Skip to main content
SpringerLink
Log in

Multi-Objective Optimal Design of Switch Reluctance Motors Using Adaptive Genetic Algorithm

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

Included in the following conference series:

Abstract

In this paper a design methodology based on multi objective genetic algorithm (MOGA) is presented to design the switched reluctance motors with multiple conflicting objectives such as efficiency, power factor, full load torque, and full load current, specified dimension, weight of cooper and iron and also manufacturing cost. The optimally designed motor is compared with an industrial motor having the same ratings. Results verify that the proposed method gives better performance for the multi-objective optimization problems. The results of optimal design show the reduction in the specified dimension, weight and manufacturing cost, and the improvement in the power factor, full load torque, and efficiency of the motor.A major advantage of the method is its quite short response time in obtaining the optimal design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cunka, M.: Design optimization of electric motors by multi-objective fuzzy genetic algorithms. Mathematical and Computational Applications 13(3), 153–163 (2008)

    Article  MathSciNet  Google Scholar 

  2. Xu, J.-X., Xu, J.-X., Zheng, Q.: Multiobjective optimization of current waveforms for switched reluctance motors by genetic algorithm. In: WCCI, vol. 2, pp. 1860–1865 (2002)

    Google Scholar 

  3. Owatchaiphong, S., Fuengwarodsakul, N.H.: Multi-Objective Based Optimization for Switched Reluctance Machines Using Genetic Algorithms. In: 31st Electrical Engineering Conference EECON 2008 (October 2008)

    Google Scholar 

  4. Sahoo, N.C., Xu, J.X., Panda, S.K.: Determination of current waveforms for torque ripple minimisation in switched reluctance motors using iterative learning: an investigation. IEEE Electric Power Application, 369–377 (1999)

    Google Scholar 

  5. Matveev, A.: Development of Methods, Algorithms and Software for Optimal Design of Switched Reluctance Drives, Phd thesis, Technische Universiteit Eindhoven (2006)

    Google Scholar 

  6. Fuengwarodsakul, N.H., Fiedler, J.O., Bauer, S.E., De Doncker, R.W.: New Methodology in Sizing and Predesign of Switched Reluctance Machines Using Normalized Flux-Linkage Diagram. In: Industry Applications Conference, vol. 4, pp. 2704–2711 (October 2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Islamic Azad University, Bandar-Abbas Branch, Bandar-Abbas, Iran

    Mehran Rashidi & Farzan Rashidi

Authors
  1. Mehran Rashidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farzan Rashidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rashidi, M., Rashidi, F. (2010). Multi-Objective Optimal Design of Switch Reluctance Motors Using Adaptive Genetic Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_69

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17563-3_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation