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Real-time maximized power generation of vertical axis wind turbines based on characteristic curves of power coefficients via fuzzy pulse width modulation load regulation

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  • Lap-Arparat, Pongpak
  • Leephakpreeda, Thananchai

Abstract

The efficiency of power generation is strongly dependent on wind speeds and rotational speeds of vertical axis wind turbines (VAWTs) over time. The efficiency is determined by the characteristic curves of power coefficients vs. tip speed ratios. In this work, the rotations of VAWTs can be tightly managed at the angular speed of the optimal tip speed ratio in order to yield the maximum mechanical work in a wide range of wind speeds, all the time. The maximized power generation of VAWTs is systematically obtained by a fuzzy pulse width modulation load regulation of power generation based characteristic curves of power coefficients. Regulated hybrid VAWTs are analytically and experimentally investigated to illustrate the significant improvement of power generation, compared with another traditional hybrid VAWT under varying low speed wind conditions. It is confirmed that the energy production of 16.50 Wh from the controlled VAWT is significantly higher (by 57.48%) than the 10.48 Wh from the uncontrolled VAWT in open field conditions during a testing day. In experimenting with the hybrid VAWT in a real working situation, the proposed methodology can be generalized for real-time implementation in maximizing the power generation of other VAWTs at a wind farm.

Suggested Citation

  • Lap-Arparat, Pongpak & Leephakpreeda, Thananchai, 2019. "Real-time maximized power generation of vertical axis wind turbines based on characteristic curves of power coefficients via fuzzy pulse width modulation load regulation," Energy, Elsevier, vol. 182(C), pages 975-987.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:975-987
    DOI: 10.1016/j.energy.2019.06.098
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    Cited by:

    1. Dong, Mi & Sun, Mingren & Song, Dongran & Huang, Liansheng & Yang, Jian & Joo, Young Hoon, 2022. "Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest," Energy, Elsevier, vol. 257(C).
    2. Kashif Sohail & Hooman Farzaneh, 2022. "Model for Optimal Power Coefficient Tracking and Loss Reduction of the Wind Turbine Systems," Energies, MDPI, vol. 15(11), pages 1-19, June.
    3. Aktaş, Ahmet & Kırçiçek, Yağmur, 2020. "A novel optimal energy management strategy for offshore wind/marine current/battery/ultracapacitor hybrid renewable energy system," Energy, Elsevier, vol. 199(C).
    4. Fathy, Ahmed & Rezk, Hegazy & Yousri, Dalia & Kandil, Tarek & Abo-Khalil, Ahmed G., 2022. "Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system," Energy, Elsevier, vol. 249(C).
    5. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    6. Pallotta, A. & Pietrogiacomi, D. & Romano, G.P., 2020. "HYBRI – A combined Savonius-Darrieus wind turbine: Performances and flow fields," Energy, Elsevier, vol. 191(C).

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