A fuzzy logic controlled genetic algorithm for optimal electrical distribution network reconfiguration

Electrical distribution network reconfiguration is a complex optimization process aimed at finding a radial operating structure that minimizes the system power loss while satisfying operating constraints. This paper presents the application of a fuzzy controlled real coded genetic algorithm to solve the reconfiguration problem. Two controllers are used to adaptively adjust the crossover and mutation probabilities based on the fitness function. Simulation results are presented for the proposed method which is compared to a genetic algorithm with fixed crossover and mutation probabilities.

[1]  John D. McDonald,et al.  Distribution network reconfiguration: single loop optimization , 1996 .

[2]  H. Muhlenbein.  The Breeder Genetic Algorithm-a provable optimal search algorithm and its application , 1994 .

[3]  A. T. Johns,et al.  Environmental/economic dispatch using fuzzy logic controlled genetic algorithms , 1997 .

[4]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[5]  H.C.S. Rughooputh,et al.  A modified genetic algorithm for optimal electrical distribution network reconfiguration , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[6]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[7]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[8]  J. Z. Zhu,et al.  Optimal reconfiguration of electrical distribution network using the refined genetic algorithm , 2002 .

[9]  I. Mareels,et al.  An efficient brute-force solution to the network reconfiguration problem , 2000 .

[10]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[11]  H. Chiang,et al.  Optimal network reconfigurations in distribution systems. I. A new formulation and a solution methodology , 1990 .

[12]  Takeshi Yamakawa,et al.  A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control , 1993, IEEE Trans. Neural Networks.

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[15]  H. Rudnick,et al.  Reconfiguration of electric distribution systems , 1997 .

[16]  S. K. Basu,et al.  A new algorithm for the reconfiguration of distribution feeders for loss minimization , 1992 .