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

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2009 - 11

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Clustering Techniques in Load Profile Analysis for Distribution Stations

BOBRIC, E. C., CARTINA, G., GRIGORAS, G.
 
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Download PDF pdficon (501 KB) | Citation | Downloads: 1,504 | Views: 8,013

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.


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[1] Optimal Clustering of Time Periods for Electricity Demand-Side Management, Rogers, David F., Polak, George G., IEEE Transactions on Power Systems, ISSN 0885-8950, Issue 4, Volume 28, 2013.
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[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.
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[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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[5] Evaluation of wind farm aggregation using probabilistic clustering algorithms for power system stability assessment, Rahman, Mir Toufikur, Hasan, Kazi N., Sokolowski, Peter, Sustainable Energy, Grids and Networks, ISSN 2352-4677, Issue , 2022.
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[6] Morphometrical analysis of daily load graphs, Komenda, Taras, Komenda, Nataliya, International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, Issue 1, Volume 42, 2012.
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[7] Classification of daily electric load profiles of non-residential buildings, Bourdeau, Mathieu, Basset, Philippe, Beauchêne, Solène, Da Silva, David, Guiot, Thierry, Werner, David, Nefzaoui, Elyes, Energy and Buildings, ISSN 0378-7788, Issue , 2021.
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[9] Least Squares Modeling of Voltage Harmonic Distortion Due to PC Cluster Operation, MUJOVIC, S., DJUKANOVIC, S., RADULOVIC, V., KATIC, V., RASOVIC, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 13, 2013.
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[11] Strategies for Power/Energy Saving in Distribution Networks, GRIGORAS, G., CARTINA, G., BOBRIC, E. C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 10, 2010.
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[13] The random varying loads and their impacts on the performance of smart grids, Iqteit, Nassim A., Arsoy, Ayşen Basa, Çakır, Bekir, Electric Power Systems Research, ISSN 0378-7796, Issue , 2022.
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[15] The Impact of the Load Side Parameters on PC Cluster's Harmonics Emission, KATIC, V. A., MUJOVIC, S. V., RADULOVIC, V. M., RADOVIC, J. S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 11, 2011.
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[16] Two-Stage Load Pattern Clustering Using Fast Wavelet Transformation, Mets, Kevin, Depuydt, Frederick, Develder, Chris, IEEE Transactions on Smart Grid, ISSN 1949-3053, Issue 5, Volume 7, 2016.
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[17] Load profile analysis for reducing energy demands of production systems in non-production times, Dehning, Patrick, Blume, Stefan, Dér, Antal, Flick, Dominik, Herrmann, Christoph, Thiede, Sebastian, Applied Energy, ISSN 0306-2619, Issue , 2019.
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[18] Optimal BESS Scheduling Strategy in Microgrids Based on Genetic Algorithms, Sidea, Dorian-Octavian, Toma, Lucian, Sanduleac, Mihai, Picioroaga, Irina-Ioana, Boicea, Valentin-Adrian, 2019 IEEE Milan PowerTech, ISBN 978-1-5386-4722-6, 2019.
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[19] Locality sensitive hashing of customer load profiles, Beretka, Sandor F., Varga, Ervin D., 2013 International Conference on Renewable Energy Research and Applications (ICRERA), ISBN 978-1-4799-1464-7, 2013.
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[20] Voltage and reactive power control in a distribution system using hybrid SA-DBPSO algorithm, Rahideh, Abdolhamid, Gitizadeh, Mohsen, Sadrzadeh, Ali, 2015 20th Conference on Electrical Power Distribution Networks Conference (EPDC), ISBN 978-1-4673-6612-0, 2015.
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[21] Using Significant Classification Rules to Analyze Korean Customers' Power Consumption Behavior: Incremental Tree Induction using Cascading-and-Sharing Method, Piao, Minghao, Li, Meijing, Ryu, Keun Ho, 2010 10th IEEE International Conference on Computer and Information Technology, ISBN 978-1-4244-7547-6, 2010.
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[22] Data-driven residential customer aggregation based on seasonal behavioral patterns, Chen, Kunjin, Hu, Jun, He, Ziyu, 2017 IEEE Power & Energy Society General Meeting, ISBN 978-1-5386-2212-4, 2017.
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