Automated HF Modelling of Induction Machines Considering the Effects of Aging

Leuzzi, Riccardo; Monopoli, Vito Giuseppe; Cupertino, Francesco; Zanchetta, Pericle

Abstract

The use of wide bandgap semiconductor devices, such as SiC and GaN MOSFETs, in high-frequency converters introduces new challenges for the design of electric drives. The very fast switching transient of which these devices are capable, in fact, can become a serious threat for the reliability of the entire system. Electromagnetic interferences due to the high dv/dt and di/dt, voltage reflections along the cable that cause overvoltage and ringing at the motor terminals, and large common-mode voltages that produce current circulation in the motor bearings are recognized as the major phenomena leading to premature failure of the drive. It is therefore important for designers to approach the problem from a system point of view, having the possibility to accurately model the system in the high-frequency domain to take appropriate measures to increase reliability. In this paper, an automated fitting procedure is proposed to identify the high-frequency model of an induction machine, which is based on using a genetic optimization algorithm to find the best rational approximation for the motor characteristics. A series of accelerated electrical aging tests have also been performed on the motors. The results are used iteratively in the proposed fitting procedure to obtain a time-varying model taking into account the aging progression.

Citation

Leuzzi, R., Monopoli, V. G., Cupertino, F., & Zanchetta, P. (2019). Automated HF Modelling of Induction Machines Considering the Effects of Aging. In Proceedings: 2019 IEEE Energy Conversion Congress and Exposition (ECCE) (3117-3122). https://doi.org/10.1109/ECCE.2019.8913299

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2019 IEEE Energy Conversion Congress and Exposition (ECCE)
• Start Date: Sep 29, 2019
• End Date: Oct 3, 2019
• Acceptance Date: May 3, 2019
• Online Publication Date: Nov 28, 2019
• Publication Date: 2019-09
• Deposit Date: Mar 4, 2020
• Publicly Available Date: Mar 9, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 3117-3122
• Series ISSN: 2329-3748
• Book Title: Proceedings: 2019 IEEE Energy Conversion Congress and Exposition (ECCE)
• ISBN: 978-1-7281-0396-9
• DOI: https://doi.org/10.1109/ECCE.2019.8913299
• Keywords: AC machines, automatic modelling, electrical aging, genetic algorithm, high-frequency behavior
• Public URL: https://nottingham-repository.worktribe.com/output/4090811
• Publisher URL: https://ieeexplore.ieee.org/document/8913299
• Additional Information: © 2019 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.