IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v31y2023i06ns0218348x23401424.html
   My bibliography  Save this article

Flexible Adaptive Marine Predator Algorithm For High-Dimension Optimization And Application In Wind Turbine Fault Diagnosis

Author

Listed:
  • MINGZHU TANG

    (College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China)

  • JIABIAO YI

    (College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China)

  • HUAWEI WU

    (��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China)

  • YANG WANG

    (��School of Electric Engineering, Shanghai Dianji University, Shanghai 201306, P. R. China§State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, P. R. China)

  • CHENHUAN CAO

    (College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China)

  • ZIXIN LIANG

    (College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China)

  • JIAWEN ZUO

    (College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China)

  • FUQIANG XIONG

    (�State Grid Hunan Extra High Voltage Substation Company, Changsha 410029, P. R. China∥Substation Intelligent Operation and Inspection, Laboratory of State Grid Hunan Electric Power, Co., Ltd., Changsha 410029, P. R. China)

Abstract

The marine predator algorithm (MPA) is the latest metaheuristic algorithm proposed in 2020, which has an outstanding merit-seeking capability, but still has the disadvantage of slow convergence and is prone to a local optimum. To tackle the above problems, this paper proposed the flexible adaptive MPA. Based on the MPA, a flexible adaptive model is proposed and applied to each of the three stages of population iteration. By introducing nine benchmark test functions and changing their dimensions, the experimental results show that the flexible adaptive MPA has faster convergence speed, more accurate convergence ability, and excellent robustness. Finally, the flexible adaptive MPA is applied to feature selection experiments. The experimental results of 10 commonly used UCI high-dimensional datasets and three wind turbine (WT) fault datasets show that the flexible adaptive MPA can effectively extract the key features of high-dimensional datasets, reduce the data dimensionality, and improve the effectiveness of the machine algorithm for WT fault diagnosis (FD).

Suggested Citation

  • Mingzhu Tang & Jiabiao Yi & Huawei Wu & Yang Wang & Chenhuan Cao & Zixin Liang & Jiawen Zuo & Fuqiang Xiong, 2023. "Flexible Adaptive Marine Predator Algorithm For High-Dimension Optimization And Application In Wind Turbine Fault Diagnosis," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-25.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x23401424
    DOI: 10.1142/S0218348X23401424
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X23401424
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218348X23401424?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.

    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:wsi:fracta:v:31:y:2023:i:06:n:s0218348x23401424. 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: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    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.