IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8895022.html
   My bibliography  Save this article

Random Vector Functional Link Network Optimized by Jaya Algorithm for Transient Stability Assessment of Power Systems

Author

Listed:
  • Jianhong Pan
  • Jiashu Fan
  • Aidi Dong
  • Yang Li

Abstract

A novel transient stability assessment (TSA) approach using random vector functional link (RVFL) network optimized by Jaya algorithm, called Jaya-RVFL, is proposed for power systems in this paper. First, by extracting system-level features from phasor measurement unit (PMU) measurements as predictors, an RVFL-based TSA model is proposed. In order to improve the performance of RVFL classifiers, a quantile scaling approach is utilized to optimize the randomization range of input weights via the Jaya algorithm. The simulation results on IEEE 39-bus system and a real-world power system show that the presented method outperforms other popular methods comprising multilayer perception, probabilistic neural network, and support vector machine.

Suggested Citation

  • Jianhong Pan & Jiashu Fan & Aidi Dong & Yang Li, 2020. "Random Vector Functional Link Network Optimized by Jaya Algorithm for Transient Stability Assessment of Power Systems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:8895022
    DOI: 10.1155/2020/8895022
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/8895022.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/8895022.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/8895022?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:8895022. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.