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Design of an Expert System for Distribution Planning System using Soft Computing Techniques

Author

Listed:
  • Shabbir Uddin

    (Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim, India)

  • Amitava Ray

    (Department of Mechanical Engineering, National Institute of Technology, Silchar, India)

  • Karma Sonam Sherpa

    (Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim, India)

  • Sandeep Chakravorty

    (Department of Electrical Engineering, Baddi University of Emerging Science and Technology, Baddi, India)

Abstract

An expert system for distribution planning is proposed in this paper. Choosing a best location of a distribution substation and grouping the various load points to be fed from a particular distribution substation has always been a concern to the distribution planners. Here in this paper the authors present a hybridization of K-means clustering method with fuzzy context aware decision algorithm for choosing the optimum location of distribution substation and its feeder layout. K means clustering has been applied to various loads which are at different location to form a cluster with load points in closer proximity so that a substation could be placed for each cluster for the distribution of power. Fuzzy Context Aware Decision Algorithm based on the Analytical Hierarchy process (AHP) is then applied on each cluster to decide on the feeder layout connecting the load points in each cluster. The feeder layout is based on the various reliability factors and thus the result obtained will lead to optimum feeder path and will hence lower long range distribution expenses.

Suggested Citation

  • Shabbir Uddin & Amitava Ray & Karma Sonam Sherpa & Sandeep Chakravorty, 2016. "Design of an Expert System for Distribution Planning System using Soft Computing Techniques," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 5(2), pages 45-63, April.
    • Handle: RePEc:igg:jeoe00:v:5:y:2016:i:2:p:45-63
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