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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Nov 2018
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Avg review time: 81 days


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

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.

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.

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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  3/2014 - 3

A Novel Approach to Fault Detection in Complex Electric Power Systems

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, WANG, Z. See more information about WANG, Z. on SCOPUS See more information about WANG, Z. on SCOPUS See more information about WANG, Z. 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 (756 KB) | Citation | Downloads: 634 | Views: 1,129

Author keywords
wide area measurement system, wide area backup protection, topology analysis, fault detection, Rayleigh disturbance

References keywords
power(24), systems(15), jijepes(6), fault(6), energy(6), electric(6), wide(5), system(5), research(5), protection(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 27 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03003
Web of Science Accession Number: 000340869800003
SCOPUS ID: 84907377086

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The new type of backup protection can utilize different kinds of information in a larger scale. The research of this paper is focused on the centralized decision and distributed implementation of wide area backup protection system in large-scale power grid. Topology analysis of power network is substantially network connectivity judgment. The operation conditions in case of a failure should be truthfully reflected in the actual structure of network topology, which requires the system failure must be detected promptly and accurately, and prepare for the subsequent adjustment of operation scheme. In the research of this paper, for different kinds of complex system failures, we have put forward a novel fault factor analysis scheme which can realize rapid, accurate and effective fault detection. Many simulations have verified that the fault factor analysis can successfully detect the failures in complex electric power system.

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Cited-By ISI Web of Science

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Cited-By CrossRef

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] New Developments of Characteristic Analysis in Wireless Sensor Networks, Sun, Yi, Zhang, Yagang, IETE Journal of Research, ISSN 0377-2063, Issue 2, Volume 62, 2016.
Digital Object Identifier: 10.1080/03772063.2015.1084897

[2] Wind energy prediction with LS-SVM based on Lorenz perturbation, Zhang, Yagang, Wang, Penghui, Zhang, Chenhong, Lei, Shuang, The Journal of Engineering, ISSN 2051-3305, Issue 13, Volume 2017, 2017.
Digital Object Identifier: 10.1049/joe.2017.0626

[3] Fault Correspondence Analysis in Complex Electric Power Systems, WANG, C., ZHANG, Y., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 15, 2015.
Digital Object Identifier: 10.4316/AECE.2015.01002
[CrossRef] [Full text]

[4] Fault diagnosis and prediction of complex system based on Hidden Markov model, Li, Chen, Wei, Fajie, Wang, Cheng, Zhou, Shenghan, Guirao, Juan L.G., Gao, Wei, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 5, Volume 33, 2017.
Digital Object Identifier: 10.3233/JIFS-169344

[5] New Progress in Wind Prediction Based on Nonlinear Amendment, Zhang, Yagang, Yang, Jingyun, Wang, Kangcheng, Zhao, Zheng, Liu, Jinkang, Wang, Yinding, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, ISBN 978-1-4799-6719-3, 2014.
Digital Object Identifier: 10.1109/BDCloud.2014.13

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

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