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Artificial Neural Based Speed and Flux Estimators for Induction Machine Drives with Matlab/Simulink

Author

Listed:
  • Ahmed A. Zaki Diab

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61511, Egypt)

  • Mohammed A. Elsawy

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61511, Egypt)

  • Kotin A. Denis

    (Department of Electric Drive and Automation of Industrial Installations, Novosibirsk State Technical University, 630000 Novosibirsk, Russia)

  • Salem Alkhalaf

    (Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 58613, Saudi Arabia)

  • Ziad M. Ali

    (College of Engineering at Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions.

Suggested Citation

  • Ahmed A. Zaki Diab & Mohammed A. Elsawy & Kotin A. Denis & Salem Alkhalaf & Ziad M. Ali, 2022. "Artificial Neural Based Speed and Flux Estimators for Induction Machine Drives with Matlab/Simulink," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1348-:d:796580
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    References listed on IDEAS

    as
    1. Ahmed G. Mahmoud A. Aziz & Hegazy Rez & Ahmed A. Zaki Diab, 2021. "Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives," Mathematics, MDPI, vol. 9(4), pages 1-27, February.
    2. Chitra, A. & Himavathi, S., 2016. "Investigation and analysis of high performance green energy induction motor drive with intelligent estimator," Renewable Energy, Elsevier, vol. 87(P2), pages 965-976.
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    Cited by:

    1. Fahimeh Shiravani & Patxi Alkorta & Jose Antonio Cortajarena & Oscar Barambones, 2022. "An Integral Sliding Mode Stator Current Control for Industrial Induction Motor," Mathematics, MDPI, vol. 10(15), pages 1-20, August.

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