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Power Electronic Circuits Design: A Particle Swarm Optimization Approach

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Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

The development of power electronics results in a growing need for automatic design and optimization for power electronic circuits (PECs). This paper presents a particle swarm optimization (PSO) approach for the PECs design. The optimization problem is divided into two processes using a decoupled technique and PSO is employed to optimize the values of the circuit components in the power conversion stage (PCS) and the feedback network (FN), respectively. A simple mutation operator is also incorporated into PSO to enhance the population diversity. The algorithm is applied to the optimization of a buck regulator for meeting requirements under large-signal changes and at steady state. Compared with genetic algorithm (GA), PSO can yield more optimized values of circuit components with lower computational effort.

This work was supported by NSF of China Project No.60573066 and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, P.R. China.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sun Yat-sen University, China, 510275

    Jun Zhang, Yuan Shi & Zhi-Hui Zhan

Authors
  1. Jun Zhang
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  2. Yuan Shi
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  3. Zhi-Hui Zhan
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhang, J., Shi, Y., Zhan, ZH. (2008). Power Electronic Circuits Design: A Particle Swarm Optimization Approach. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_61

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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