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

Adrienn Dineva, Amir Mosavi, Sina Faizollahzadeh Ardabili, Istvan Vajda, Shahaboddin Shamshirband, Timon Rabczuk and Kwok-Wing Chau
Additional contact information
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
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
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

Energies, 2019, vol. 12, issue 6, 1-28

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.

Keywords: soft computing; artificial intelligence; machine learning; rotating electrical machines; energy systems; deep learning; electric vehicles; big data; hybrid models; ensemble models; energy informatics; electrical engineering; computational intelligence; data science; energy management; control; electric motor drives (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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