IDEAS home Printed from https://ideas.repec.org/a/hin/complx/2420171.html
   My bibliography  Save this article

Optimal Network Reconfiguration to Reduce Power Loss Using an Initial Searching Point for Continuous Genetic Algorithm

Author

Listed:
  • Thuan Thanh Nguyen
  • Thang Trung Nguyen
  • Ngoc Au Nguyen

Abstract

In this paper, an effective method to determine an initial searching point (ISP) of the network reconfiguration (NR) problem for power loss reduction is proposed for improving the efficiency of the continuous genetic algorithm (CGA) to the NR problem. The idea of the method is to close each initial open switch in turn and solve power flow for the distribution system with the presence of a closed loop to choose a switch with the smallest current in the closed loop for opening. If the radial topology constraint of the distribution system is satisfied, the switch opened is considered as a control variable of the ISP. Then, ISP is attached to the initial population of CGA. The calculated results from the different distribution systems show that the proposed CGA using ISP could reach the optimal radial topology with better successful rate and obtained solution quality than the method based on CGA using the initial population generated randomly and the method based on CGA using the initial radial configuration attached to the initial population. As a result, CGA using ISP can be a favorable method for finding a more effective radial topology in operating distribution systems.

Suggested Citation

  • Thuan Thanh Nguyen & Thang Trung Nguyen & Ngoc Au Nguyen, 2020. "Optimal Network Reconfiguration to Reduce Power Loss Using an Initial Searching Point for Continuous Genetic Algorithm," Complexity, Hindawi, vol. 2020, pages 1-21, May.
    • Handle: RePEc:hin:complx:2420171
    DOI: 10.1155/2020/2420171
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/2420171.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2020/2420171.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/2420171?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
    ---><---

    Citations

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


    Cited by:

    1. Monika Kulisz & Justyna Kujawska & Bartosz Przysucha & Wojciech Cel, 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network," Energies, MDPI, vol. 14(18), pages 1-17, September.

    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:complx:2420171. 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.