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A Study on Wind Collection Effect of Vertical Axis Windmills

Author

Listed:
  • Tadashi Hosoe

    (Department of Eco-Electric Power Research Center, Aichi Institute of Technology, Toyota-City 470-0356, Japan)

  • Kazuto Yukita

    (Department of Eco-Electric Power Research Center, Aichi Institute of Technology, Toyota-City 470-0356, Japan)

Abstract

In recent years, global warming caused by greenhouse gasses such as carbon dioxide has become a concern. This has resulted in increased focus on environmentally friendly power systems. Consequently, renewable energy power generation methods, such as wind and solar power generation, have attracted attention. Wind power generation is expected to significantly increase in the future. However, in many inland areas in Japan, the average wind speed remains 6 m/s or less. In this study, we proposed the introduction of winglets and wind collectors (used in aircraft wings) into straight-wing vertical-axis wind turbines to improve their power generation efficiency. Field tests were conducted to confirm the effectiveness of the proposed method. Using winglets and wind collectors, the wind turbine rotation speed was increased at low wind speeds, which facilitated the generation of power. Moreover, it was confirmed that a wind turbine equipped with the proposed winglets and wind collectors could capture wind without its dispersal as it passed through the turbine.

Suggested Citation

  • Tadashi Hosoe & Kazuto Yukita, 2024. "A Study on Wind Collection Effect of Vertical Axis Windmills," Energies, MDPI, vol. 17(23), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6088-:d:1535903
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    References listed on IDEAS

    as
    1. Fabio Famoso & Ludovica Maria Oliveri & Sebastian Brusca & Ferdinando Chiacchio, 2024. "A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant," Energies, MDPI, vol. 17(7), pages 1-24, March.
    2. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
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