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| 2. |
- Barthelmie, Rebecca, et al.
(författare)
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ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions
- 2004
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Ingår i: Wind Energy. - : Wiley. - 1095-4244 .- 1099-1824. ; 7, s. 225-245
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Tidskriftsartikel (refereegranskat)abstract
- While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional-scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers.
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| 3. |
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| 4. |
- Barthelmie, Rebecca, et al.
(författare)
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ENDOW: Efficient Development of Offshore Windfarms
- 2003
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Ingår i: Proceedings from Offshore Wind Energy in Meditteranean and Other Seas OWEMES2003, Naples, Italy, 10-12 April 2003.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 5. |
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| 6. |
- Hallgren, Christoffer, et al.
(författare)
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Brief communication : On the definition of the low-level jet
- 2023
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Ingår i: Wind Energy Science. - : Copernicus Publications. - 2366-7443 .- 2366-7451. ; 8:11, s. 1651-1658
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Tidskriftsartikel (refereegranskat)abstract
- Low-level jets (LLJs) are examples of non-logarithmic wind speed profiles affecting wind turbine power production, wake recovery, and structural/aerodynamic loading. However, there is no consensus regarding which definition should be applied for jet identification. In this study we argue that a shear definition is more relevant to wind energy than a falloff definition. The shear definition is demonstrated and validated through the development of a European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) LLJ climatology for six sites. Identification of LLJs and their morphology, frequency, and intensity is critically dependent on the (i) vertical window of data from which LLJs are extracted and (ii) the definition employed.
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| 7. |
- Hallgren, Christoffer, et al.
(författare)
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Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
- 2024
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Ingår i: Wind Energy Science. - : Copernicus Publications. - 2366-7443 .- 2366-7451. ; 9:4, s. 821-840
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Tidskriftsartikel (refereegranskat)abstract
- Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.
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