Power Distribution Network Planning Application Based on Multi-Objective Binary Particle Swarm Optimization Algorithm

  • José Roberto Bezerra
  • Giovanni Cordeiro Barroso
  • Ruth Pastôra Saraiva Leão
  • Raimundo Furtado
  • Eudes Barbosa de Medeiros
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)


Power distribution networks are the most susceptible sector of the whole electric grid in terms of reliability. Failures along the lines cause the disconnection of a great number of customers what have an immediate impact on quality and security indices. Innovations capable to mitigate impacts or improve reliability are ever pursued by the electric utilities. In view of that, the planning of the modern distribution networks must consider the installation of switches along the network as an important procedure to isolate failures reducing the impact and the number of customers not supplied. However, the complexity and the dimension of the current distribution networks, makes the task of proper allocation of switches strongly dependent on the expertise of engineers. This paper proposes an application based on a Multi-Objective Particle Swarm Optimization algorithm that determines the suitable placement and a feasible number of switches on the power distribution networks in order to minimize the number of customers affected by faults. Detailed information about the algorithm and its application in a test distribution system is presented. The effectiveness of the algorithm is presented in a case study applied to the IEEE 123-Node Test Feeder.


Particle Swarm Optimization Distribution Network Particle Swarm Optimization Algorithm Circuit Breaker Power Distribution Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Moradi, A., Fotuhi-Firuzabad, M., Rashidi-Nejad, M.: A reliability cost/worth approach to determine optimum switching placement in distribution systems. In: Transmission and Distribution Conference and Exhibition: Asia and Pacific. IEEE/PES, pp. 1–5 (2005)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (November- December 1995)Google Scholar
  3. 3.
    Christian Blum, X.L.: Swarm intelligence introduction and applications. Springer, Berlin (2008)zbMATHCrossRefGoogle Scholar
  4. 4.
    Wang, H., Jiang, H., Xu, K., Li, G.: Reactive power optimization of power system based on improved particle swarm optimization. In: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, DRPT, pp. 606–609 (July 2011)Google Scholar
  5. 5.
    Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Computational Cybernetics and Simulation, vol. 5 (October 1997)Google Scholar
  6. 6.
    Moradi, A., Fotuhi-Firuzabad, M.: Optimal switch placement in distribution systems using trinary particle swarm optimization algorithm. IEEE Transactions on Power Delivery 23(1), 271–279 (2008)CrossRefGoogle Scholar
  7. 7.
    Abdul Latiff, N., Tsimenidis, C., Sharif, B., Ladha, C.: Dynamic clustering using binary multi-objective particle swarm optimization for wireless sensor networks. In: IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2008, pp. 1–5 (September 2008)Google Scholar
  8. 8.
    Wang, L., Ye, W., Fu, X., Menhas, M.: A modified multi-objective binary particle swarm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part II. LNCS, vol. 6729, pp. 41–48. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Bezerra, J.R., Barroso, G.C., Leao, R.P.: Switch placement algorithm for reducing customers outage impacts on radial distribution networks. In: TENCON 2012 - 2012 IEEE Region 10 Conference, pp. 1–6 (November 2012)Google Scholar
  10. 10.
    Kersting, W.: Radial distribution test feeders. IEEE Power Engineering Society Winter Meeting 2, 908–912 (2001)Google Scholar
  11. 11.
    Singh, K.: Electricity distribution network expansion planning (March 2013),
  12. 12.
    Deb, K.: Multi-objective evolutionary algorithms: Introducing bias among pareto-optimal solutions. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing. Natural Computing Series, pp. 263–292. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley Paperback. Wiley (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Roberto Bezerra
    • 1
  • Giovanni Cordeiro Barroso
    • 2
  • Ruth Pastôra Saraiva Leão
    • 2
  • Raimundo Furtado
    • 2
  • Eudes Barbosa de Medeiros
    • 3
  1. 1.Instituto Federal do CearáBrazil
  2. 2.Universidade Federal do CearáBrazil
  3. 3.Companhia Energética do CearáBrazil

Personalised recommendations