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Cited by 277

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Identification of Machine Parameters of a Synchronous Motor

Rahman, K.M., Hiti, S.·2005·IEEE Transactions on Industry Applications

Abstract

This paper presents a method for estimating the machine parameters of a synchronous motor. The presented method is equally applicable for a wound field synchronous motor, synchronous reluctance motor, or permanent magnet (PM) synchronous motor, both the interior and surface-mount types. The method works particularly well for machines having a significant amount of space harmonics, such as synchronous reluctance and interior PM (IPM) machines, where the harmonics are predominantly the slot harmonics. It is also well suited for surface PM machines operating under saturation, where the harmonics are saturation-induced. The presence of these harmonics makes the parameter identification difficult. Most of the methods presented so far in the literature have failed to properly identify machine parameters in the presence of space harmonics. In this paper, the machine parameters, identified by using a proposed algorithm, are compared with the finite element and the experimental results to demonstrate the effectiveness of the presented method. Both interior PM and surface PM machines are considered.

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Metadata

Authors
Rahman, K. M., & Hiti, S.
Venue
IEEE Transactions on Industry Applications
Published
2005-03
Volume
41
Issue
2
Pages
557-565
ISSN
0093-9994 (ISSN-L)
OpenAlex
W1502250602
Cited by
277
References
1
FWCI
5.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Identification of Machine Parameters of a Synchronous Motor. https://app.cochat.ai/papers/10.1109/tia.2005.844379
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Rahman, K. M., & Hiti, S. (2005). Identification of Machine Parameters of a Synchronous Motor. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2005.844379

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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