IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v182y2019icp975-987.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219312307
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.06.098?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. Pallotta, A. & Pietrogiacomi, D. & Romano, G.P., 2020. "HYBRI – A combined Savonius-Darrieus wind turbine: Performances and flow fields," Energy, Elsevier, vol. 191(C).
    4. 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).
    5. 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).
    6. 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).

    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:182:y:2019:i:c:p:975-987. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.