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The Application of a Genetic Algorithm with a Chromosome Limites Life for the Distribution System Loss Minimization Re-Configuration Problem

  • Published : 2007.01.31

Abstract

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic Algorithm (GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transforming problem to minimize load generated load point outage in each sub-section. This approach is one of the most difficult procedures and become combination problems. A new approach using GA was developed for this problem. GA is a general purpose optimization technique based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. Test results for the model system with 24 nodes 29 branches are reported in the paper.

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References

  1. D. Choi, J. Hasegawa 'An Application of Genetic Algorithms to the Distribution System Loss Minimization Re-configuration Problem' IPEC '95 Vol. 2. pp. 436-441 1995
  2. D. Choi, J. Hasegawa 'An Application of Genetic Algorithms to the Network Reconfiguration in Distribution Systems for Loss Minimization and Balancing Problem'. IEEE SICICI '95 pp. 81-86. 2-8 July. 1995
  3. R. Billinton, R. Goel. 'An analytical approach to evaluate probability distribution associated with the reliability indices of electric distribution systems' IEEE Trans. on Power elivery, Vol. PWRD-1, No. 3. pp. 245-252, July 1986
  4. 4Mesut E. Baran, Felix F. Wu, 'Network reconfiguration in distribution systems for loss reduction and load balancing', IEEE Transactions on Power Delivery, Vol. 4, No. 2,1401-1407, April 1989 https://doi.org/10.1109/61.25627
  5. Koichi, Nara, Atusshi. Shiose, Minoru. Kitagawa, Toshihisa Ishihara, 'Implimentation of genetic reduction and load balancing', IEEE Transactions on Power Delivery, Vol. 4, No. 2,1401-1407, April 1989 https://doi.org/10.1109/61.25627
  6. Tsutomu Oyama, 'Restorative planning of power system using genetic algorithm with branch exchange method, Proceedngs of ISAP96. pp.175-179,1996.2
  7. D. E. Goldberg, 'Genetic algorithms in search, optimization and machine learning, Addison-Wesley, 1989
  8. Michalewicz, Genetic Algorithms*Data, structures= Evolution Programs second edition, Springer-Verlag, 1994
  9. D. B. Fogel, 'Introduction to simulated evolutionary optimization', IEEE Trans on Neural Networks, Vol. 5, No.1, pp. 3-14, 1994 https://doi.org/10.1109/72.265956
  10. Branko Soucek, 'Dynamic, Genetic and Chaotic programming, The Sixth-Generation, John Wiley & Sona inc. 1992