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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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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 in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

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  1/2009 - 11

 HIGH-IMPACT PAPER 

Clustering Techniques in Load Profile Analysis for Distribution Stations

BOBRIC, E. C., CARTINA, G., GRIGORAS, G.
 
View the paper record and citations in View the paper record and citations in Google Scholar
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Download PDF pdficon (501 KB) | Citation | Downloads: 1,237 | Views: 7,369

Author keywords
load profile, clustering techniques, data flow analysis, power consumption, distribution station

References keywords
power(5), load(5), clustering(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-02-03
Volume 9, Issue 1, Year 2009, On page(s): 63 - 66
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.01011
Web of Science Accession Number: 000264815300011
SCOPUS ID: 67749137135

Abstract
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Full text preview
The demand characteristic is the most important one in analyzing customer information. In a distribution network, there is in any moment certain degree of uncertainty about busses loads, and consequently, about load level of network, busses voltage level, and power losses. Therefore, it is very important to estimate first of all the load profiles of buses, using available data (measurements effectuated in distribution stations). The results obtained for various distribution stations demonstrate the effectiveness of the present method in overcoming the difficulties encountered in optimal planning and operation of distribution networks.


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

[1] Load profiles and their use in electricity settlement, Electricity Association, Publisher UKERC, 1997

[2] "Handbook of Applied Multivariate Statistics and Mathematical Modelling", Edited by: Howard E.A. Tinsley and Steven D. Brown ISBN: 978-0-12-691360-6 [PermaLink]

[3] Gh. Cartina, Gh. Grigoras., E.C. Bobric, "Clustering Techniques in Load Analyse", Proc. of the International Power Systems Conference, PSC'05, 2005, Timisoara, Romania, pp. 123-130

[4] R.F. Chang, C.N. Lu, "Load profiling and its applications in power market", Power Engineering Society General Meeting, 2003, IEEE Volume 2, 13-17 July 2003,
[CrossRef] [Web of Science Times Cited 3]


[5] C. Nitu, A. S. Dobrescu, "The Role of Weather Indicators in Energy Consumption", Advances in Electrical and Computer Engineering, Suceava, Romania, ISSN 1582-7445, No 1/2008, volume 8, pp. 17-20
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[6] JMP Statistics and Graphics Guide: Version 3, SAS Institute Inc., Cary, NC, USA, 1999

[7] A.K. Jain, M.N. Murty, P.J. Flynn, Data Clustering: A Rewiew, ACM Computing Serveys, 31, 264-323, 1999
[CrossRef] [Web of Science Times Cited 7363] [SCOPUS Times Cited 9688]


[8] Clustering: An Introduction, Available: http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html

[9] Gh. Cartina, Gh. Grigoras, E.C. Bobric, "Clustering Techniques in Fuzzy Modeling. Power Systems Applications", Casa de Editura VENUS, Iasi, 2005

[10] P.E. Sinioros, C. Filote, A. Graur, M.G. Ioannides, "A New Real Time Method of the Instantaneous Active and Reactive Power Calculus", Advances in Electrical and Computer Engineering, Suceava, Romania ISSN 1582-7445, No. 1/2001, volume 1 (8), pp. 5-10

[11] M. Gavrilas, V.C. Sfintes, M.N. Filimon, "Identifying typical load profiles using neural-fuzzy models", 16th IEEE/PES Transmission and Distribution Conf. and Exposition, 2001, Atlanta, pp. 421-426
[CrossRef]


[12] M. Gavrilas, Gh. Cartina, Gh. Grigoras, O. Ivanov, "Modelarea sarcinilor din retelele electrice", Editura PIM, Iasi, 2006

[13] D. Gerbec, S. Gasperic, and F. Gubina, "Comparison of Different Classification Methods for the Consumers' Load Profile Determination", 17th International Conference on Electricity Distribution, CIRED, Barcelona, vol. Session 6, 2003.



References Weight

Web of Science® Citations for all references: 7,368 TCR
SCOPUS® Citations for all references: 9,691 TCR

Web of Science® Average Citations per reference: 526 ACR
SCOPUS® Average Citations per reference: 692 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 2022-06-28 23:07 in 30 seconds.




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


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