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Accuracy Improvement of Neural Network State Variable Estimator in Induction Motor Drive

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

Some accuracy improvement of the neural network (NN) estimator is proposed in the paper. The estimator approximates stator current components in the rotor flux reference frame. Two approaches are considered: data mining with GMDH algorithm and gradual training of the NN in the desired frequency range. In both cases the accuracy of the estimator is significantly improved. Provided tests confirmed this feature and encourage to implement such an estimator it in a sensorless vector controlled induction motor drive.

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References

  1. Ohyama, K., Asher, G.M., Sumner, M.: Comparison of the practical performance and operating limits of sensorless induction motor drive using a closed loop flux observer and a full order flux observer. In: Proc. EPE 1999, Lausanne, on CD (1999)

    Google Scholar 

  2. Jelonkiewicz, J.: Modified MRAS estimator in sensorless vector control of induction motor. In: XII Symposium PPEE 2007, Wisla 2007, pp. 305–308 (2007)

    Google Scholar 

  3. Sumner, M., Spiteri Staines, C., Gao, Q., Asher, G.: Sensorless Speed Operation of Cage Induc-tion Motor using Zero Drift Feedback Integration with MRAS Observer. In: Proc. EPE 2005, Dresden, on CD (2005)

    Google Scholar 

  4. Vas, P.: Artificial–Intelligence-Based Electrical Machines and Drives. In: Monographs in Electrical and Electronic Engineering nr 45. Oxford University Press, Oxford (1999)

    Google Scholar 

  5. Korbicz J.W, Rutkowski L., Tadeusiewicz R.: Biocybernetyka i Inzynieria Biomedyczna 2000 Tom 6, Sieci Neuronowe, PAN, Akademicka Oficyna Wydawnicza EXIT, Warszawa 2000, pp. 227-255 (2000)

    Google Scholar 

  6. Kuchar, M., Branstetter, P., Kaduch, M.: ANN-based speed estimator for induction motor. In: Proc. EPE-PEMC 2004, Riga, on CD (2004)

    Google Scholar 

  7. Grzesiak, L., Ufnalski, B.: DTC drive with ANN-based stator flux estimator. In: Proc. EPE 2005, Dresden, on CD (2005)

    Google Scholar 

  8. Jelonkiewicz, J., Przybyl, A.: Knowledge extraction from data for neural network state variables estimators in induction motor. In: SENE 2005, Lodz 2005, pp. 211–216 (2005)

    Google Scholar 

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Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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© 2008 Springer-Verlag Berlin Heidelberg

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Jelonkiewicz, J., Przybył, A. (2008). Accuracy Improvement of Neural Network State Variable Estimator in Induction Motor Drive. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_8

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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