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Detection and location of interturn short circuits in the stator windings of operating motors

Penman, J., Sedding, H.G., Lloyd, B.A., Fink, W.T.·1994·IEEE Transactions on Energy Conversion

Abstract

One major cause of motor failures is breakdown of the turn insulation leading to puncture of the groundwall. Early detection of interturn shorts during motor operation would eliminate consequential damage to adjacent coils and the stator core reducing repair costs and motor outage time. In addition to the benefits gained from early detection of turn insulation breakdown, significant advantages would accrue by locating the faulted coil within the stator winding. Fault location would not only increase the speed of the repair, but would also permit more optimal scheduling of the repair outage. This work was successful in practically implementing a theory to predict changes in the axial leakage flux resulting from stator winding interturn shorts and in developing an algorithm to locate the position of the faulted coil. An experimental setup consisting of a 200 hp motor loaded by a generator was used to validate this theory. Suitable transducers were developed and installed on this motor. Measurement using this experimental configuration clearly validated the theoretical model. On the basis of this experimental work an instrument to continuously monitor for shorted turns is under development.>

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Metadata

Authors
Penman, J., Sedding, H. G., Lloyd, B. A., & Fink, W. T.
Venue
IEEE Transactions on Energy Conversion
Published
1994
Volume
9
Issue
4
Pages
652-658
ISSN
0885-8969 (ISSN-L)
OpenAlex
W2142632891
Cited by
394
References
0
FWCI
0.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Detection and location of interturn short circuits in the stator windings of operating motors. https://app.cochat.ai/papers/10.1109/60.368345
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Penman, J., Sedding, H. G., Lloyd, B. A., & Fink, W. T. (1994). Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Transactions on Energy Conversion. https://doi.org/10.1109/60.368345

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.