Synchronous reluctance machine analytical model optimization and validation through finite element analysis

Degano, Michele; Mahmoud, Hanafy; Bianchi, Nicola; Gerada, Chris

Abstract

Synchronous reluctance motors with rotor flux barriers are a promising type of electric machines thanks to various benefits, among the others, the absence of permanent magnets and rotor excitation. However, these kind of machines has high torque ripple. This is due to the high harmonic content in the magnetomotive force which interacts with the rotor anisotropy. To the aim of minimizing this ripple, a proper selection of the end angles of the flux barriers should be adopted. Air-gap field, the electromagnetic torque and the torque ripple are predicted by an analytical model. An optimization strategy is applied to this fast model to achieve a smooth torque with a suitable rotor geometry. A validation of the proposed method is assessed by comparing the optimization outputs to the re-evaluated FE analysis of the machine. The results considered over a wide range of solutions will show the excellent capability of the analytical model. The final aim of this work is to predict the end barrier angles position as an preliminary starting point for the rotor design of synchronous reluctance machines.

Citation

Degano, M., Mahmoud, H., Bianchi, N., & Gerada, C. (2016, September). Synchronous reluctance machine analytical model optimization and validation through finite element analysis. Presented at 2016 XXII International Conference on Electrical Machines (ICEM 2016), Lausanne, Switzerland

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2016 XXII International Conference on Electrical Machines (ICEM 2016)
• Start Date: Sep 4, 2016
• End Date: Sep 7, 2016
• Online Publication Date: Nov 3, 2016
• Publication Date: Sep 4, 2016
• Deposit Date: Jun 24, 2024
• Peer Reviewed: Peer Reviewed
• Pages: 585-591
• Book Title: 2016 XXII International Conference on Electrical Machines (ICEM 2016)
• ISBN: 9781509025398
• DOI: https://doi.org/10.1109/ICELMACH.2016.7732585
• Public URL: https://nottingham-repository.worktribe.com/output/35446772