Skip to main content
SpringerLink
Log in

Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors

  • Chapter
  • First Online:
Engineering Applications of Soft Computing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 129))

Abstract

Induction motors represent the main component in most of the industries. Induction motors represent the main component in most of the industries. They use the biggest energy percentages in industrial facilities. This consume depends on the operation conditions of the induction motor imposed by its internal parameters. In this approach, the parameter estimation process is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Çaliş H, Çakir A, Dandil E (2013) Artificial immunity-based induction motor bearing fault diagnosis. Turk J Electr Eng Comput Sci 21(1):1–25

    Google Scholar 

  2. Prakash V, Baskar S, Sivakumar S, Krishna KS (2008) A novel efficiency improvement measure in three-phase induction motors, its conservation potential and economic analysis. Energy Sustain Dev 12(2):78–87

    Article  Google Scholar 

  3. Waters SS, Willoughby RD (1983) Modeling induction motors for system studies. IEEE Trans Ind Appl IA-19(5):875–878

    Article  Google Scholar 

  4. Ansuj S, Shokooh F, Schinzinger R (1989) Parameter estimation for induction machines based on sensitivity analysis. IEEE Trans Ind Appl 25(6):1035–1040

    Article  Google Scholar 

  5. De Kock J, Van der Merwe F, Vermeulen H (1994) Induction motor parameter estimation through an output error technique. IEEE Trans Energy Convers 9(1):69–76

    Article  Google Scholar 

  6. Mohammadi HR, Akhavan A (2014) Parameter estimation of three-phase induction motor using hybrid of genetic algorithm and particle swarm optimization, J Eng, vol 2014

    Google Scholar 

  7. Bishop RR, Richards GG (1990) Identifying induction machine parameters using a genetic optimization algorithm. In: Proceedings on IEEE South east conference, vol 2, pp 476–479

    Google Scholar 

  8. Lindenmeyer D, Dommel HW, Moshref A, Kundur P (2001) An induction motor parameter estimation method. Int J Electr Power Energy Syst 23(4):251–262

    Article  Google Scholar 

  9. Sakthivel VP, Bhuvaneswari R, Subramanian S (2010) An improved particle swarm optimization for induction motor parameter determination. Int J Comput Appl 1(2):71–76

    Google Scholar 

  10. Sakthivel VP, Bhuvaneswari R, Subramanian S (2010) Artificial immune system for parameter estimation of induction motor. Expert Syst Appl 37(8):6109–6115

    Article  Google Scholar 

  11. Sakthivel VP, Bhuvaneswari R, Subramanian S (2011) An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm. Meas J Int Meas Confed 44(4):674–684

    Article  Google Scholar 

  12. Perez I, Gomez-Gonzalez M, Jurado F (2013) Estimation of induction motor parameters using shuffled frog-leaping algorithm. Electr Eng 95(3):267–275

    Article  Google Scholar 

  13. Abro AG, Mohamad-Saleh J (2014) Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation. Turk J Electr Eng Comput Sci 22:620–636

    Article  Google Scholar 

  14. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179(13):2232–2248

    Article  MATH  Google Scholar 

  15. Farivar F, Shoorehdeli MA (2016) Stability analysis of particle dynamics in gravitational search optimization algorithm. Inf Sci 337:25–43

    Article  Google Scholar 

  16. Yazdani S, Nezamabadi-pour H, Kamyab S (2014) A gravitational search algorithm for multimodal optimization. Swarm Evol Comput 14:1–14

    Article  Google Scholar 

  17. Beigvand SD, Abdi H, La Scala M (2016) Combined heat and power economic dispatch problem using gravitational search algorithm. Electr Power Syst Res 133:160–172

    Article  Google Scholar 

  18. Kumar V, Chhabra JK, Kumar D (2014) Automatic cluster evolution using gravitational search algorithm and its application on image segmentation. Eng Appl Artif Intell 29:93–103

    Article  Google Scholar 

  19. Zhang W, Niu P, Li G, Li P (2013) Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowl Based Syst 39:34–44

    Article  Google Scholar 

  20. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University Press, Erciyes, p 10

    Google Scholar 

  21. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  22. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, vol 4, pp 1942–1948

    Google Scholar 

  23. Sakthivel VP, Subramanian S (2011) On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization. Energy 36(3):1713–1720

    Article  Google Scholar 

  24. Jamadi M, Merrikh-Bayat F (2014) New method for accurate parameter estimation of induction motors based on artificial bee colony algorithm. arXiv:1402.4423

    Google Scholar 

  25. Ursem RK, Vadstrup P (2003) Parameter identification of induction motors using differential evolution. In: The 2003 congress on evolutionary computation, CEC’03, vol 2, pp 790–796

    Google Scholar 

  26. Kotz S, Johnson NL (eds) (1992) Breakthroughs in statistics: methodology and distribution. Springer, New York, NY, USA, pp 196–202

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Margarita-Arimatea Díaz-Cortés & Raúl Rojas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas

Authors
  1. Margarita-Arimatea Díaz-Cortés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raúl Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Margarita-Arimatea Díaz-Cortés .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Díaz-Cortés, MA., Cuevas, E., Rojas, R. (2017). Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors. In: Engineering Applications of Soft Computing. Intelligent Systems Reference Library, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-57813-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57813-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57812-5

  • Online ISBN: 978-3-319-57813-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation