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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2023 - 8

Transformer Core Saturation Fault Analysis using Current Sensor Signals and Thermal Image Features

VIDHYA, R. See more information about VIDHYA, R. on SCOPUS See more information about VIDHYA, R. on IEEExplore See more information about VIDHYA, R. on Web of Science, VANAJA RANJAN, P. See more information about  VANAJA RANJAN, P. on SCOPUS See more information about  VANAJA RANJAN, P. on SCOPUS See more information about VANAJA RANJAN, P. on Web of Science, SHANKER, N. R. See more information about SHANKER, N. R. on SCOPUS See more information about SHANKER, N. R. on SCOPUS See more information about SHANKER, N. R. on Web of Science
 
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Download PDF pdficon (2,406 KB) | Citation | Downloads: 550 | Views: 329

Author keywords
condition monitoring, fault detection, image processing, thermal analysis, feature extraction

References keywords
transformer(19), power(14), current(12), transformers(8), saturation(8), inrush(7), deliv(7), type(5), faults(5), analysis(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-11-30
Volume 23, Issue 4, Year 2023, On page(s): 69 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.04008
Web of Science Accession Number: 001147490000006
SCOPUS ID: 85182241078

Abstract
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Transformer faults are identified and classified using current sensor signals. Transformer core saturation detection is challenging using current sensor signals due to overlapping of load-based faults such as overload, short circuit, grounding faults, in-rush and power supply fluctuations in current sensor signals. Existing methods are unable to differentiate load-based fault and Transformer core-based faults from current sensor signals. In this paper, transformer core-based faults such as overheating, voltage regulation issues due to power fluctuation, increased current draw due to short circuit or overload are differentiated from load-based faults, using current sensor signal energy band and thermal image of current sensor which are acquired simultaneously. In this paper, transformer core-based faults are differentiated from load-based faults after the current signals are processed with Modified- Tunable Q-factor Wavelet Transform and Rational Dilation Wavelet Transform and current sensor thermal images are processed with Multi Resolution wavelet - Deep Convolutional Neural Network. Energy band-based values from current sensor signal and current sensor thermal image Haralick features are used for differentiating transformer core-based and load-based faults. From the experimental and simulation results the transformer core-based and load-based faults are detected with an accuracy of 95% and compared with traditional methods.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 586 TCR
SCOPUS® Citations for all references: 750 TCR

Web of Science® Average Citations per reference: 21 ACR
SCOPUS® Average Citations per reference: 27 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-25 16:10 in 181 seconds.




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