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Operation assessment of analytical wind turbine wake models
https://orcid.org/0000-0002-6852-5394
Abstract Wind turbine wakes have a strong impact on wind farms as they affect the power output and the turbulence level. These factors have a determinant impact on turbines lifetime. Thus, wake modelling is of critical importance to the wind energy industry, playing a central role in the optimization of wind farm layouts. This work aims at assessing some of the available analytical wake models that modify the computed wind field by CFD as a post-processing correction tool. Such validation was done with recourse to experimental SCADA data obtained in an onshore wind farm with eight wind turbines distributed by two rows. Conclusions were drawn for the Jensen, Jensen2D, Larsen, Gaussian BPA and Gaussian Ishihara models, by analyzing the computed velocity ratio relative to the upstream leading turbine, both in a single wake and in multiple wake situations.
Highlights SCADA data from an onshore wind farm were used for testing 5 analytical wake models. Unknown atmospheric stability and temporal lag between turbines add uncertainty. Averaging data in 1° sectors allowed reduction of experimental data scattering. The Gaussian models showed the best performance.
Operation assessment of analytical wind turbine wake models
https://orcid.org/0000-0002-6852-5394
Abstract Wind turbine wakes have a strong impact on wind farms as they affect the power output and the turbulence level. These factors have a determinant impact on turbines lifetime. Thus, wake modelling is of critical importance to the wind energy industry, playing a central role in the optimization of wind farm layouts. This work aims at assessing some of the available analytical wake models that modify the computed wind field by CFD as a post-processing correction tool. Such validation was done with recourse to experimental SCADA data obtained in an onshore wind farm with eight wind turbines distributed by two rows. Conclusions were drawn for the Jensen, Jensen2D, Larsen, Gaussian BPA and Gaussian Ishihara models, by analyzing the computed velocity ratio relative to the upstream leading turbine, both in a single wake and in multiple wake situations.
Highlights SCADA data from an onshore wind farm were used for testing 5 analytical wake models. Unknown atmospheric stability and temporal lag between turbines add uncertainty. Averaging data in 1° sectors allowed reduction of experimental data scattering. The Gaussian models showed the best performance.
Operation assessment of analytical wind turbine wake models
https://orcid.org/0000-0002-6852-5394
Abstract Wind turbine wakes have a strong impact on wind farms as they affect the power output and the turbulence level. These factors have a determinant impact on turbines lifetime. Thus, wake modelling is of critical importance to the wind energy industry, playing a central role in the optimization of wind farm layouts. This work aims at assessing some of the available analytical wake models that modify the computed wind field by CFD as a post-processing correction tool. Such validation was done with recourse to experimental SCADA data obtained in an onshore wind farm with eight wind turbines distributed by two rows. Conclusions were drawn for the Jensen, Jensen2D, Larsen, Gaussian BPA and Gaussian Ishihara models, by analyzing the computed velocity ratio relative to the upstream leading turbine, both in a single wake and in multiple wake situations.
Highlights SCADA data from an onshore wind farm were used for testing 5 analytical wake models. Unknown atmospheric stability and temporal lag between turbines add uncertainty. Averaging data in 1° sectors allowed reduction of experimental data scattering. The Gaussian models showed the best performance.
Operation assessment of analytical wind turbine wake models
Lopes, António M.G. (author) / Vicente, António H.S.N. (author) / Sánchez, Omar H. (author) / Daus, Regina (author) / Koch, Herbert (author)
2021-11-11
Article (Journal)
Electronic Resource
English
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