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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 57 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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

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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  1/2010 - 16

 HIGHLY CITED PAPER 

Cluster Classification of Partial Discharges in Oil-impregnated Paper Insulation

SURESH, S. D. R. See more information about SURESH, S. D. R. on SCOPUS See more information about SURESH, S. D. R. on IEEExplore See more information about SURESH, S. D. R. on Web of Science, USA, S. See more information about USA, S. on SCOPUS See more information about USA, S. on SCOPUS See more information about USA, S. on Web of Science
 
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Download PDF pdficon (668 KB) | Citation | Downloads: 2,065 | Views: 5,021

Author keywords
PDs, multiple PD sources, oil-impregnated paper insulation, PD classification, cluster analysis

References keywords
partial(11), discharge(9), insulation(7), power(6), transformer(5), pattern(5), recognition(4), fuzzy(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 90 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01016
Web of Science Accession Number: 000275458900016
SCOPUS ID: 77954667129

Abstract
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Full text preview
Recognition of multiple partial discharge (PD) sources in high voltage equipment has been a challenging task until now. The work reported here, aims to recognize multiple PD sources in oil-impregnated paper using Cluster Analysis (CA) and Fuzzy Logic (FL). The typical sources of PD in transformer are identified and the corresponding single source PD defect laboratory models are fabricated. From the measured PD signals, the necessary statistical parameters are extracted by applying CA for classification. A Fuzzy based algorithm has been developed to recognize single source PDs. The developed algorithm has also been applied to recognize multiple PD sources.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 6 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 8
View record in SCOPUS®
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Cited-By CrossRef

[1] Influences of Traction Load Shock on Artificial Partial Discharge Faults within Traction Transformer—Experimental Test for Pattern Recognition, Li, Shuaibing, Gao, Guoqiang, Hu, Guangcai, Gao, Bo, Yin, Haojie, Wei, Wenfu, Wu, Guangning, Energies, ISSN 1996-1073, Issue 10, Volume 10, 2017.
Digital Object Identifier: 10.3390/en10101556
[CrossRef]

[2] Comprehensive Evaluation and Application of GIS Insulation Condition Part 1: Selection and Optimization of Insulation Condition Comprehensive Evaluation Index Based on Multi-Source Information Fusion, Tang, Bowen, Tang, Ju, Liu, Yilu, Zeng, Fuping, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2925804
[CrossRef]

[3] Comprehensive Evaluation and Application of GIS Insulation Condition Part 2: Construction and Application of Comprehensive Evaluation Model Considering Universality and Economic Value, Tang, Bowen, Sun, Yuanzhang, Wu, Siying, Gao, Keli, Yan, Xianglian, Zeng, Fuping, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2925802
[CrossRef]

[4] Improving pattern recognition accuracy of partial discharges by new data preprocessing methods, Majidi, Mehrdad, Oskuoee, Mohammad, Electric Power Systems Research, ISSN 0378-7796, Issue , 2015.
Digital Object Identifier: 10.1016/j.epsr.2014.09.014
[CrossRef]

[5] Influences of oil flow speed and temperature on partial discharge properties in transformer oil, Li, Shuaibing, Gao, Bo, Wu, Guangning, 2016 Australasian Universities Power Engineering Conference (AUPEC), ISBN 978-1-5090-1405-7, 2016.
Digital Object Identifier: 10.1109/AUPEC.2016.7749302
[CrossRef]

Updated 2 days, 4 hours ago

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


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