Intelligent Technique for Electricity Theft Identification Using Autoregressive Model.
No Thumbnail Available
Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
LAUTECH Journal of Engineering and Technology, Faculty of Engineering Ladoke Akintola University, Ogbomosho. Available online at
Abstract
Various studies have investigated electricity theft, an illegally act, perpetrated to the detriment of the
electricity power providers, however, less attention has been given to identification of the types of electricity
theft. Data were acquired from the Consumer Load Prototype developed at two different levels using Sensor-
A connected to the Pole Terminal Unit and Sensor-B connected to the Consumer Terminal Unit. The output
of the sensors were connected to BNC-2110 device and linked to the PCI 6420E channel, which log the data
in the computer for further analysis. LABVIEW (2012) software was programmed to acquires data at a
sampling frequency of 500Hz and decimated at 10s interval before logging into the computer hard disk. The
feature extraction of the data acquired was achieved using autoregressive technique and model order
selectionwas based on minimum description length. The model coefficient AR (20), data acquired and
predicted data were used for theft identification. Meter-bypassing theft was identified when the energy
consumption from sensor A and sensor B are different, however sensor B reads zero value and there are
disparities in the model coefficients. Illegal connection before the meter theft was identified whenever there is
difference in energy consumption as evaluated form sensor A and sensor B and there is no zero value
recorded from sensor B, while Meter tampering was detected when the energy consumption as evaluated
form sensor A and sensor B are different and there are no disparities in the model coefficients.
Description
Keywords
Autoregressive technique, Distribution network, Electricity theft identification, Model coefficients
Citation
Abdullateef, A. I., Salami, M.-J. E., & Akorede, M. F. (2018): Intelligent Technique for Electricity Theft Identification Using Autoregressive Model. LAUTECH Journal of Engineering and Technology, 12(1), 1–12, Published by Faculty of Engineering Ladoke Akintola University, Ogbomosho. Available online at http://www.laujet.com/index.php/laujet/issue/view/12