• DocumentCode
    3356581
  • Title

    Optimal Planning of Substation Locating and Sizing Based on Improved QPSO Algorithm

  • Author

    Xiao Bai ; Qin Tao ; Feng Dan ; Mu Gang ; Li Ping ; Xiao Guang-Ming

  • Author_Institution
    Sch. of Electr. Eng., Northeast Dianli Univ., Jilin
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    A hybrid model for substation locating and sizing is presented in this paper to work out the number of newly-built substations and capacity-expansion, load assignment and optimal size of substation. Geographic information system is used as a platform to manage a great deal of data in this method, in which the optimum assembly model of the substation capacity and the number of substations be the first layer model, then the second layer model of load assignment is constructed by load constructing vector, and then improves traditional single location model as the third layer model of substation locating and sizing. Based on economy and reliability constraints, three- layer model is nested to each other, finally the improved quantum-behaved particle swarm optimization (IQPSO) algorithm is used as a new algorithm to solve hybrid model. The IQPSO algorithm is tested by a realistic planning project to verify the effectiveness and feasibility.
  • Keywords
    geographic information systems; particle swarm optimisation; power engineering computing; power system planning; reliability; substations; QPSO algorithm; geographic information system; load assignment; quantum-behaved particle swarm optimization algorithm; second layer model; substation location optimal planning; Assembly systems; Convergence; Cost function; Geographic Information Systems; Mathematical model; Meeting planning; Particle swarm optimization; Solid modeling; Substations; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918571
  • Filename
    4918571