• DocumentCode
    2623007
  • Title

    Application of Ant Colony System algorithm to distribution networks reconfiguration for loss reduction

  • Author

    Ghorbani, M.A. ; Hosseinian, S.H. ; Vahidi, B.

  • Author_Institution
    Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran
  • fYear
    2008
  • fDate
    22-24 May 2008
  • Firstpage
    269
  • Lastpage
    273
  • Abstract
    Optimal load distribution among the power distribution substation feeders is one of the most important and least expensive operational tools to lower the losses. For this purpose, the state of the network switches (open/closed) should be determined such that the load switched from the heavily loaded feeders to the lightly loaded ones and in the meantime the operational constraints and radial nature of the network are not violated. In this paper, the ant colony system (ACS) algorithm is used to solve this problem. here, the artificial ants would be able to find the best possible solution by using the information in the form of pheromones poured in the path they travel. A new strategy for switch selection by ants is proposed in this paper. At the end, in order to check the capability of this approach, three different networks cited in the literature are studied and the comparison of results is included.
  • Keywords
    distribution networks; optimisation; ant colony system algorithm; artificial ants; distribution networks reconfiguration; loss reduction; optimal load distribution; power distribution substation feeders; radial distribution network; Ant colony optimization; Artificial neural networks; Automation; Power distribution; Power generation economics; Protection; Substations; Switches; System testing; Voltage; Radial distribution network; ant colony system; reconfiguration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Optimization of Electrical and Electronic Equipment, 2008. OPTIM 2008. 11th International Conference on
  • Conference_Location
    Brasov
  • Print_ISBN
    978-1-4244-1544-1
  • Electronic_ISBN
    978-1-4244-1545-8
  • Type

    conf

  • DOI
    10.1109/OPTIM.2008.4602377
  • Filename
    4602377