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

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  • 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
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    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Peter J. Schubel & Richard J. Crossley, 2012. "Wind Turbine Blade Design," Energies, MDPI, vol. 5(9), pages 1-25, September.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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.

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