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Power converter fault diagnosis of switched reluctance motor drives using high-frequency signal injection

Nasir Ali, Qiang Gao, Xu Cai, Pavol Makyš et Marek Štulrajter

Communication écrite (2017)

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Abstract

Accurate fault diagnosis, with immediate fault identification and isolation, is of paramount importance for power converters of switched reluctance motor drives, as it allows early adoption of fault tolerant procedures that eliminate the adverse effects of faults on machine operation. This paper presents an online fault diagnostic algorithm for power converter faults in SRM drives based on high frequency voltage signal injection. Unlike the other methods that use additional sensors, this algorithm extracts the fault signatures from the fundamental current by injecting a high frequency voltage signal into the upper switches of asymmetric power converter. The typical four fault types of power transistors are analyzed by monitoring the frequency and amplitude variation of the extracted high frequency current signal. In addition, the variation in amplitude of the fundamental current with the occurrence of fault is also considered as fault signature. Simulations performed with three phase 12/8 SRM drive and results are presented to verify the effectiveness of the proposed diagnostic algorithm.

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Département: Département de génie électrique
ISBN: 9781538611272
URL de PolyPublie: https://publications.polymtl.ca/73325/
Nom de la conférence: 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017)
Lieu de la conférence: Beijing, China
Date(s) de la conférence: 2017-10-29 - 2017-11-01
Maison d'édition: IEEE
DOI: 10.1109/iecon.2017.8216877
URL officielle: https://doi.org/10.1109/iecon.2017.8216877
Date du dépôt: 27 févr. 2026 16:03
Dernière modification: 27 févr. 2026 16:03
Citer en APA 7: Ali, N., Gao, Q., Cai, X., Makyš, P., & Štulrajter, M. (octobre 2017). Power converter fault diagnosis of switched reluctance motor drives using high-frequency signal injection [Communication écrite]. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China. https://doi.org/10.1109/iecon.2017.8216877

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