Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition
Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2681 |
| _version_ | 1797491847502757888 |
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| author | Anantha Padmanabhan Kidambi Sekar Marijn Floris van Dooren Andreas Rott Martin Kühn |
| author_facet | Anantha Padmanabhan Kidambi Sekar Marijn Floris van Dooren Andreas Rott Martin Kühn |
| author_sort | Anantha Padmanabhan Kidambi Sekar |
| collection | DOAJ |
| description | Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems. |
| first_indexed | 2024-03-10T00:55:09Z |
| format | Article |
| id | doaj.art-cc7b6142cb0544949fbc93910f5dddd5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:55:09Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cc7b6142cb0544949fbc93910f5dddd52023-11-23T14:45:42ZengMDPI AGRemote Sensing2072-42922022-06-011411268110.3390/rs14112681Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal DecompositionAnantha Padmanabhan Kidambi Sekar0Marijn Floris van Dooren1Andreas Rott2Martin Kühn3ForWind, Institue of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyForWind, Institue of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyForWind, Institue of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyForWind, Institue of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, GermanyPreview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems.https://www.mdpi.com/2072-4292/14/11/2681SpinnerLidarinflow reconstructionreduced-order modellingblade interferenceGappy-POD |
| spellingShingle | Anantha Padmanabhan Kidambi Sekar Marijn Floris van Dooren Andreas Rott Martin Kühn Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition Remote Sensing SpinnerLidar inflow reconstruction reduced-order modelling blade interference Gappy-POD |
| title | Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition |
| title_full | Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition |
| title_fullStr | Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition |
| title_full_unstemmed | Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition |
| title_short | Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition |
| title_sort | lower order description and reconstruction of sparse scanning lidar measurements of wind turbine inflow using proper orthogonal decomposition |
| topic | SpinnerLidar inflow reconstruction reduced-order modelling blade interference Gappy-POD |
| url | https://www.mdpi.com/2072-4292/14/11/2681 |
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