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Investigation of a small Horizontal–Axis wind turbine performance with and without winglet

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  • Khaled, Mohamed
  • Ibrahim, Mostafa M.
  • Abdel Hamed, Hesham E.
  • AbdelGwad, Ahmed F.

Abstract

The objective of this study is to demonstrate computationally the effect of winglet length and cant angle on the performance of a small horizontal-axis wind turbine. The computational study was done using the ANSYS Fluent 15 software for a steady-state flow. Different designs of winglet with different lengths and cant angles were numerically studied and optimized using Artificial Neural Network (ANN). The winglet length was changed from 1% to 7% of the wind turbine rotor radius with cant angles from 150 to 900. The parameters of the wind turbine performance, which are power coefficient and thrust force coefficient were investigated for different winglet configurations. This was carried out from cut-in wind speed (3.12 m/s) to wind speed (12 m/s). It demonstrated that, there were noticeable enhancements in power and thrust coefficients in the presence of winglet. The best improvement in the performance was achieved when winglet length was 6.32% and cant angle 48.30. At this case, the percentage increase in power coefficient equals to the percentage increase in thrust coefficient which was 8.787%.

Suggested Citation

  • Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
  • Handle: RePEc:eee:energy:v:187:y:2019:i:c:s0360544219316056
    DOI: 10.1016/j.energy.2019.115921
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    References listed on IDEAS

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    1. Farhan, A. & Hassanpour, A. & Burns, A. & Motlagh, Y. Ghaffari, 2019. "Numerical study of effect of winglet planform and airfoil on a horizontal axis wind turbine performance," Renewable Energy, Elsevier, vol. 131(C), pages 1255-1273.
    2. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    3. Pelletier, Francis & Masson, Christian & Tahan, Antoine, 2016. "Wind turbine power curve modelling using artificial neural network," Renewable Energy, Elsevier, vol. 89(C), pages 207-214.
    4. Flores, P. & Tapia, A. & Tapia, G., 2005. "Application of a control algorithm for wind speed prediction and active power generation," Renewable Energy, Elsevier, vol. 30(4), pages 523-536.
    5. Nicolas Tobin & Ali M. Hamed & Leonardo P. Chamorro, 2015. "An Experimental Study on the Effects ofWinglets on the Wake and Performance of a ModelWind Turbine," Energies, MDPI, vol. 8(10), pages 1-18, October.
    6. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    7. Pillai, Indu R. & Banerjee, Rangan, 2009. "Renewable energy in India: Status and potential," Energy, Elsevier, vol. 34(8), pages 970-980.
    8. Zhu, Bing & Sun, Xiaojing & Wang, Ying & Huang, Diangui, 2017. "Performance characteristics of a horizontal axis turbine with fusion winglet," Energy, Elsevier, vol. 120(C), pages 431-440.
    9. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    10. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
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    Cited by:

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    2. Azlan, F. & Tan, M.K. & Tan, B.T. & Ismadi, M.-Z., 2023. "Passive flow-field control using dimples for performance enhancement of horizontal axis wind turbine," Energy, Elsevier, vol. 271(C).
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    4. Mahmoud G. Hemeida & Ashraf M. Hemeida & Tomonobu Senjyu & Dina Osheba, 2022. "Renewable Energy Resources Technologies and Life Cycle Assessment: Review," Energies, MDPI, vol. 15(24), pages 1-36, December.
    5. Sun, Yukun & Qian, Yaoru & Gao, Yang & Wang, Tongguang & Wang, Long, 2024. "Stall control on the wind turbine airfoil via the single and dual-channel of combining bowing and suction technique," Energy, Elsevier, vol. 290(C).
    6. José Luis Torres-Madroñero & Joham Alvarez-Montoya & Daniel Restrepo-Montoya & Jorge Mario Tamayo-Avendaño & César Nieto-Londoño & Julián Sierra-Pérez, 2020. "Technological and Operational Aspects That Limit Small Wind Turbines Performance," Energies, MDPI, vol. 13(22), pages 1-39, November.
    7. Yossri, Widad & Ben Ayed, Samah & Abdelkefi, Abdessattar, 2021. "Airfoil type and blade size effects on the aerodynamic performance of small-scale wind turbines: Computational fluid dynamics investigation," Energy, Elsevier, vol. 229(C).
    8. Shyuan Cheng & Yaqing Jin & Leonardo P. Chamorro, 2020. "Wind Turbines with Truncated Blades May Be a Possibility for Dense Wind Farms," Energies, MDPI, vol. 13(7), pages 1-13, April.
    9. Emmanuvel Joseph Aju & Dhanush Bhamitipadi Suresh & Yaqing Jin, 2020. "The Influence of Winglet Pitching on the Performance of a Model Wind Turbine: Aerodynamic Loads, Rotating Speed, and Wake Statistics," Energies, MDPI, vol. 13(19), pages 1-15, October.
    10. Zhang, Jisheng & Liu, Siyuan & Guo, Yakun & Sun, Ke & Guan, Dawei, 2022. "Performance of a bidirectional horizontal-axis tidal turbine with passive flow control devices," Renewable Energy, Elsevier, vol. 194(C), pages 997-1008.

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