Adaptive Technique to Solve Multi-objective Feeder Reconfiguration Problem in Real Time Context

  • Carlos Henrique N. de Resende Barbosa
  • Walmir Matos Caminhas
  • Joao Antonio de Vasconcelos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)


This paper presents an innovative method to solve the reconfiguration problem in a distribution network. The main motivation of this work is to take advantage of the power flow analysis repetition when reconfiguration leads the network to a previous configuration due to cyclical loading pattern. The developed methodology combines an optimization technique with fuzzy theory to gain efficiency without losing robustness. In this methodology, the power flow is estimated by well-trained neo-fuzzy neuron network to achieve computing time reduction in the evaluation of individuals during evolutionary algorithm runs. It is noteworthy that the proposed methodology is scalable and its benefits increase as larger feeders are dealt. The effectiveness of the proposed method is demonstrated through examples. The overall performance achieved in the experiments has proved that it is also proper to real time context.


multi-objective optimization NSGA-II fuzzy inference feeder reconfiguration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chis, M., Salama, M.M.A., Jayaram, S.: Capacitor Placement in Distribution Systems Using Heuristic Search Strategies. IEE Proceedings on Generation, Transmission and Distribution 144(3), 225–230 (1997)CrossRefGoogle Scholar
  2. 2.
    Das, D.: Reconfiguration of Distribution System using Fuzzy Multi-objective Approach. International Journal of Electrical Power & Energy Systems 28(5), 331–338 (2006)CrossRefGoogle Scholar
  3. 3.
    Venkatesh, B., Ranjan, R.: Optimal Radial Distribution System Reconfiguration using Fuzzy Adaptation of Evolutionary Programming. Electrical Power & Energy Systems 25, 775–780 (2003)CrossRefGoogle Scholar
  4. 4.
    Kalesar, B.M., Seifi, A.R.: Fuzzy Load Flow in Balanced and Unbalanced Radial Distribution Systems incorporating Composite Load Model. Electrical Power & Energy Systems 32(1), 17–23 (2010)CrossRefGoogle Scholar
  5. 5.
    Zhou, Z.Q., Shirmohammadi, D., Liu, W.-H.E.: Distribution Feeder Reconfiguration for Service Restoration and Load Balancing. IEEE Transactions on Power Systems 12(2), 724–729 (1997)CrossRefGoogle Scholar
  6. 6.
    Aoki, K., Nara, K., Itoh, M., Satoh, T., Kuwabara, H.: A New Algorithm for Service Restoration in Distribution Systems. IEEE Transactions on Power Delivery 4(3), 1832–1839 (1989)CrossRefGoogle Scholar
  7. 7.
    Nara, K., Mishima, Y., Satoh, T.: Network Reconfiguration for Loss Minimization and Load Balancing. In: Power Engineering Society General Meeting, Ibaraki Univ., Japan, pp. 2413–2418. IEEE, Los Alamitos (2003)Google Scholar
  8. 8.
    Sarfi, R.J., Salama, M.A., Chikhani, A.Y.: A Survey of the State of the Art in Distribution System Reconfiguration for System Loss Reduction. Electric Power Systems Research 31(1), 61–70 (1994)CrossRefGoogle Scholar
  9. 9.
    Lee, S.J., Lim, S.I., Bokk-Shin, A.: Service Restoration of Primary Distribution Systems based on Fuzzy Evaluation of Multi-criteria. IEEE Transactions on Power Systems 13(3), 1156–1162 (1998)CrossRefGoogle Scholar
  10. 10.
    Sahoo, N.C., Ranjan, R., Prasad, K., Chaturvedi, A.: A Fuzzy-tuned Genetic Algorithm for Optimal Reconfigurations of Radial Distribution Network. Wiley InterScience, European Transactions on Electrical Power 17(1), 97–111 (2007)CrossRefGoogle Scholar
  11. 11.
    Song, Y.H., Wang, G.S., Johns, A.T., Wang, P.Y.: Distribution Network Reconfiguration for Loss Reduction using Fuzzy controlled Evolutionary Programming. In: IEE Proceedings on Generation, Transmission and Distribution, vol. 144(4), pp. 345–350 (July 1997)Google Scholar
  12. 12.
    Huang, Y.C.: Enhanced Genetic Algorithm-based Fuzzy Multi-objective Approach to Distribution Network Reconfiguration. In: IEE Proceedings on Generation, Transmission and Distribution, vol. 149(5), pp. 615–620 (September 2002)Google Scholar
  13. 13.
    Hsu, Y.Y., Kuo, H.C.: A Heuristic based Fuzzy Reasoning Approach for Distribution System Service Restoration. IEEE Transactions on Power Delivery 9(2), 948–953 (1994)CrossRefGoogle Scholar
  14. 14.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  15. 15.
    Naga Raj, B., Prakasa Rao, K.S.: A new Fuzzy Reasoning Approach for Load Balancing in Distribution System. IEEE Transactions on Power Systems 10(3), 1426–1432 (1995)CrossRefGoogle Scholar
  16. 16.
    Hsiao, Y.T., Chien, C.Y.: Enhancement of Restoration Service in Distribution Systems Using a Combination Fuzzy-GA Method. IEEE Transactions on Power Systems 15(4), 1394–1400 (2000)CrossRefGoogle Scholar
  17. 17.
    Lopez, E., Opazo, H., Garcia, L., Bastard, P.: Online Reconfiguration considering Variability Demand: Applications to Real Networks. IEEE Transactions on Power Systems 19(1), 549–553 (2004)CrossRefGoogle Scholar
  18. 18.
    Yamakawa, T., Uchino, E., Miki, T., Kusabagi, H.: A Neo Fuzzy Neuron and its Applications to System Identification and Predictions to System Behavior. In: Proceedings of the 2nd IIZUKA, IIizuka, Japan, pp. 477–483 (1992)Google Scholar
  19. 19.
    Pereira, M.A., Murari, C.F., Castro Jr., C.A.: A Fuzzy Heuristic Algorithm for Distribution Systems’ Service Restoration. Fuzzy Sets and Systems 102(1), 125–133 (1999)CrossRefGoogle Scholar
  20. 20.
    Hsu, Y.Y., Yang, C.C.: Fast Voltage Estimation using Artificial Neural Network. Electrical Power System 27(11), 1–9 (1993)Google Scholar
  21. 21.
    Jang, J.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Inteligence. Prentice Hall, New Jersey (1997)Google Scholar
  22. 22.
    Baran, M., Wu, F.: Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing. IEEE Transactions on Power Delivery 4(2), 1401–1407 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carlos Henrique N. de Resende Barbosa
    • 1
    • 2
  • Walmir Matos Caminhas
    • 2
  • Joao Antonio de Vasconcelos
    • 2
  1. 1.Departamento de Ciencias Exatas e AplicadasUniversidade Federal de Ouro PretoJoao MonlevadeBrasil
  2. 2.Departamento de Engenharia EletricaUniversidade Federal de Minas GeraisBelo HorizonteBrasil

Personalised recommendations