Machine Learning Based Correction Model in PMSM Power Loss Estimation for More-Electric Aircraft Applications

Gao, Yuan; Yang, Tao; Wang, Xin; Bozhko, Serhiy; Wheeler, Pat

Abstract

This study utilizes the machine learning (ML) technique to estimate the power loss of surface-mounted Permanent Magnet Synchronous Motor (PMSM) for More-Electric Aircraft (MEA). Existing approaches do not consider ML methods in power loss calculation and only depend on empirical correction factors. The proposed ML aided model is proved to be more precise. Matching the analytical loss estimation with finite-element analysis (FEA) is the main research goal which includes two aspects: iron loss and permanent magnet (PM) loss. They are both based on conventional formulae but this study analyzes the limitation of these equations and the ML correction model can provide dedicated factors for the analytical motor model to make sure that the loss estimation is accurate in the whole motor design space. Average correction factor (ACF) approach is regarded as the comparison method to verify the excellent performance of the proposed ML model.

Citation

Gao, Y., Yang, T., Wang, X., Bozhko, S., & Wheeler, P. (2020, November). Machine Learning Based Correction Model in PMSM Power Loss Estimation for More-Electric Aircraft Applications. Presented at 23rd International Conference on Electrical Machines and Systems, ICEMS 2020, Hamamatsu, Japan

• Presentation Conference Type: Edited Proceedings
• Conference Name: 23rd International Conference on Electrical Machines and Systems, ICEMS 2020
• Start Date: Nov 24, 2020
• End Date: Nov 27, 2020
• Acceptance Date: Jun 26, 2020
• Online Publication Date: Dec 22, 2020
• Publication Date: Nov 24, 2020
• Deposit Date: Jan 8, 2021
• Publicly Available Date: Jan 8, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1940-1944
• Series Title: International Conference on Electrical Machines and Systems (ICEMS)
• Series ISSN: 2640-7841
• Book Title: 2020 23rd International Conference on Electrical Machines and Systems (ICEMS)
• ISBN: 978-1-7281-8930-7
• DOI: https://doi.org/10.23919/ICEMS50442.2020.9290844
• Public URL: https://nottingham-repository.worktribe.com/output/5205334
• Publisher URL: https://ieeexplore.ieee.org/document/9290844
• 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.