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Loss Minimization Control of Induction Motor Using GA-PSO

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

This paper deals with the GA-PSO (Genetic Algorithm-Particle Swarm Optimization) based vector control for loss minimization operation of induction motor. It is estimated that more than around 50% of the world electric energy generated is consumed by electric machines such as induction motor, DC motor. So, improving efficiency in electric drives is important and control strategy for minimum energy loss is needed as one of optimal operation strategy. In this paper, vector control approach is suggested for an optimal operation of induction motor using GA-PSO tuning method through simulation.

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References

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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, Korea, 305-719

    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. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, D.H., Park, J.I. (2005). Loss Minimization Control of Induction Motor Using GA-PSO. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_30

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  • DOI: https://doi.org/10.1007/11552451_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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