Evolutionary Multiobjective Optimization of a System-Level Motor Drive Design

Cheong, Benjamin; Giangrande, Paolo; Zhang, Xiaochen; Galea, Michael; Zanchetta, Pericle; Wheeler, Patrick

Abstract

The use of optimization algorithms to design motor drive components is increasingly common. To account for component interactions, complex system-level models with many input parameters and constraints are needed, along with advanced optimization techniques. This article explores the system-level optimization of a motor drive design, using advanced evolutionary multiobjective optimization (EMO) algorithms. Practical aspects of their application to a motor drive design optimization are discussed, considering various modelling, search space definition, performance space mapping, and constraints handling techniques. Further, for illustration purposes, a motor drive design optimization case study is performed, and visualization plots for the design variables and constrained performances are proposed to aid analysis of the optimization results. With the increasing availability and capability of modern computing, this article shows the significant advantages of optimization-based designs with EMO algorithms as compared to traditional design approaches, in terms of flexibility and engineering time.

Citation

Cheong, B., Giangrande, P., Zhang, X., Galea, M., Zanchetta, P., & Wheeler, P. (2020). Evolutionary Multiobjective Optimization of a System-Level Motor Drive Design. IEEE Transactions on Industry Applications, 56(6), 6904-6913. https://doi.org/10.1109/tia.2020.3016630

• Journal Article Type: Article
• Acceptance Date: Aug 9, 2020
• Online Publication Date: Aug 13, 2020
• Publication Date: 2020-11
• Deposit Date: Jan 20, 2021
• Publicly Available Date: Jan 20, 2021
• Journal: IEEE Transactions on Industry Applications
• Print ISSN: 0093-9994
• Electronic ISSN: 1939-9367
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 56
• Issue: 6
• Pages: 6904-6913
• DOI: https://doi.org/10.1109/tia.2020.3016630
• Keywords: Control and Systems Engineering; Electrical and Electronic Engineering; Industrial and Manufacturing Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/5021785
• Publisher URL: https://ieeexplore.ieee.org/document/9166700
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.