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Review of Soft Computing Models in Design and Control of Rotating Electrical Machines

Author

Listed:
  • Adrienn Dineva

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary)

  • Amir Mosavi

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
    Queensland University of Technology (QUT), Centre for Accident Research Road Safety–Queensland (CARRS-Q), 130 Victoria Park Road, Queensland 4059, Australia
    Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany)

  • Sina Faizollahzadeh Ardabili

    (Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Istvan Vajda

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Timon Rabczuk

    (Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia)

  • Kwok-Wing Chau

    (Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

Abstract

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

Suggested Citation

  • Adrienn Dineva & Amir Mosavi & Sina Faizollahzadeh Ardabili & Istvan Vajda & Shahaboddin Shamshirband & Timon Rabczuk & Kwok-Wing Chau, 2019. "Review of Soft Computing Models in Design and Control of Rotating Electrical Machines," Energies, MDPI, vol. 12(6), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1049-:d:215012
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    References listed on IDEAS

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    1. Bahman Najafi & Sina Faizollahzadeh Ardabili & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and," Energies, MDPI, vol. 11(4), pages 1-18, April.
    2. Torrent-Fontbona, F. & López, B., 2016. "Decision support for grid-connected renewable energy generators planning," Energy, Elsevier, vol. 115(P1), pages 577-590.
    3. de Finetti, Bruno, 1985. "Cambridge Probability Theorists," The Manchester School of Economic & Social Studies, University of Manchester, vol. 53(4), pages 348-363, December.
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