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Five Megawatt Wind Turbine Power Output Improvements by Passive Flow Control Devices

Author

Listed:
  • Unai Fernandez-Gamiz

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Ana Boyano

    (Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Igor Ansoategui

    (Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

  • Irantzu Uriarte

    (Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

The effects of two types of flow control devices, vortex generators (VGs) and Gurney flaps (GFs), on the power output performance of a multi-megawatt horizontal axis wind turbine is presented. To that end, an improved blade element momentum (BEM)-based solver has been developed and BEM-based computations have been carried out on the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine. The results obtained from the clean wind turbine are compared with the ones obtained from the wind turbine equipped with the flow control devices. A significant increase in the average wind turbine power output has been found for all of the flow control device configurations and for the wind speed realizations studied in the present work. Furthermore, a best configuration case is proposed which has the largest increase of the average power output. In that case, increments on the average power output of 10.4% and 3.5% have been found at two different wind speed realizations. The thrust force and bending moment in the root of the blade have also been determined and compared with the values of the clean wind turbine. A residual increase in the bending moment of less than 1% has been found.

Suggested Citation

  • Unai Fernandez-Gamiz & Ekaitz Zulueta & Ana Boyano & Igor Ansoategui & Irantzu Uriarte, 2017. "Five Megawatt Wind Turbine Power Output Improvements by Passive Flow Control Devices," Energies, MDPI, vol. 10(6), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:742-:d:99482
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    References listed on IDEAS

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    1. He-Yong Xu & Chen-Liang Qiao & Zheng-Yin Ye, 2016. "Dynamic Stall Control on the Wind Turbine Airfoil via a Co-Flow Jet," Energies, MDPI, vol. 9(6), pages 1-25, June.
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    Cited by:

    1. Shunlei Zhang & Xudong Yang & Bifeng Song, 2021. "Numerical Investigation of Performance Enhancement of the S809 Airfoil and Phase VI Wind Turbine Blade Using Co-Flow Jet Technology," Energies, MDPI, vol. 14(21), pages 1-20, October.
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    3. Unai Fernandez-Gamiz & Macarena Gomez-Mármol & Tomas Chacón-Rebollo, 2018. "Computational Modeling of Gurney Flaps and Microtabs by POD Method," Energies, MDPI, vol. 11(8), pages 1-19, August.
    4. Aitor Saenz-Aguirre & Unai Fernandez-Gamiz & Ekaitz Zulueta & Alain Ulazia & Jon Martinez-Rico, 2019. "Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    5. Ruben Gutierrez-Amo & Unai Fernandez-Gamiz & Iñigo Errasti & Ekaitz Zulueta, 2018. "Computational Modelling of Three Different Sub-Boundary Layer Vortex Generators on a Flat Plate," Energies, MDPI, vol. 11(11), pages 1-21, November.
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    7. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
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    10. Cemil Yigit, 2020. "Effect of Air-Ducted Blade Design on Horizontal Axis Wind Turbine Performance," Energies, MDPI, vol. 13(14), pages 1-15, July.
    11. Matthias Schramm & Hamid Rahimi & Bernhard Stoevesandt & Kim Tangager, 2017. "The Influence of Eroded Blades on Wind Turbine Performance Using Numerical Simulations," Energies, MDPI, vol. 10(9), pages 1-15, September.
    12. Jonas Kazda & Nicolaos Antonio Cutululis, 2018. "Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation," Energies, MDPI, vol. 11(12), pages 1-19, November.
    13. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    14. Md Zishan Akhter & Farag Khalifa Omar, 2021. "Review of Flow-Control Devices for Wind-Turbine Performance Enhancement," Energies, MDPI, vol. 14(5), pages 1-35, February.
    15. Alfredo Alcayde & Quetzalcoatl Hernandez-Escobedo & David Muñoz-Rodríguez & Alberto-Jesus Perea-Moreno, 2022. "Worldwide Research Trends on Optimizing Wind Turbine Efficiency," Energies, MDPI, vol. 15(18), pages 1-7, September.
    16. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    17. Wenxian Yang & Theodoros Alexandridis & Wenye Tian, 2018. "Numerical Research of the Effect of Surface Biomimetic Features on the Efficiency of Tidal Turbine Blades," Energies, MDPI, vol. 11(4), pages 1-15, April.
    18. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    19. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.

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