DOI:10.1007/s40565-017-0268-1 |
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Identification and characterization of irregular consumptionsof load data |
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Author:
Desh Deepak SHARMA1
, S. N. SINGH2
, Jeremy LIN2
,
Elham FORUZAN3
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Author Affiliation:
1 Indian Institute of Technology, Kanpur, Kanpur, India
2 PJM Interconnection, Audubon, PA, USA
3 Department of Electrical Engineering, University of
Nebraska–Lincoln, Lincoln, NE, USA
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Foundation: |
This work is supported by the Department of
Science and Technology (DST), New Delhi, India (No. DST/EE/
2014127). Also, D.D. Sharma acknowledges the MJP Rohilkhand
University, Bareilly, UP for providing leave for pursuing PhD at IIT
Kanpur. The views presented in this paper do not necessarily represent
those of the PJM Interconnection, USA. |
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Abstract: |
The historical information of loadings on substation
helps in evaluation of size of photovoltaic (PV)
generation and energy storages for peak shaving and distribution
system upgrade deferral. A method, based on
consumption data, is proposed to separate the unusual
consumption and to form the clusters of similar regular
consumption. The method does optimal partition of the
load pattern data into core points and border points, high
and less dense regions, respectively. The local outlier
factor, which does not require fixed probability distribution
of data and statistical measures, ranks the unusual consumptions
on only the border points, which are a few
percent of the complete data. The suggested method finds
the optimal or close to optimal number of clusters of
similar shape of load patterns to detect regular peak and
valley load demands on different days. Furthermore,
identification and characterization of features pertaining to
unusual consumptions in load pattern data have been done
on border points only. The effectiveness of the proposed
method and characterization is tested on two practical
distribution systems. |
Keywords: |
Density based clustering, Irregular
consumption, Local outlier factor, Peak demand, Valley
demand |
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Online Time:2017/05/09 |
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