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 MW wind turbine operating in free-, partial-, an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-08-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/5/1129/2020/wes-5-1129-2020.pdf |
| _version_ | 1818851124813234176 |
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| author | D. Conti N. Dimitrov A. Peña |
| author_facet | D. Conti N. Dimitrov A. Peña |
| author_sort | D. Conti |
| collection | DOAJ |
| 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 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 %–11 %). 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> |
| first_indexed | 2024-12-19T07:00:03Z |
| format | Article |
| id | doaj.art-691b2e680ed54b779e0931d2cebcce77 |
| institution | Directory Open Access Journal |
| issn | 2366-7443 2366-7451 |
| language | English |
| last_indexed | 2024-12-19T07:00:03Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj.art-691b2e680ed54b779e0931d2cebcce772022-12-21T20:31:26ZengCopernicus 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 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 %–11 %). 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 |
| spellingShingle | D. Conti N. Dimitrov A. Peña Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements Wind Energy Science |
| title | 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_short | Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements |
| title_sort | aeroelastic load validation in wake conditions using nacelle mounted lidar measurements |
| url | https://wes.copernicus.org/articles/5/1129/2020/wes-5-1129-2020.pdf |
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