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Neural Network aided PMSM multi-objective design and optimization for more-electric aircraft applications

Gao, Yuan; Yang, Tao; Bozhko, Serhiy; Wheeler, Pat; Dragicevic, Tomis-Lav; Gerada, Chris

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Authors

Yuan Gao

TAO YANG TAO.YANG@NOTTINGHAM.AC.UK
Professor of Aerospace Electricalsystems

Tomis-Lav Dragicevic



Abstract

This study uses the Neural Network (NN) technique to optimize design of surface-mounted Permanent Magnet Synchronous Motors (PMSMs) for More-Electric Aircraft (MEA) applications. The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space. Based on that, a globally optimal design can be quickly obtained. Matching the analytical estimation with Finite-Element Analysis (FEA) is the main research target of training the NN. Conventional analytical formulae serve as the basis of this study, but they are prone to loss accuracy (especially for a large design space) due to their assumptions and simplifications. With the help of the trained NNs, the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process. The Average Correction Factor (ACF) approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model. Furthermore, a NN aided three-stage-seven-step optimization methodology is proposed. Finally, a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach. In this case, the NN aided analytical model can generate one motor design in 0.04 s while it takes more than 1 min for the used FEA model.

Citation

Gao, Y., Yang, T., Bozhko, S., Wheeler, P., Dragicevic, T.-L., & Gerada, C. (2022). Neural Network aided PMSM multi-objective design and optimization for more-electric aircraft applications. Chinese Journal of Aeronautics, 35(10), 233-246. https://doi.org/10.1016/j.cja.2021.08.006

Journal Article Type Article
Acceptance Date Jun 2, 2021
Online Publication Date Sep 16, 2021
Publication Date 2022-10
Deposit Date Aug 11, 2021
Publicly Available Date Sep 20, 2022
Journal Chinese Journal of Aeronautics
Print ISSN 1000-9361
Electronic ISSN 1000-9361
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 35
Issue 10
Pages 233-246
DOI https://doi.org/10.1016/j.cja.2021.08.006
Keywords Permanent Magnet Synchronous Motor (PMSM); Design and Optimization; Neural Network (NN); Mean Length per Turn (MLT); Loss Estimation; More-Electric Aircraft (MEA)
Public URL https://nottingham-repository.worktribe.com/output/6012785
Publisher URL https://www.sciencedirect.com/science/article/pii/S100093612100279X

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