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Analyses of the Extensible Blade in Improving Wind Energy Production at Sites with Low-Class Wind Resource

Author

Listed:
  • Jiale Li

    (Department of Civil Engineering, Case Western Reserve University, 10900 Euclid Avenue, Bingham Building, Cleveland, OH 44106-7201, USA)

  • Xiong (Bill) Yu

    (Department of Civil Engineering, Case Western Reserve University, 10900 Euclid Avenue, Bingham Building, Cleveland, OH 44106-7201, USA
    Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA)

Abstract

This paper describes the feasibility analysis of an innovative, extensible blade technology. The blade aims to significantly improve the energy production of a wind turbine, particularly at locations with unfavorable wind conditions. The innovative ‘smart’ blade will be extended at low wind speed to harvest more wind energy; on the other hand, it will be retracted to its original shape when the wind speed is above the rated wind speed to protect the blade from damages by high wind loads. An established aerodynamic model is implemented in this paper to evaluate and compare the power output of extensible blades versus a baseline conventional blade. The model was first validated with a monitored power production curve based on the wind energy production data of a conventional turbine blade, which is subsequently used to estimate the power production curve of extended blades. The load-on-blade structures are incorporated as the mechanical criteria to design the extension strategies. Wind speed monitoring data at three different onshore and offshore sites around Lake Erie are used to predict the annual wind energy output with different blades. The effects of extension on the dynamic characteristics of blade are analyzed. The results show that the extensive blade significantly increases the annual wind energy production (up to 20% to 30%) with different blade extension strategies. It, therefore, has the potential to significantly boost wind energy production for utility-scale wind turbines located at sites with low-class wind resource.

Suggested Citation

  • Jiale Li & Xiong (Bill) Yu, 2017. "Analyses of the Extensible Blade in Improving Wind Energy Production at Sites with Low-Class Wind Resource," Energies, MDPI, vol. 10(9), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1295-:d:110361
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    1. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    2. 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.
    3. Wichser, Corinne & Klink, Katherine, 2008. "Low wind speed turbines and wind power potential in Minnesota, USA," Renewable Energy, Elsevier, vol. 33(8), pages 1749-1758.
    4. No, T.S. & Kim, J.-E. & Moon, J.H. & Kim, S.J., 2009. "Modeling, control, and simulation of dual rotor wind turbine generator system," Renewable Energy, Elsevier, vol. 34(10), pages 2124-2132.
    5. Mekonnen, Addisu D. & Gorsevski, Pece V., 2015. "A web-based participatory GIS (PGIS) for offshore wind farm suitability within Lake Erie, Ohio," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 162-177.
    6. Sharma, R.N. & Madawala, U.K., 2012. "The concept of a smart wind turbine system," Renewable Energy, Elsevier, vol. 39(1), pages 403-410.
    7. Soler-Bientz, Rolando, 2011. "Preliminary results from a network of stations for wind resource assessment at North of Yucatan Peninsula," Energy, Elsevier, vol. 36(1), pages 538-548.
    8. Wang, Xuefei & Yang, Xu & Zeng, Xiangwu, 2017. "Seismic centrifuge modelling of suction bucket foundation for offshore wind turbine," Renewable Energy, Elsevier, vol. 114(PB), pages 1013-1022.
    9. Sedaghat, Ahmad & El Haj Assad, M. & Gaith, Mohamed, 2014. "Aerodynamics performance of continuously variable speed horizontal axis wind turbine with optimal blades," Energy, Elsevier, vol. 77(C), pages 752-759.
    10. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    11. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.
    12. Dai, Juchuan & Liu, Deshun & Wen, Li & Long, Xin, 2016. "Research on power coefficient of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 86(C), pages 206-215.
    13. Gorsevski, Pece V. & Cathcart, Steven C. & Mirzaei, Golrokh & Jamali, Mohsin M. & Ye, Xinyue & Gomezdelcampo, Enrique, 2013. "A group-based spatial decision support system for wind farm site selection in Northwest Ohio," Energy Policy, Elsevier, vol. 55(C), pages 374-385.
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    Cited by:

    1. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    2. Gherboudj, Imen & Zorgati, Mohamed & Chamarthi, Phani-Kumar & Tuomiranta, Arttu & Mohandes, Baraa & Beegum, Naseema S. & Al-Sudairi, Jood & Al-Owain, Omar & Shibli, Hussain & El-Moursi, Mohamed & Ghed, 2021. "Renewable energy management system for Saudi Arabia: Methodology and preliminary results," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Wang, Xuefei & Zeng, Xiangwu & Yang, Xu & Li, Jiale, 2019. "Seismic response of offshore wind turbine with hybrid monopile foundation based on centrifuge modelling," Applied Energy, Elsevier, vol. 235(C), pages 1335-1350.
    4. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.
    5. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    6. Toni Pujol & Albert Massaguer & Eduard Massaguer & Lino Montoro & Martí Comamala, 2018. "Net Power Coefficient of Vertical and Horizontal Wind Turbines with Crossflow Runners," Energies, MDPI, vol. 11(1), pages 1-24, January.

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