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

EasyChair Preprint no. 4275

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 electricalmachinesinbothaspectsofcontrolanddesign. Fromthisperspective,awiderangeofenergy 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 havegreatlyinfluencedtheprogressionofthissignificantrealmofenergy. Throughanoveltaxonomy 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.

Keyphrases: AI., Big Data., DL., hybrid models., ML., rotating electrical machines.

@Booklet{EasyChair:4275,
  author = {Adrienn Dineva and Amir Mosavi and Sina Faizollahzadeh Ardabili and Istvan Vajda and Shahaboddin Shamshirband and Timon Rabczuk and Kwok-Wing Chau},
  title = {Review of Soft Computing Models in Design and Control of Rotating Electrical Machines},
  howpublished = {EasyChair Preprint no. 4275},

  year = {EasyChair, 2020}}