T03:39:30ZengCopernicus PublicationsWind Energy Science2366-74432366-74512020-08-0151129115410.5194/wes-5-1129-2020Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurementsD. ContiN. DimitrovA. Peña<p>We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3&thinsp;MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4&thinsp;%–11&thinsp;%). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.</p>https://wes.copernicus.org/articles/5/1129/2020/wes-5-1129-2020.pdf
collection DOAJ
language English
format Article
sources DOAJ
author D. Conti
N. Dimitrov
A. Peña
spellingShingle D. Conti
N. Dimitrov
A. Peña
Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
Wind Energy Science
author_facet D. Conti
N. Dimitrov
A. Peña
author_sort D. Conti
title Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
title_short Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
title_full Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
title_fullStr Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
title_full_unstemmed Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
title_sort aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
publisher Copernicus Publications
series Wind Energy Science
issn 2366-7443
2366-7451
publishDate 2020-08-01
description <p>We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3&thinsp;MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4&thinsp;%–11&thinsp;%). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.</p>
url https://wes.copernicus.org/articles/5/1129/2020/wes-5-1129-2020.pdf
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