An Efficient Invasive Weed Optimization Algorithm for Distribution Feeder Reconfiguration and Loss Minimization

  • K. Sathish Kumar
  • K. Rajalakhsmi
  • S. Prabhakar Karthikeyan
  • R. Rajaram
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


The distribution network carries electricity from the transmission system and delivers it to consumers. Distribution losses account for major part of power system losses. The low-voltage operation in the distribution system is a major reason for higher technical losses due to inherent properties of the network. In this paper, a method based on invasive weed optimization algorithm (IWOA) is proposed for distribution network reconfiguration with the objective of real power loss minimization. The feeder reconfiguration problem is formulated as a nonlinear optimization problem, and IWOA is used to find the optimal solution. The proposed method is implemented on standard 16-bus test system [1]. Test results show that the proposed feeder reconfiguration method can effectively ensure the loss minimization [2].


Invasive weed optimization algorithm (IWOA) Feeder reconfiguration 



The authors would like to thank the management of VIT University for their encouragement and the support given during this work.


  1. 1.
    C. Lakshminarayana, M.R. Mohan, An improved technique for service restoration in distribution systems using non-dominated sorting genetic algorithm. Int. J. Electr. Power Energy. Syst 31(3), 162–170 (2011)Google Scholar
  2. 2.
    E.R. Sanseverino, Minimum losses reconfiguration of MV distribution networks through local control of tie-switches. IEEE Trans. Power Deliv. 18(3), 762–771 (2003)CrossRefGoogle Scholar
  3. 3.
    Q. Zhou, D. Shirmohammadi, W.-H.E. Liu, Distribution feeder reconfiguration for service restoration and Load balancing. IEEE Trans. Power Syst. 12(2), 724–729 (1997)CrossRefGoogle Scholar
  4. 4.
    D. Das, A fuzzy multiobjective approach for network reconfiguration of distribution systems. IEEE Trans. Power Deliv. 21(1), 202–209 (2006)CrossRefGoogle Scholar
  5. 5.
    A.R. Mehrabian, C. Lucas, A novel numerical optimization algorithm inspired from weed colonization. Int. J Ecol. Inform. 1, 355–366 (2006)CrossRefGoogle Scholar
  6. 6.
    J.Z. Zhu, Optimal reconfiguration of electric distribution network using refined genetic algorithm. Electr. Power Syst. Res. 62, 37–42 (2002)CrossRefGoogle Scholar
  7. 7.
    Y.K. Wu, C.Y. Lee, L.C. Liu, S.H. Tsai, Study of reconfiguration for the distribution system with distributed generators. IEEE. Trans. Power Deliv. 25(3) (2010)Google Scholar
  8. 8.
    J.A. Martín, A.J. Gil, A new heuristic approach for distribution systems loss reduction. Electr. Power Syst. Res. 78(11), 1953–1958 (2008)CrossRefGoogle Scholar
  9. 9.
    A. Swarnkar, N. Gupta, K.R. Niazi, Minimal loss configuration for large scale radial distribution systems using adaptive genetic algorithms, in 16th National Power Systems Conference. (2010)Google Scholar
  10. 10.
    K. Sathish Kumar, T. Jayabarathi, Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm. Int. J. Electr. Power Energy Syst. (2011)Google Scholar
  11. 11.
    T. Niknam, An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration. Int. J. Expert Syst. Appl. 38, 2878–2887 (2011)CrossRefGoogle Scholar
  12. 12.
    T. Niknam, E. Azadfarsani, M. Jabbari, A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for distribution feeder reconfiguration. Int. J. Energy Convers. Manage. 54, 7–16 (2012)CrossRefGoogle Scholar
  13. 13.
    T. Thakur, J. Dhiman, A new approach to load flow solutions for radial distribution system. IEEE PES Transmission and Distribution Conference and Exposition Latin America (2006)Google Scholar
  14. 14.
    S. Civanlar, J.J. Grainger, H. Yin, S.S.H. Lee, Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Deliv. 3(3), 1217–1223 (1988)CrossRefGoogle Scholar
  15. 15.
    D. Shirmohammadi, W.H. Hong, Reconfiguration of electric distribution networks for resistive line loss reduction. IEEE Trans. Power Deliv. 4(1), 1492–1498 (1989)CrossRefGoogle Scholar
  16. 16.
    F.V. Gomes, S. Carneiro, J.L.R. Pereira, M.P. Vinagre, P.A.N. Garcia, A new heuristic reconfiguration algorithm for large distribution systems. IEEE Trans. Power Syst. 20(3), 1373–1378 (2005)CrossRefGoogle Scholar
  17. 17.
    S.K. Goswami, S.K. Basu, A new algorithm for the reconfiguration of distribution feeders for loss minimization. IEEE Trans. Power Deliv. 7(3), 1484–1491 (1992)CrossRefGoogle Scholar
  18. 18.
    T.E. Mcdermott, I. Drezga, R.P. Broadwater, A heuristic nonlinear constructive method for distribution system reconfiguration. IEEE Trans. Power Syst. 14(2), 478–483 (1999)CrossRefGoogle Scholar
  19. 19.
    FV. Gomes, S. Carneiro Jr., JLR. Pereira, MP. Vinagre, PAN. Garcia, LR. De Araujo. A new distribution system reconfiguration approach using optimum power flow and sensitivity analysis for loss reduction. IEEE Trans. Power Syst 21(4) (2006)Google Scholar
  20. 20.
    C.T. Su, C.F. Chang, J.P. Chiou, Distribution network reconfiguration for loss reduction by ant colony search algorithm. Int. J. Electr. Power Syst. Res. 75, 190–199 (2005)CrossRefGoogle Scholar
  21. 21.
    DP. Kothari, R. Ranjan, KC. Singal, Renewable energy sources and technology (Prentice Hall India 2011)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • K. Sathish Kumar
    • 1
  • K. Rajalakhsmi
    • 2
  • S. Prabhakar Karthikeyan
    • 1
  • R. Rajaram
    • 1
  1. 1.School of Electrical EngineeringVIT UniversityVelloreIndia
  2. 2.Department of Electronics EngineeringAlpha College of Engineering, Anna UniversityChennaiIndia

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