Electricity Theft Detection Techniques for Distribution System in GUVNL

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards Distribution Power Loss analysis for electric utilities using a novel intelligencebased techniques like Extreme Learning Machine (ELM), OS-ELM & Support Vector Machine (SVM). The main motivation of this study is to assist Gujarat Urjha Vikas Nigam LTD (GUVNL) to reduce its Distribution Power Loss due to electricity theft. The proposed model preselects suspected customers to be inspected onsite for fraud based on irregularities and abnormal consumption behaviour. This approach provides a method of data mining and involves feature extraction from historical customer consumption data. The approach uses customer load profile information to expose abnormal behaviour that is known to be highly correlated with Power Loss activities. The result yields classification classes that are used to shortlist potential fraud suspects for onsite inspection, based on significant behaviour that emerges due to irregularities in consumption. Simulation results prove the proposed method is more effective compared to the current actions taken by GUVNL in order to reduce Power Loss activities.