IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v7y2016i1d10.1007_s13198-015-0375-1.html
   My bibliography  Save this article

Particle swarm optimization based PI controller of VSC-HVDC system connected to a wind farm

Author

Listed:
  • Lakhdar Mazouz

    (Sidi Belabbes University)

  • Sid Ahmed Zidi

    (Sidi Belabbes University)

  • Mohamed Khatir

    (Sidi Belabbes University)

  • Tahar Benmessaoud

    (Djelfa University)

  • Slami Saadi

    (Djelfa University)

Abstract

This paper, explores the connection of a wind farm to the grid through high voltage direct current system on voltage source converter VSC-HVDC. 12 MW wind farm consisting of 6 individual 2 MW permanent magnet synchronous generators is presented. In this paper we proposed a new particle swarm optimization algorithm (PSO) based PI controller in order to optimize the gains of PI controller of VSC-HVDC link, which consequently improve the stability of the link after a strict faults. Different results are obtained to show the efficiency of the proposed PSO algorithm for the conception of optimal controller for a VSC-HVDC link connected to a wind farm. MATLAB/Similink simulations are provided to illustrate the performance of the proposed approach under serious perturbation as single AC fault and DC fault.

Suggested Citation

  • Lakhdar Mazouz & Sid Ahmed Zidi & Mohamed Khatir & Tahar Benmessaoud & Slami Saadi, 2016. "Particle swarm optimization based PI controller of VSC-HVDC system connected to a wind farm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 239-246, December.
    • Handle: RePEc:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-015-0375-1
    DOI: 10.1007/s13198-015-0375-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-015-0375-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-015-0375-1?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. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.

    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:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-015-0375-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.