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Transformer Core Saturation Fault Analysis using Current Sensor Signals and Thermal Image FeaturesVIDHYA, R.   , VANAJA RANJAN, P. , SHANKER, N. R. |
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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
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. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
