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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
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    References listed on IDEAS

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    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).
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