IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4946-d416679.html
   My bibliography  Save this article

Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Author

Listed:
  • Tuan Pham Van

    (Faculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, Vietnam)

  • Dung Vo Tien

    (Faculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, Vietnam)

  • Zbigniew Leonowicz

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Michal Jasinski

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Tomasz Sikorski

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Prasun Chakrabarti

    (Department of Computer Science and Engineering, Techno India NJR Institute of Technology Udaipur, Rajasthan 313003, India
    Data Analytics and Artificial Intelligence Laboratory, Engineering-Technology School, Thu Dau Mot University, Thu Dau Mot City 820000, Vietnam)

Abstract

This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Suggested Citation

  • Tuan Pham Van & Dung Vo Tien & Zbigniew Leonowicz & Michal Jasinski & Tomasz Sikorski & Prasun Chakrabarti, 2020. "Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive," Energies, MDPI, vol. 13(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4946-:d:416679
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4946/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4946/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Teresa Orlowska-Kowalska & Mateusz Korzonek & Grzegorz Tarchala, 2020. "Performance Analysis of Speed-Sensorless Induction Motor Drive Using Discrete Current-Error Based MRAS Estimators," Energies, MDPI, vol. 13(10), pages 1-23, May.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ondrej Lipcak & Filip Baum & Jan Bauer, 2021. "Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation," Energies, MDPI, vol. 14(20), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed G. Mahmoud A. Aziz & Almoataz Y. Abdelaziz & Ziad M. Ali & Ahmed A. Zaki Diab, 2023. "A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques," Energies, MDPI, vol. 16(6), pages 1-32, March.
    2. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4946-:d:416679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.