IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v31y2006i12p2153-2161.html
   My bibliography  Save this article

Assessment of optimum tip speed ratio in wind turbines using artificial neural networks

Author

Listed:
  • Yurdusev, M.A.
  • Ata, R.
  • Çetin, N.S.

Abstract

Wind turbine blade design depends on several factors, such as turbine profile used, blade number, power factor, and tip speed ratio. The key to designing a wind turbine is to assess the optimal tip speed ratio (TSR). This will directly affect the power generated and, in turn, the effectiveness of the investment made. TSR is suggested to be taken between 7 and 8 and in practice generally taken as 7 for a 3-blade network-connected wind turbine. However, the optimal TSR is dependent upon the profile type used and the blade number and could fall out of the boundaries suggested. Therefore, it has to be assessed accordingly. In this study, the optimal TSR and the power factor of a wind turbine are predicted using artificial neural networks (ANN) based on the parameters involved for NACA 4415 and LS-1 profile types with 3 and 4 blades. The ANN structure built is found to be more successful than the conventional approach in estimating the TSR and power factor.

Suggested Citation

  • Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
    • Handle: RePEc:eee:energy:v:31:y:2006:i:12:p:2153-2161
    DOI: 10.1016/j.energy.2005.09.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544205001817
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2005.09.007?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. 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.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    3. Çam, Ertugrul & Arcaklıoğlu, Erol & Çavuşoğlu, Abdullah & Akbıyık, Bilge, 2005. "A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks," Renewable Energy, Elsevier, vol. 30(2), pages 227-239.
    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. Imraan, Mustahib & Sharma, Rajnish N. & Flay, Richard G.J., 2013. "Wind tunnel testing of a wind turbine with telescopic blades: The influence of blade extension," Energy, Elsevier, vol. 53(C), pages 22-32.
    2. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "On-line designed hybrid controller with adaptive observer for variable-speed wind generation system," Energy, Elsevier, vol. 35(7), pages 3022-3030.
    3. Peter J. Schubel & Richard J. Crossley, 2012. "Wind Turbine Blade Design," Energies, MDPI, vol. 5(9), pages 1-25, September.
    4. Jie Zhu & Xin Cai & Rongrong Gu, 2017. "Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 10(1), pages 1-18, January.
    5. Song, Zhanfeng & Shi, Tingna & Xia, Changliang & Chen, Wei, 2012. "A novel adaptive control scheme for dynamic performance improvement of DFIG-Based wind turbines," Energy, Elsevier, vol. 38(1), pages 104-117.
    6. Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
    7. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    8. Kehinde A. Adeyeye & Nelson Ijumba & Jonathan S. Colton, 2021. "Multi-Parameter Optimization of Efficiency, Capital Cost and Mass of Ferris Wheel Turbine for Low Wind Speed Regions," Energies, MDPI, vol. 14(19), pages 1-18, September.
    9. Liu, Xiongwei & Wang, Lin & Tang, Xinzi, 2013. "Optimized linearization of chord and twist angle profiles for fixed-pitch fixed-speed wind turbine blades," Renewable Energy, Elsevier, vol. 57(C), pages 111-119.
    10. Sergiienko, N.Y. & da Silva, L.S.P. & Bachynski-Polić, E.E. & Cazzolato, B.S. & Arjomandi, M. & Ding, B., 2022. "Review of scaling laws applied to floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    11. Zhiqiang Yang & Minghui Yin & Yan Xu & Yun Zou & Zhao Yang Dong & Qian Zhou, 2016. "Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines," Energies, MDPI, vol. 9(12), pages 1-15, December.
    12. 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.
    13. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
    14. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "Fuzzy neural network output maximization control for sensorless wind energy conversion system," Energy, Elsevier, vol. 35(2), pages 592-601.
    15. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    16. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    17. Alkhabbaz, Ali & Yang, Ho-Seong & Weerakoon, A.H Samitha & Lee, Young-Ho, 2021. "A novel linearization approach of chord and twist angle distribution for 10 kW horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 178(C), pages 1398-1420.
    18. Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    19. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2010. "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, Elsevier, vol. 35(3), pages 695-702.

    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. 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.
    3. 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.
    4. López, P. & Velo, R. & Maseda, F., 2008. "Effect of direction on wind speed estimation in complex terrain using neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2266-2272.
    5. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    6. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    7. Hemmatabady, Hoofar & Welsch, Bastian & Formhals, Julian & Sass, Ingo, 2022. "AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling," Applied Energy, Elsevier, vol. 311(C).
    8. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    9. Rosiek, S. & Batlles, F.J., 2010. "Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network," Renewable Energy, Elsevier, vol. 35(12), pages 2894-2901.
    10. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    11. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    12. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    13. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    14. Samet, Haidar & Hashemi, Farid & Ghanbari, Teymoor, 2015. "Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1-18.
    15. Μichalena, Evanthie & Hills, Jeremy M., 2012. "Renewable energy issues and implementation of European energy policy: The missing generation?," Energy Policy, Elsevier, vol. 45(C), pages 201-216.
    16. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    17. 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).
    18. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    19. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    20. Haddad, S. & Benghanem, M. & Mellit, A. & Daffallah, K.O., 2015. "ANNs-based modeling and prediction of hourly flow rate of a photovoltaic water pumping system: Experimental validation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 635-643.

    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:31:y:2006:i:12:p:2153-2161. 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.