ELECTRICAL POWER THEFT DETECTION

Authors

  • Pranali Sable Final year B.E Student , Dept. of Computer Engineering, AISSMS IOIT College of Engineering, Pune Maharashtra ,India.
  • Priyanka Ohol Final year B.E Student , Dept. of Computer Engineering, AISSMS IOIT College of Engineering, Pune Maharashtra, India.
  • Pallavi Murhe Final year B.E Student , Dept. of Computer Engineering, AISSMS IOIT College of Engineering, Pune Maharashtra, India.
  • Sneha Vidhate Final year B.E Student , Dept. of Computer Engineering, AISSMS IOIT College of Engineering, Pune Maharashtra, India

Keywords:

Electricity theft, Extreme learning machine (ELM), Online Sequential Extreme learning machine (OSELM), Expert System. Intelligent systems

Abstract

Distribution of electricity involves significant Technical as well as Non-Technical Losses (NTL). Illegal
consumption of electricity or electricity theft constitutes a major share of NTL. This project discusses several methods
implemented by illegal consumers for stealing. With the advent of advanced metering technologies, real-time energy
consumption data will be available at the utilities end, which can be used to detect illegal consumers. This project presents
an encoding technique that simplifies the received customer energy consumption readings (patterns) and maps them into
corresponding irregularities in consumption. Then, this project elucidates operation of intelligent classification techniques
on customer energy consumption data to classify genuine and illegal consumers. These classification models are applied on
regular energy consumption data as well as the encoded data to compare corresponding classification accuracies and
computational overhead. Depending on abnormal consumtion behaviour suspected consumers are onspected .Using data
mining techniques suspected customers profiles are loaded .The approach of this project is to deal with power loss activity
like detecting the illegal power consumers

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Published

2022-08-23

How to Cite

Pranali Sable, Priyanka Ohol, Pallavi Murhe, & Sneha Vidhate. (2022). ELECTRICAL POWER THEFT DETECTION. International Journal of Advance Engineering and Research Development (IJAERD), 4(12), -. Retrieved from https://ijaerd.org/index.php/IJAERD/article/view/5884

Issue

Vol. 4 No. 12 (2017): ETCWDC-2017 (Emerging Technologies in the Computer World organized by Department of Computer Engineering)

Section

Articles