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A technique for improving the convergence characteristic of genetic algorithms and its application to a genetic-based load flow algorithm

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Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

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Abstract

This paper is devoted to the development of a technique for the enhancement of the convergence of genetic algorithms. Based on the concept of solution acceleration, a technique is proposed and applied to a constrained-genetic-algorithm load-flow algorithm CGALF recently developed for solving the problem of evaluating the voltage profile and power flow in electric power networks. The enhanced CGALF algorithm is applied to a practical power system to illustrate the effectiveness of the developed method.

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References

  1. MICHALEWICZ, Z.: A survey of constraint handling techniques in evolutionary computation methods, Proc. of Fourth Annual Conference on Evolutionary Programming, MIT Press, 1995.

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  2. MICHALEWICZ, Z.: Genetic algorithms, numerical optimisation, and constraints, Proc. of Fourth Annual Conference on Evolutionary Programming, MIT Press, 1995.

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  3. MICHALEWICZ, Z. and JANIKOW, C.: Handling constraints in genetic algorithms, Proc. of Fourth Conference on Genetic Algorithms, 1991, pp. 151–157, Morgan Kaufmann, California.

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  4. WONG, K.P. and LI, A.: ‘A method to handle constraints in genetic-algorithm load flow in electric power systems', Proc. 1st Australia-Korea Joint Workshop on Evolutionary Computation, Taejon, Korea, September, 1995, pp.229–245.

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  5. WONG, K.P. and LI, A.: 'solving the load flow problem using genetic algorithm', IEEE Conf. Proc. on Evolutionary Computing (ICEC'95), Perth, Australia, November, 1995, pp.103–108.

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  6. GRAINGER, J.J. and STEVENSON, JR. W.D.: Power System Analysis, McGrawHill, Inc, 1994.

    Google Scholar 

  7. KLOS, A. and KERNER, A.: ‘The non-uniqueness of load flow solutions', Proc. 5th Power System Computation Conf. (PSCC), Cambridge, UK, July 1975, V.3.1/8.

    Google Scholar 

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Authors and Affiliations

  1. Artificial Intelligence and Power Systems Research Group Department of Electrical and Electronic Engineering, University of Western Australia, 6907, Nedlands, Western Australia

    Kit Po Wong & An Li

Authors
  1. Kit Po Wong
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  2. An Li
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Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Wong, K.P., Li, A. (1997). A technique for improving the convergence characteristic of genetic algorithms and its application to a genetic-based load flow algorithm. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028533

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  • DOI: https://doi.org/10.1007/BFb0028533

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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