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Estimate Parameters of Induction Motor Using ANN and GA Algorithm

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AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application (AETA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 465))

Abstract

This paper presents methods for estimating induction motor parameters such as stator resistance, stator inductance, rotor inductance, rotor time constant… by artificial neural network (ANN) and genetic algorithm (GA). The first part is the mathematical basis for estimating motor parameters by neural and genetic algorithms, the second part is the motor model for data collection for estimation, the third part is simulation and As a result of the simulation, the results show that it is possible to accurately estimate the parameters of the induction motor by ANN or GA algorithm.

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References

  1. Brandstetter, P., Dobrovsky, M., Kuchar, M.: Implementation of genetic algorithm in control structure of induction motor A.C. Drive. Adv. Electr. Comput. Eng. 14(4), 15–20 (2014)

    Article  Google Scholar 

  2. Datta, M., Rafiq, M.A., Ghosh, B.C.: Genetic algorithm based fast speed response induction motor drive without speed encoder, POWERENG 2007, Setubal, Portugal (2007)

    Google Scholar 

  3. Chandrakala, K.R.M.V., Balamurugan, S., Sankaranarayanan, K.: Genetic algorithm tuned optimal variable structure system controller for enhanced load frequency control. Int. Rev. Electr. Eng.-IREE 7(2), 4105–4112 (2012)

    Google Scholar 

  4. Brandstetter, P.: Sensorless control of induction motor using modified MRAS. Int. Rev. Electr. Eng.- IREE 7(3), 4404–4411 (2012)

    Google Scholar 

  5. Saghafinia, A., Ping, H.W., Rahman, M.A.: High performance induction motor drive using hybrid fuzzy-PI and PI controllers: a review. Int. Rev. Electr.-Eng. IREE 5(5), 2000–2012 (2010)

    Google Scholar 

  6. Girovsky, P., Timko, J., Zilkova, J.: Shaft sensor-less FOC control of an induction motor using neural estimators. Acta Polytechnica Hungarica 9(4), 31–45 (2012)

    Google Scholar 

  7. Rajasekhar,A., Abraham, A., Jatoth, R.K.: Controller tuning using a cauchy mutated artificial bee colony algorithm. In: Advances in Intelligent and Soft Computing. Springer Verlag Berlin, vol. 87, pp. 11–18 (2011)

    Google Scholar 

  8. Brandstetter, P., Krecek, T.: Speed and current control of permanent magnet synchronous motor drive using IMC controllers. Adv. Electr. Comput. Eng. 12(4), 3–10 (2012)

    Article  Google Scholar 

  9. Ben Regaya, C., Zaafouri, A., Chaari, A.: Electric drive control with rotor resistance and rotor speed observers based on fuzzy logic. Math. Prob. Eng. 2014, 9 (2014). Hindawi Publishing Corporation

    Article  Google Scholar 

  10. Megherbi, A.C., Megherbi, H., Benmahamed, K., Aissaoui, A.G., Tahour, A.: Parameter identification of induction motors using variable-weighted cost function of genetic algorithms. J. Electr. Eng. Technol. 5(4), 597–605 (2010)

    Article  Google Scholar 

  11. Timer, J., Adžic, E., Porobic, V., Marcetic, D.: Influence of rotor time constant error on IFOC control structure. ELECTRONICS 13(1), 43 (2009)

    Google Scholar 

  12. Eissa, M.M., Virk, G.S., AbdelGhany, A.M., Ghith, E.S.: Optimum induction motor speed control technique using genetic algorithm. Am. J. Intell. Syst. 3(1), 1–12 (2013)

    Google Scholar 

  13. Chacko, S., Bhende, C.N., Jain, S., Nema, R.K.: Rotor resistance estimation of vector controlled induction motor drive using GA/PSO tuned fuzzy controller. Int. J. Electr. Eng. Inform. 8(1), 218 (2016)

    Google Scholar 

  14. Brandstetter, P., Chlebis, P., Palacky, P., Skuta, O.: Application of RBF network in rotor time constant adaptation. Electron. Electr. Eng./Elektronika Ir. Elektrotechnika 113(7), 21–26 (2011)

    Google Scholar 

  15. Tran, T.C., Brandstetter, P., Duy, V.H., Vo, H.H., Dong, C.: PID speed controller optimization using online genetic algorithm for induction motor drive. In: AETA 2016: Recent Advances in Electrical Engineering and Related Sciences. Book Series: Lecture Notes in Electrical Engineering, vol. 415, pp. 564–576 (2017)

    Google Scholar 

  16. Chacko, S., Bhende, C.N., Jain, S., Nema, R.K.: A novel rotor resistance estimation technique for vector controlled induction motor drive using TS fuzzy. Int. J. Power Electron. Drive Syst. (IJPEDS) 6(3), 538–553 (2015)

    Article  Google Scholar 

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Acknowledgement

The paper was supported by the projects: Center for Intelligent Drives and Advanced Machine Control (CIDAM) project, reg. no. TE02000103 funded by the Technology Agency of the Czech Republic, project reg. no. SP2017/104 funded by the Student Grant Competition of VSB-Technical University of Ostrava.

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Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, 19 Nguyen Huu Tho, Dist. 7, Ho Chi Minh City, Vietnam

    Thinh Cong Tran, Vo Hoang Duy, Chau Dong, Cuong Dinh Tran & Sang Dang Ho

  2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. Listopadu 15/2172, 708 33, Ostrava-Poruba, Czech Republic

    Pavel Brandstetter

Authors
  1. Thinh Cong Tran
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  2. Pavel Brandstetter
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  3. Vo Hoang Duy
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  4. Chau Dong
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  5. Cuong Dinh Tran
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  6. Sang Dang Ho
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Corresponding author

Correspondence to Thinh Cong Tran .

Editor information

Editors and Affiliations

  1. Faculty of Electrical - Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Vo Hoang Duy

  2. International Cooperation, Research and Training Institute, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tran Trong Dao

  3. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB-TUO, Ostrava, Czech Republic

    Ivan Zelinka

  4. Department of Mechanical Design Engineering, Pukyong National University, Busan, Korea (Republic of)

    Sang Bong Kim

  5. Faculty of Electrical - Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tran Thanh Phuong

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Tran, T.C., Brandstetter, P., Duy, V.H., Dong, C., Tran, C.D., Ho, S.D. (2018). Estimate Parameters of Induction Motor Using ANN and GA Algorithm. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds) AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2017. Lecture Notes in Electrical Engineering, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-69814-4_84

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  • DOI: https://doi.org/10.1007/978-3-319-69814-4_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69813-7

  • Online ISBN: 978-3-319-69814-4

  • eBook Packages: EngineeringEngineering (R0)

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