Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm
Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed cali...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/5/1293 |
| _version_ | 1827588155509833728 |
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| author | Maarten T. van Beek Axelle Viré Søren J. Andersen |
| author_facet | Maarten T. van Beek Axelle Viré Søren J. Andersen |
| author_sort | Maarten T. van Beek |
| collection | DOAJ |
| description | Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%. |
| first_indexed | 2024-03-09T00:29:20Z |
| format | Article |
| id | doaj.art-590741e327b545689aa521231cc4a987 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:29:20Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-590741e327b545689aa521231cc4a9872023-12-11T18:36:10ZengMDPI AGEnergies1996-10732021-02-01145129310.3390/en14051293Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind FarmMaarten T. van Beek0Axelle Viré1Søren J. Andersen2Department of Wind Energy, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsDepartment of Wind Energy, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsDepartment of Wind Energy, Technological University of Denmark, 2800 Kgs. Lynbgy, DenmarkWind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.https://www.mdpi.com/1996-1073/14/5/1293wind farm controlwake steeringLillgrundsensitivity analysisuncertainty analysis |
| spellingShingle | Maarten T. van Beek Axelle Viré Søren J. Andersen Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm Energies wind farm control wake steering Lillgrund sensitivity analysis uncertainty analysis |
| title | Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm |
| title_full | Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm |
| title_fullStr | Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm |
| title_full_unstemmed | Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm |
| title_short | Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm |
| title_sort | sensitivity and uncertainty of the floris model applied on the lillgrund wind farm |
| topic | wind farm control wake steering Lillgrund sensitivity analysis uncertainty analysis |
| url | https://www.mdpi.com/1996-1073/14/5/1293 |
| work_keys_str_mv | AT maartentvanbeek sensitivityanduncertaintyoftheflorismodelappliedonthelillgrundwindfarm AT axellevire sensitivityanduncertaintyoftheflorismodelappliedonthelillgrundwindfarm AT sørenjandersen sensitivityanduncertaintyoftheflorismodelappliedonthelillgrundwindfarm |