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Predictive Control Applied to Matrix Converters: A Systematic Literature Review

Author

Listed:
  • Sergio Toledo

    (Faculty of Engineering, Universidad Nacional de Asunción, Luque 2060, Paraguay
    Current address: Campovía y San Antonio, CITEC, Luque 2060, Paraguay.)

  • David Caballero

    (Faculty of Engineering, Universidad Nacional de Asunción, Luque 2060, Paraguay)

  • Edgar Maqueda

    (Faculty of Engineering, Universidad Nacional de Asunción, Luque 2060, Paraguay)

  • Juan J. Cáceres

    (Faculty of Engineering, Universidad Nacional de Asunción, Luque 2060, Paraguay)

  • Marco Rivera

    (Department of Electrical Engineering, Universidad de Talca, Curicó 3340000, Chile)

  • Raúl Gregor

    (Faculty of Engineering, Universidad Nacional de Asunción, Luque 2060, Paraguay)

  • Patrick Wheeler

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

Power electronic devices play an important role in energy conversion. Among the options, matrix converters, in combination with predictive control, represent a good alternative for the power conversion stage. Although several reviews have been undertaken on this topic, they have been conducted in a non-systematic manner, without indicating how the studies considered were chosen. This paper presents results from a systematic literature review on predictive control applied to matrix converters that included 142 primary papers, which were selected after applying a defined protocol with clear inclusion and exclusion criteria. The study provides a detailed classification of predictive control methods and strategies applied to different matrix converter topologies. Research findings require to be understood in combination to develop a common understanding of the topic and ensure that future research effort is based on solid premises. In light of this, this study identifies and characterizes different predictive control techniques and matrix converter topologies through systematic literature review. The results of the review indicate that interest in the area is increasing. A number of open questions in the field are discussed.

Suggested Citation

  • Sergio Toledo & David Caballero & Edgar Maqueda & Juan J. Cáceres & Marco Rivera & Raúl Gregor & Patrick Wheeler, 2022. "Predictive Control Applied to Matrix Converters: A Systematic Literature Review," Energies, MDPI, vol. 15(20), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7801-:d:949626
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    References listed on IDEAS

    as
    1. Jianwei Zhang & Margarita Norambuena & Li Li & David Dorrell & Jose Rodriguez, 2019. "Sequential Model Predictive Control of Three-Phase Direct Matrix Converter," Energies, MDPI, vol. 12(2), pages 1-14, January.
    2. Shuang Feng & Chaofan Wei & Jiaxing Lei, 2019. "Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control," Energies, MDPI, vol. 12(15), pages 1-20, August.
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    Citations

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    Cited by:

    1. Raúl Gregor & Sergio Toledo & Edgar Maqueda & Julio Pacher, 2023. "Part I—Advancements in Power Converter Technologies: A Focus on SiC-MOSFET-Based Voltage Source Converters," Energies, MDPI, vol. 16(16), pages 1-17, August.
    2. Yao Li & Lin Qiu & Xing Liu & Jien Ma & Jian Zhang & Youtong Fang, 2023. "A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters," Energies, MDPI, vol. 16(5), pages 1-13, February.

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