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Clustering Techniques in Load Profile Analysis for Distribution StationsBOBRIC, E. C., CARTINA, G., GRIGORAS, G. |
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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
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. |
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Digital Object Identifier: 10.1109/TPWRS.2013.2252373 [CrossRef]
[2] Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection, Flick, Dominik, Keck, Claudio, Herrmann, Christoph, Thiede, Sebastian, Procedia CIRP, ISSN 2212-8271, Issue , 2020.
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[3] Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation, Varga, Ervin D., Beretka, Sandor F., Noce, Christian, Sapienza, Gianluca, IEEE Transactions on Power Systems, ISSN 0885-8950, Issue 4, Volume 30, 2015.
Digital Object Identifier: 10.1109/TPWRS.2014.2354552 [CrossRef]
[4] A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures, Moussa, Sherin, Mastorakis, N., Mladenov, V., Bulucea, A., MATEC Web of Conferences, ISSN 2261-236X, Issue , 2016.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
