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Intelligent Identification Methods for Rotor Resistance Parameter of Induction Motor Drive

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 402))

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Abstract

This paper presents two intelligent identification approaches for rotor resistance of an indirect vector controlled induction motor drive. First approach is based on fuzzy logic control (FLC) able to compensate for variations and errors, FLC scheme was employed to overcome the lack of a precise mathematical model of the process. In the second approach is based on artificial neural networks (ANNs), the error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. The performances of the two intelligent approaches are investigated and compared in simulation.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Mohammadia Engineering School, Ibn Sina Avenue, Agdal-Rabat 765, Morocco

    Moulay Rachid Douiri & Mohamed Cherkaoui

Authors
  1. Moulay Rachid Douiri
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  2. Mohamed Cherkaoui
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Editor information

Editors and Affiliations

  1. School of Computing National University of Singapore, 13 Computing Drive, 117417, Singapore

    Gary Tan

  2. Society of Simulation and Gaming of Singapore c/o School of Computing National University of Singapore, 13 Computing Drive, 117417, Singapore

    Gee Kin Yeo

  3. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    Stephen John Turner

  4. School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore

    Yong Meng Teo

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© 2013 Springer-Verlag Berlin Heidelberg

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Douiri, M.R., Cherkaoui, M. (2013). Intelligent Identification Methods for Rotor Resistance Parameter of Induction Motor Drive. In: Tan, G., Yeo, G.K., Turner, S.J., Teo, Y.M. (eds) AsiaSim 2013. AsiaSim 2013. Communications in Computer and Information Science, vol 402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45037-2_24

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  • DOI: https://doi.org/10.1007/978-3-642-45037-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45036-5

  • Online ISBN: 978-3-642-45037-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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