Modeling of a switched reluctance motor under stator winding fault condition

H. Chen; W. Yan; Q. Wang; S. Lu; Zhe Chen

doi:10.1109/INTMAG.2015.7157245

Modeling of a switched reluctance motor under stator winding fault condition

H. Chen, W. Yan, Q. Wang, S. Lu, Zhe Chen

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Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

This paper presents a method for modeling inter-turn winding faults in SRM based on artificial neural network (ANN), incorporating a simplified and effective magnetic equivalent circuit method to estimate phase flux-linkages as a function of current and rotor position under inter-turn winding faults.

Original language	English
Title of host publication	2015 IEEE International Magnetics Conference, INTERMAG 2015
Publisher	IEEE
Publication date	14 Jul 2015
Pages	EZ-13
Article number	7157245
ISBN (Print)	978-1-4799-7321-7
DOIs	https://doi.org/10.1109/INTMAG.2015.7157245
Publication status	Published - 14 Jul 2015
Event	2015 IEEE International Magnetics Conference, INTERMAG 2015 - Beijing, China Duration: 11 May 2015 → 15 May 2015

Conference

Conference	2015 IEEE International Magnetics Conference, INTERMAG 2015
Country/Territory	China
City	Beijing
Period	11/05/2015 → 15/05/2015

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10.1109/INTMAG.2015.7157245

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@inproceedings{aa5059d72471421594c239f30c3bb211,

title = "Modeling of a switched reluctance motor under stator winding fault condition",

abstract = "This paper presents a method for modeling inter-turn winding faults in SRM based on artificial neural network (ANN), incorporating a simplified and effective magnetic equivalent circuit method to estimate phase flux-linkages as a function of current and rotor position under inter-turn winding faults.",

author = "H. Chen and W. Yan and Q. Wang and S. Lu and Zhe Chen",

year = "2015",

month = jul,

day = "14",

doi = "10.1109/INTMAG.2015.7157245",

language = "English",

isbn = "978-1-4799-7321-7 ",

pages = "EZ--13",

booktitle = "2015 IEEE International Magnetics Conference, INTERMAG 2015",

publisher = "IEEE",

address = "United States",

note = "2015 IEEE International Magnetics Conference, INTERMAG 2015 ; Conference date: 11-05-2015 Through 15-05-2015",

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TY - GEN

T1 - Modeling of a switched reluctance motor under stator winding fault condition

AU - Chen, H.

AU - Yan, W.

AU - Wang, Q.

AU - Lu, S.

AU - Chen, Zhe

PY - 2015/7/14

Y1 - 2015/7/14

N2 - This paper presents a method for modeling inter-turn winding faults in SRM based on artificial neural network (ANN), incorporating a simplified and effective magnetic equivalent circuit method to estimate phase flux-linkages as a function of current and rotor position under inter-turn winding faults.

AB - This paper presents a method for modeling inter-turn winding faults in SRM based on artificial neural network (ANN), incorporating a simplified and effective magnetic equivalent circuit method to estimate phase flux-linkages as a function of current and rotor position under inter-turn winding faults.

U2 - 10.1109/INTMAG.2015.7157245

DO - 10.1109/INTMAG.2015.7157245

M3 - Article in proceeding

AN - SCOPUS:84942474208

SN - 978-1-4799-7321-7

SP - EZ-13

BT - 2015 IEEE International Magnetics Conference, INTERMAG 2015

PB - IEEE

T2 - 2015 IEEE International Magnetics Conference, INTERMAG 2015

Y2 - 11 May 2015 through 15 May 2015

ER -

Modeling of a switched reluctance motor under stator winding fault condition

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