IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v187y2019ics0360544219316056.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219316056
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.115921?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Eslam S. Abdelghany & Hesham H. Sarhan & Raed Alahmadi & Mohamed B. Farghaly, 2023. "Study the Effect of Winglet Height Length on the Aerodynamic Performance of Horizontal Axis Wind Turbines Using Computational Investigation," Energies, MDPI, vol. 16(13), pages 1-20, July.
    4. 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).
    5. Barbarić, Marina & Batistić, Ivan & Guzović, Zvonimir, 2022. "Numerical study of the flow field around hydrokinetic turbines with winglets on the blades," Renewable Energy, Elsevier, vol. 192(C), pages 692-704.
    6. 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.
    7. 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.
    8. 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).
    9. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    2. Barbarić, Marina & Batistić, Ivan & Guzović, Zvonimir, 2022. "Numerical study of the flow field around hydrokinetic turbines with winglets on the blades," Renewable Energy, Elsevier, vol. 192(C), pages 692-704.
    3. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    4. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
    5. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
    6. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    7. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    8. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    9. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    10. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    11. Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
    12. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
    13. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1556-1566, April.
    14. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
    15. Mohammed, Y.S. & Mustafa, M.W. & Bashir, N., 2014. "Hybrid renewable energy systems for off-grid electric power: Review of substantial issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 527-539.
    16. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    17. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    18. 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.
    19. 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).
    20. Jung, Sungmoon & Kwon, Soon-Duck, 2013. "Weighted error functions in artificial neural networks for improved wind energy potential estimation," Applied Energy, Elsevier, vol. 111(C), pages 778-790.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:187:y:2019:i:c:s0360544219316056. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.