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JCR Impact Factor: 0.699
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Issues per year: 4
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Next issue: Feb 2019
Avg review time: 79 days


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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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2018 - 9

Monofractal and Multifractal Analysis of Discharge Signals in Transformer Pressboards

CEKLI, S. See more information about CEKLI, S. on SCOPUS See more information about CEKLI, S. on IEEExplore See more information about CEKLI, S. on Web of Science, UZUNOGLU, C. P. See more information about  UZUNOGLU, C. P. on SCOPUS See more information about  UZUNOGLU, C. P. on SCOPUS See more information about UZUNOGLU, C. P. on Web of Science, UGUR, M. See more information about UGUR, M. on SCOPUS See more information about UGUR, M. on SCOPUS See more information about UGUR, M. on Web of Science
 
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 (1,323 KB) | Citation | Downloads: 134 | Views: 384

Author keywords
partial discharges, power transformers, fractals, acoustic sensors, power quality

References keywords
board(12), insulation(10), fractal(9), discharge(9), analysis(7), voltage(6), partial(6), higuchi(5), high(5), electric(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02009
Web of Science Accession Number: 000434245000009
SCOPUS ID: 85047883421

Abstract
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Pressboards are commonly used as insulating materials employed in electrical connections of transformers. Pressboards are typically made from vegetable fibers, which contain cellulose. The proper operation of power transformer depends mainly on constant monitoring of insulation materials against failure. Due to the complex and close structure of power transformers, it is very challenging task to detect failure and hence possible location of degradation of pressboard internally. Generated discharge signals may result in breakdown of system insulation and system failure. In this study, the investigation of insulation degradation is fulfilled by analyzing discharge signals and simultaneously produced acoustic signals during discharges. For this purpose, a test setup is used for investigating discharge signals of pressboard samples under different electrical stresses. This paper proposes monofractal and multifractal analysis of discharge and acoustic signals of pressboards. The Higuchi's method is an effective monofractal analysis tool for measurement of fractal dimension of self-affine signals, which is proposed for online monitoring of discharge signals of pressboards. In order to investigate obtained discharge signals with accelerated fluctuations effectively, multifractal detrended fluctuation analysis is proposed for these signals, which exhibit nonlinear behavior.


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

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References Weight

Web of Science® Citations for all references: 5,218 TCR
SCOPUS® Citations for all references: 5,934 TCR

Web of Science® Average Citations per reference: 141 ACR
SCOPUS® Average Citations per reference: 160 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 2019-02-15 17:13 in 242 seconds.




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