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Nontechnical Loss
Detection for Metered Customers in Alexandria Electricity Distribution
Company Using Support Vector Machine
A. Hatem Tameem Alfarra, B.
Amani Attia, C. S. M. El Safty
2018/04/20
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Abstract
Non-technical losses (NTL) during transmission
and distribution (T&D) of electrical energy is a major problem faced
by utility companies which is very difficult to fight and detect. For
that, more of power utilities spend thousands dollar for research centres
to find efficient methods for detecting and controlling abnormalities.
Electricity theft and billing irregularities forms the main portion of
NTL. With the introduction of smart meter, the frequency of reporting
energy consumption data to the utility company has been increased. Incoming
and outgoing energy could be monitored and analyzed. Electricity theft
is a complex problem with many parameters to be evaluated before implementing
any measures to detect and control that. These parameters include some
issues like social, economic, regional, managerial, infrastructural, and
corruption.
In recent years, several data mining and research studies on fraud detection
and prediction techniques have been carried out in the electricity distribution
sector. Support vector machine (SVM) technique has dominated the research
for classifying data and detecting fraudulent electricity customers. SVM
technique has good ability in data mining and data classification. The
paper objective is to analyze the metered energy consumption data recorded
and predict the pattern or the form of daily users energy consumption,
then using SVM to classify the data whether normal or theft. The suggested
technique is then tested using real data from Alexandria Electricity Distribution
Company (AEDC). The proposed technique was able to distinguish between
healthy and theft cases.
| Published in: Renewable Energy
& Power Quality Journal (RE&PQJ, Nº. 16) |
| Pages: 468-474 |
Date of Publication: 2018/04/20 |
| ISSN: 2172-038X |
Date of Current Version:2018/03/23 |
| REF: 353-18 |
Issue Date: April 2018 |
| DOI:10.24084/repqj16.353 |
Publisher: EA4EPQ |
Authors and affiliations
A. Hatem Tameem Alfarra1, B. Amani Attia2, C. S. M.
El Safty1
1. Electrical Engineering & Control Department. Arab Academy for Sience
Technology and Maritime Traspotation AASTMT. Campus of Alexandria
(Egypt)
2 Alexandria Electricity Distribution Company (AEDC). (Egypt)
Key words
Electricity theft, NTL, Smart meter, SVM
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