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Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming

Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming

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  • Author(s): Y.H. Song 1 ; G.S. Wang 2 ; A.T. Johns 3 ; P.Y. Wang 4
    • View affiliations
    • Affiliations: 1: Department of Electrical Engineering and Electronics, Brunel University, Uxbridge, United Kingdom
      2: Department of Electrical Engineering and Electronics, Westinghouse Systems Limited, Chippenham, United Kingdom
      3: School of Electronic and Electrical Engineering, Power and Energy Systems Group, University of Bath, Bath, United Kingdom
      4: School of Electronic and Electrical Engineering, Power and Energy Systems Group, Electric Power Research Institute, Beijing, Peoples' Republic of China
  • Source: Volume 144, Issue 4, July 1997, p. 345 – 350
    DOI:  10.1049/ip-gtd:19971101 , Print ISSN 1350-2360, Online ISSN 1359-7051
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Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary search technique, evolutionary programming (EP), has been adopted and improved for this particular application. To improve the performance of EP, a fuzzy controlled EP (FCEP), based on heuristic information, is first proposed. The mutation fuzzy controller adaptively adjusts the mutation rate during the simulated evolutionary process. The status of each switch in distribution systems is naturally represented by a binary control parameter 0 or 1. The length of string is much shorter than those proposed by others. A chain-table and combined depth-first and breadth-first search strategy is employed to further speed up the optimisation process. The equality and inequality constraints are imbedded into the fitness function by penalty factors which guarantee the optimal solutions searched by the FCEP are feasible. The implementation of the proposed FCEP for feeder reconfiguration is described in detail. Numerical results are presented to illustrate the feasibility of the proposed FCEP.

Inspec keywords: genetic algorithms; losses; fuzzy logic; energy conservation; distribution networks

Other keywords: penalty factors; breadth-first search strategy; binary control parameter; loss reduction; optimisation; fuzzy controlled evolutionary programming; evolutionary search technique; depth-first search strategy; distribution network reconfiguration; mutation fuzzy controller; energy saving; fitness function; mutation rate adjustment

Subjects: Optimisation techniques; Distribution networks

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