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A Study of Frequency Domain Reflectometry Technique for High-Voltage Rotating Machine Winding Condition Assessment
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.ORCID iD: 0000-0001-9128-7825
China Nuclear Power Operation Technology Co., Ltd., Minzu Road 1021, Wuhan 430073, China.ORCID iD: 0000-0001-5469-144X
China Nuclear Power Operation Technology Co., Ltd., Minzu Road 1021, Wuhan 430073, China.
China Nuclear Power Operation Technology Co., Ltd., Minzu Road 1021, Wuhan 430073, China.
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2023 (English)In: Machines, E-ISSN 2075-1702, Vol. 11, no 883Article in journal (Refereed) [Artistic work] Published
Abstract [en]

Detecting and locating local degradations at an incipient stage is very important for mission-critical high-voltage rotating machines. One particular challenge in the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on the scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially created defects. These defects include turn-to-turn short, surface creepage, loose coils, insufficient end-winding spacing, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that FDR can identify and locate structural and insulation degradation in both shielded and unshielded objects with good sensitivity. This makes FDR a complementary technique for machine fault diagnosis and aging assessment.

Place, publisher, year, edition, pages
MDPI AG , 2023. Vol. 11, no 883
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-335395DOI: 10.3390/machines11090883ISI: 001079209100001Scopus ID: 2-s2.0-85172472288OAI: oai:DiVA.org:kth-335395DiVA, id: diva2:1794695
Note

QC 20231102

Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2023-11-02Bibliographically approved

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Paper(27127 kB)644 downloads
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Cheng, JialuTaylor, Nathaniel

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