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Design of Optimal Power Distribution Networks Using Multiobjective Genetic Algorithm

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KI 2005: Advances in Artificial Intelligence (KI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3698))

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Abstract

This paper presents solution of optimal power distribution networks problem of large-sized power systems via a genetic algorithm of real type. The objective is to find out the best power distribution network reliability while simultaneously minimizing the system expansion costs. To save an important CPU time, the constraints are to be decomposing into active constraints and passives ones. The active constraints are the parameters which enter directly in the cost function and the passives constraints are affecting the cost function indirectly as soft limits. The proposed methodology is tested for real distribution systems with dimensions that are significantly larger than the ones frequently found in the literature. Simulation results show that by this method, an optimum solution can be given quickly. Analyses indicate that proposed method is effective for large-scale power systems. Further, the developed model is easily applicable to n objectives without increasing the conceptual complexity of the corresponding algorithm and can be useful for very large-scale power system.

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Author information

Authors and Affiliations

  1. Azad University of Tehran-South Branch, Tehran, Iran

    Alireza Hadi & Farzan Rashidi

Authors
  1. Alireza Hadi
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  2. Farzan Rashidi
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Artificial Intelligence Research Group, University of Koblenz-Landau,, Universitätsstr. 1, 56070, Koblenz

    Ulrich Furbach

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© 2005 Springer-Verlag Berlin Heidelberg

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Hadi, A., Rashidi, F. (2005). Design of Optimal Power Distribution Networks Using Multiobjective Genetic Algorithm. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_17

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  • DOI: https://doi.org/10.1007/11551263_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28761-2

  • Online ISBN: 978-3-540-31818-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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