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Optimal Power Flow Method Using Evolutionary Programming

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Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

This paper reports on an evolutionary programming based method for solving the optimal power flow problem. The method incorporates an evolutionary programming based load flow solution. To demonstrate the global optimisation power of the new method it is applied to the IEEE30 bus test system with highly non-linear generator input/output cost curves and the results compared to those obtained using the method of steepest descent. The results demonstrate that the new method shows great promise for solving the optimal power flow problem when it contains highly non-linear devices.

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Author information

Authors and Affiliations

  1. Artificial Intelligence and Power Systems Research Group, University of Western Australia, Australia

    Kit Po Wong & Jason Yuryevich

Authors
  1. Kit Po Wong
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  2. Jason Yuryevich
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Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

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© 1999 Springer-Verlag Berlin Heidelberg

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Wong, K.P., Yuryevich, J. (1999). Optimal Power Flow Method Using Evolutionary Programming. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_52

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  • DOI: https://doi.org/10.1007/3-540-48873-1_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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