IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5657-d1204090.html
   My bibliography  Save this article

DSVM-Based Model-Free Predictive Current Control of an Induction Motor

Author

Listed:
  • Md Asif Hussain

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Ananda Shankar Hati

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Prasun Chakrabarti

    (Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India)

  • Bui Thanh Hung

    (Data Science Department, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 71409, Vietnam)

  • Vadim Bolshev

    (Laboratory of Intelligent Agricultural Machines and Complexes, Don State Technical University, 344000 Rostov-on-Don, Russia
    Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia)

  • Vladimir Panchenko

    (Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia)

Abstract

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations.

Suggested Citation

  • Md Asif Hussain & Ananda Shankar Hati & Prasun Chakrabarti & Bui Thanh Hung & Vadim Bolshev & Vladimir Panchenko, 2023. "DSVM-Based Model-Free Predictive Current Control of an Induction Motor," Energies, MDPI, vol. 16(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5657-:d:1204090
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5657/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5657/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    2. Prince, & Hati, Ananda Shankar & Kumar, Prashant, 2023. "An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system," Applied Energy, Elsevier, vol. 337(C).
    Full references (including those not matched with items on IDEAS)

    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. Sousa Santos, Vladimir & Cabello Eras, Juan J. & Cabello Ulloa, Mario J., 2024. "Evaluation of the energy saving potential in electric motors applying a load-based voltage control method," Energy, Elsevier, vol. 303(C).
    2. Jie Hou & Gang Nie & Guoqing Li & Wei Zhao & Baoli Sheng, 2023. "Optimization of Branch Airflow Volume for Mine Ventilation Network Based on Sensitivity Matrix," Sustainability, MDPI, vol. 15(16), pages 1-14, August.
    3. Marian Piwowarski & Damian Jakowski, 2023. "Areas of Fan Research—A Review of the Literature in Terms of Improving Operating Efficiency and Reducing Noise Emissions," Energies, MDPI, vol. 16(3), pages 1-28, January.
    4. Prince, & Hati, Ananda Shankar & Kumar, Prashant, 2023. "An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system," Applied Energy, Elsevier, vol. 337(C).

    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:16:y:2023:i:15:p:5657-:d:1204090. 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.