Artificial bee colony algorithm for solving optimal power flow problem.

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits,...

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Publicado en:Scientific World Journal pp. 159040 - 159041
Autores principales: Le Dinh, Luong, Vo Ngoc, Dieu, Vasant, Pandian
Formato: research Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
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      dt: 2013
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2013/159040
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        atl: Artificial bee colony algorithm for solving optimal power flow problem.
      aug:
        au:
          Le Dinh, Luong
          Vo Ngoc, Dieu
          Vasant, Pandian
        affil: Faculty of Mechanical, Electrical and Electronic Engineering, HCMC University of Technology (HUTECH), Ho Chi Minh City, Vietnam.
      sug:
        subj:
          Power Sources
          Models, Theoretical
          Algorithms
      ab: This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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