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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 58 days
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PUBLISHER

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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
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2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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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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Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (2,406 KB) | Citation | Downloads: 551 | Views: 330

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.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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