Volume 16, Issue 1 (March 2020)                   IJEEE 2020, 16(1): 114-121 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Nobahari A, Mosavi M R, Vahedi A. Optimal Shaping of Non-Conventional Permanent Magnet Geometries for Synchronous Motors via Surrogate Modeling and Multi-Objective Optimization Approach. IJEEE 2020; 16 (1) :114-121
URL: http://ijeee.iust.ac.ir/article-1-1366-en.html
Optimal Shaping of Non-Conventional Permanent Magnet Geometries for Synchronous Motors via Surrogate Modeling and Multi-Objective Optimization Approach
A. Nobahari , M. R. Mosavi , A. Vahedi
Abstract:   (3273 Views)
A methodology is proposed for optimal shaping of permanent magnets with non-conventional and complex geometries, used in synchronous motors. The algorithm includes artificial neural network-based surrogate model and multi-objective search based optimization method that will lead to Pareto front solutions. An interior permanent magnet topology with crescent-shaped magnets is also introduced as the case study, on which the proposed optimal shaping methodology is applied. Produced torque per magnets mass and percentage torque ripple are considered as the objectives, in order to take both performance and cost into account. Multi-layer perceptron architecture used to create the approximated model is trained to fit the samples collected via time-stepping finite element simulations. The methodology can be easily generalized to offer a fast and accurate method to optimally define arbitrary permanent magnet shape parameters in various synchronous motors.
Keywords: Permanent Magnet Shaping , Surrogate Model , Artificial Neural Network , Multi-Objective Optimization
Full-Text [PDF 1134 kb]   (1159 Downloads)    
Type of Study: Research Paper |
Received: 2018/10/21 | Revised: 2019/11/11 | Accepted: 2019/11/16
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.