IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v8y2017i1d10.1007_s13198-015-0347-5.html
   My bibliography  Save this article

Hybrid GA–PSO for optimal placement of static VAR compensators in power system

Author

Listed:
  • Abdelmalek Gacem

    (University Med Khaider
    University Echahid Hamma Lakhder)

  • Djilani Benattous

    (University Med Khaider
    University Echahid Hamma Lakhder)

Abstract

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

Suggested Citation

  • Abdelmalek Gacem & Djilani Benattous, 2017. "Hybrid GA–PSO for optimal placement of static VAR compensators in power system," 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. 8(1), pages 247-254, January.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-015-0347-5
    DOI: 10.1007/s13198-015-0347-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-015-0347-5
    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/s13198-015-0347-5?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. Malika Fodil & Ali Djerioui & Mohamed Ladjal & Abdelhakim Saim & Fouad Berrabah & Hemza Mekki & Samir Zeghlache & Azeddine Houari & Mohamed Fouad Benkhoris, 2023. "Optimization of PI Controller Parameters by GWO Algorithm for Five-Phase Asynchronous Motor," Energies, MDPI, vol. 16(10), pages 1-14, May.
    2. Hana Merah & Abdelmalek Gacem & Djilani Ben Attous & Abderezak Lashab & Francisco Jurado & Mariam A. Sameh, 2022. "Sizing and Sitting of Static VAR Compensator (SVC) Using Hybrid Optimization of Combined Cuckoo Search (CS) and Antlion Optimization (ALO) Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    3. Mandhir Kumar Verma & Vivekananda Mukherjee & Vinod Kumar Yadav & Santosh Ghosh, 2020. "Constraints for effective distribution network expansion planning: an ample review," 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. 11(3), pages 531-546, June.
    4. Lenin Kanagasabai, 2022. "Real power loss reduction by quantum based Ptilonorhynchus violaceus optimization and Haliastur Indus algorithms," 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. 13(4), pages 1913-1931, August.

    More about this item

    Keywords

    FACTS; SVC; HGAPSO; PSO; GA; Multi-objective;
    All these keywords.

    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:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-015-0347-5. 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.