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Intelligent PID Controller Tuning of AVR System Using GA and PSO

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Book cover Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

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Abstract

This paper deals with applying Euclidian data distance based GA-PSO (Genetic-Particle Swarm Optimization) algorithm (EU-GA-PSO) to PID controller tuning of AVR (Automatic Voltage Regulator). Through this approach, global and local optimal solution can simultaneously achieved for tuning of controller parameter.

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References

  1. Matsummura, S.: Adaptive Control for the Steam Temperature of Thermal Power Plants. In: Proceedings the 1993 IEEE on Control applications, September 1998, pp. 1105–1109 (1998)

    Google Scholar 

  2. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE int. Conf. Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948 (1995)

    Google Scholar 

  3. Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: IEEE Int. Conf., May 4-9, pp. 84–89 (1998)

    Google Scholar 

  4. Juang, C.-F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design, IEEE Trans. Systems, Man and Cybernetics, Part B 34, 997–1006 (2004)

    Article  Google Scholar 

  5. Kim, D.H.: Comparison of PID Controller Tuning of Power Plant Using Immune and genetic algorithm. Measurements and Applications, Ligano, Switzerland, pp. 29-31 (July 2003)

    Google Scholar 

  6. Kim, D.H.: Robust PID Controller Tuning Using Multiobjective Optimization based on Clonal Selection of Immune Algorithm. In: Proc. Int. Conf. Knowledge-based intelligent information and engineering systems, pp. 50–56. Springer, Heidelberg (2004)

    Google Scholar 

  7. Yoshida, H., Kawata, K., Fukuyama, Y.: A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment. IEEE Trans. Power Syst. 15, 1232–1239 (2000)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Instrumentation and Control Eng., Hanbat National University, 16-1 San Duckmyong-Dong Yuseong-Gu, Daejon City, 305-719, Korea

    Dong Hwa Kim & Jin Ill Park

Authors
  1. Dong Hwa Kim
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  2. Jin Ill Park
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Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

    Guang-Bin Huang

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, D.H., Park, J.I. (2005). Intelligent PID Controller Tuning of AVR System Using GA and PSO. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_38

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  • DOI: https://doi.org/10.1007/11538356_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28227-3

  • Online ISBN: 978-3-540-31907-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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