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Speed Control for Multi-phase Induction Machine Fed by Multi-level Converters Using New Neuro-Fuzzy

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Renewable Energy for Smart and Sustainable Cities (ICAIRES 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 62))

Abstract

This paper proposes a novel neuro-fuzzy control (NFC) for multi-phase induction machine (MPIM) fed by two multi-level converters (MLC) using venturing modulation algorithm. A four-layer artificial neural network (ANN) structure is utilized to train the parameters of the fuzzy logic controller (FLC) based on the minimization of the square of the error. In the proposed method indirect field oriented control (IFOC) is applied to the MPIM. The results are compared with the results obtained from a proportional–integral (PI) controller. Simulation results obtained are very satisfactory and showed that neuro-fuzzy control performance is enhanced using two multi-level converters (MLC) with introduces load disturbances.

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Author information

Authors and Affiliations

  1. Ecole Militaire Polytechnique, LCM-UER-ELT, Bordj El-Bahri, 16046, Algeria

    E. Zaidi, K. Marouani & H. Bouadi

Authors
  1. E. Zaidi
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  2. K. Marouani
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  3. H. Bouadi
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Corresponding author

Correspondence to E. Zaidi .

Editor information

Editors and Affiliations

  1. EPST-CDER, Unité de Développement des Equipements Solaires, Bou-Ismail, Algeria

    Mustapha Hatti

Appendix 1

Appendix 1

See Table 2.

Table 2. Dual star induction machine parameters for simulation
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Zaidi, E., Marouani, K., Bouadi, H. (2019). Speed Control for Multi-phase Induction Machine Fed by Multi-level Converters Using New Neuro-Fuzzy. In: Hatti, M. (eds) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018. Lecture Notes in Networks and Systems, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-030-04789-4_49

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