Optimum Sub-station Positioning Using Hierarchial Clustering

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

Selection of optimum location of a sub-station and distribution of load points to each available sub-station has been a major concern among researchers but all have made either the use of man machine interface or have made some approximations. Here in this paper, a soft computing approach Hierarchial Clustering method is used for grouping the various load points as per the number of distribution sub-stations available. The method further gives an optimum location of the distribution sub-station taking into aspects the distances of the various load points that it is feeding. The results of the discussed technique will lead to a configuration of distribution substations depending on the no. of load points and sub-stations required. It will have an effect of lowering the long range distribution expenses as it will lead to optimum feeder path. The application of the proposed methodology to a case study is presented.

Keywords

Distribution Planning Hierarchial Clustering Method (HCM) Dendrogram 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Turkay, B., Artac, T.: Optimal Distribution Network Design Using Genetic Algorithm. Electric Power Components and Systems 33, 513–524 (2005)CrossRefGoogle Scholar
  2. 2.
    Gomez, J.F., et al.: Ant Colony System Algorithm for the Planning of Primary Distribution Circuits. IEEE Transactions on Power Systems 19(2) (May 2004)Google Scholar
  3. 3.
    Li, K.K., Chung, T.S.: Distribution Planning Using Rule Based Expert System Approach. In: IEEE International Conference on Electric Utility Deregulation and Power Technologies, DRPT 2004 (April 2004)Google Scholar
  4. 4.
    Crawford, D.M., Holt, S.B.: A Mathematical Optimization Technique For Locating Sizing Distribution Sub-stations, and Driving Their Optimal Service Areas. IEEE. Trans. on Power Apparatus and Systems PAS 94(2), 230–235 (1975)CrossRefGoogle Scholar
  5. 5.
    El-Kady, M.A.: Computer Aided planning of Distribution Sub-station and Primary Feeders. IEEE. Trans. on Power Apparatus and Systems PAS 103(6), 1183–1189 (1984)CrossRefGoogle Scholar
  6. 6.
    Gonen, T., Ramirez-Rosado, I.J.: Optimal Multi Stage Planning of Power Distribution Systems. IEEE Trans. on Power Delivery PWRD-2(2), 512–519 (1987)CrossRefGoogle Scholar
  7. 7.
    Partanen, J.: A Modified Dynamic Programming Algorithm for Sizing, Locating and Timing of Feeder Reinforcements. IEEE Trans. on Power Delivery 5(1), 227–283 (1990)CrossRefGoogle Scholar
  8. 8.
    Khator, S.K., Leung, L.C.: Power Distribution Planning: A review of models and issues. IEEE Trans. Power Syst. 12, 1151–1159 (1997)CrossRefGoogle Scholar
  9. 9.
    Bernal-Agustin, J.L.: Aplicacion de Algoritmos Geneticos al Diseno Optimo de Sistemas de Distribucion de Energia Electrical. Ph.D. dieesrtation, University de Zaragoza, Espana (1998)Google Scholar
  10. 10.
    Boardman, J.T., Meekiff, C.C.: A branch and bound formulation of an electricity distribution planning problem. IEEE Trans. Power App. Syst. 104, 2112–2118 (1985)CrossRefGoogle Scholar
  11. 11.
    Nara, K., et al.: Distribution system expansion planning b multi-stage branch exchange. IEEE Trans. Power Syst. 7, 208–214 (1992)CrossRefGoogle Scholar
  12. 12.
    Carvalho, P.M.S., Ferreira, L.A.F.M.: Optimal distribution network expansion planning under uncertainty by evolutionary decision convergence. Int. J. Elect. Power Energy Syst. 20(2), 125–129 (1998)CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 29–41 (1997)CrossRefGoogle Scholar
  14. 14.
    Diaz Dorado, E., Cidras, J., Miguez, E.: Application of evolutionary algorithms for the planning of urban distribution networks of medium voltage. IEEE Trans. Power Syst. 17, 879–884 (2002)CrossRefGoogle Scholar
  15. 15.
    Chakravorty, S., Ghosh, S.: An Improvised Method for Distribution of Loads and Configuration of Distribution Sub Station. International Journal of Engineering Research and Industrial Applications 2(II), 269–280 (2009)Google Scholar
  16. 16.
    Chakravorty, S., Ghosh, S.: Fuzzy Based Distribution Planning Technique. Journal of Electrical Engineering 9, 38–43 (2009)Google Scholar
  17. 17.
    Chakravorty, S., Ghosh, S.: Distribution Planning Based on Reliability and Contingency Criteria. International Journal of Computer and Electrical Engineering 1(2), 156–161 (2009)Google Scholar
  18. 18.
    Chakravorty, S., Ghosh, S.: A Novel Approach to Distribution Planning in an Unstructured Environment. International Journal of Computer and Electrical Engineering 1(3), 362–367 (2009)Google Scholar
  19. 19.
    Chakravorty, S., Ghosh, S.: A Hybrid Model of Distribution Planning. International Journal of Computer and Electrical Engineering 1(3), 368–374 (2009)Google Scholar
  20. 20.
    Chakravorty, S., Ghosh, S.: Power Distribution Planning Using Multi-Criteria Decision Making Method. International Journal of Computer and Electrical Engineering 1(5), 622–627 (2009)Google Scholar
  21. 21.
    Chakravorty, S., Thukral, M.: Optimal Allocation of Load Using Optimization Technique. In: Proceedings of International Conference CISSE, Bridgeport, USA, pp. 435–437 (2007)Google Scholar
  22. 22.
    Chakravorty, S., Thukral, M.: Choosing Distribution Sub Station Location Using Soft Computing Technique. In: Proceedings of International Conference on Advances in Computing, Communication and Control – 2009, Mumbai, India, pp. 53–55 (2009)Google Scholar
  23. 23.
    Shabbiruddin, Chakravorty, S.: Distribution of Loads and Setting of Distribution Sub Station Using Clustering Technique. In: Proceedings of International Conference on Advances in Computing, Communication and Control–2011, pp. 88–94. Springer, Heidelberg (2011)Google Scholar
  24. 24.
    Shabbiruddin, Chakravorty, S.: Load Distribution Among Distribution Substation and Feeder Routing Using Fuzzy Clustering and Context Aware Decision Algorithm. Journal of Electrical Engineering 11, 57–67 (2011)Google Scholar
  25. 25.
    Shabbiruddin, Kibria, G.: An Efficient Method for Speed Control of DC Shunt Motor using Response Surface Methodology (RSM) Approach. Journal of Control Engineering and Technology 1, 11–16 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Electrical & Electronics Engg.Sikkim Manipal Institute of TechnologyManipalIndia
  2. 2.School of Electronics & Electrical Engg.Lovely Professional UniversityJalandhar-DelhiIndia
  3. 3.Department of Mechanical Engg.Sikkim Manipal Institute of TechnologyManipalIndia

Personalised recommendations