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Interturn Fault Detection in Induction Machines Based on High-Frequency Injection
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-6539-9265
R&D group at LumenRadio, Göteborg, 416 68, Sweden.ORCID iD: 0000-0002-8991-3580
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0001-6831-3474
2023 (English)In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 70, no 10, p. 10639-10647Article in journal (Refereed) Published
Abstract [en]

An interturn short-circuit in the stator windings can lead to the breakdown of electrical machines. In the case of induction machines, several fault detection methods and faulted models have been developed in the recent decades. These models differ mainly in how the leakage inductances of the faulted winding are modeled. This work provides a generalized model for interturn short-circuit faults, using different assumptions for the leakage inductances. The model is validated with experimental results for an exhaustive set of fault parameters, and the leakage inductances influence is analyzed. Moreover, the model is used to analyze the drawbacks of the negative-sequence fundamental current as a traditional fault signature. A high-frequency injection method for converter-fed machines is presented to overcome these limits. The proposed fault signature is the negative-sequence current at the injection frequency and it is evaluated experimentally at different operating conditions. The fault severity and its location are proved to be related to the proposed fault signature.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. Vol. 70, no 10, p. 10639-10647
Keywords [en]
Circuit faults, Stator windings, Mathematical models, Windings, Inductance, Rotors, Fault detection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-322828DOI: 10.1109/tie.2022.3217590ISI: 000975423100088Scopus ID: 2-s2.0-85141589867OAI: oai:DiVA.org:kth-322828DiVA, id: diva2:1724489
Note

Not duplicate with DiVA 1657021

QC 20231122

Available from: 2023-01-08 Created: 2023-01-08 Last updated: 2025-03-27Bibliographically approved

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fulltext(2342 kB)235 downloads
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Zanuso, GiovanniKumar, Sathiya Lingam SenthilPeretti, Luca

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