Online electricity theft detection and prevention scheme for smart cities

Electricity theft is a notable aspect of power distribution utilities due to advance in the non-technical loss. It results imbalance between power supply and demand. It consequence overload of the distribution network and extraneous tariff invoke on legally connected consumers. The advance metering infrastructure is useful for an energy audit of every distribution transformer due to a communication facility. However, direct hooking on distribution overhead line or tapping from underground cables remains an interminable issue which has to be rigorously decimated. The objective of this study is to present real-time electricity theft detection and prevention scheme (ETDPS) with the available infrastructure in the field. The proposed ETDPS is based on programmable logic control; it identifies the pilferage locations and estimates the power stolen by illegal consumers. The prototype is tested in the laboratory and the results demonstrate that the ETDPS works satisfactorily under diversified operating conditions. The proposed scheme is implemented as a part of their Smart City Pilot Project by Maharashtra State Electricity Distribution Company Limited, Nagpur (India) and the performance demonstrates its feasibility.

[1]  Chan-Nan Lu,et al.  Non-technical loss detection using state estimation and analysis of variance , 2013, 2013 IEEE Power & Energy Society General Meeting.

[2]  Makarand Sudhakar Ballal,et al.  Fuzzy Inference Based Electricity Theft Prevention System to Restrict Direct Tapping Over Distribution Line , 2020 .

[3]  Nanpeng Yu,et al.  A Physically Inspired Data-Driven Model for Electricity Theft Detection With Smart Meter Data , 2019, IEEE Transactions on Industrial Informatics.

[4]  Sieh Kiong Tiong,et al.  Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines , 2010, IEEE Transactions on Power Delivery.

[5]  Qian Ai,et al.  Electricity theft detection in smart grid using random matrix theory , 2018 .

[6]  A. N. de Souza,et al.  Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems , 2011, IEEE Transactions on Power Delivery.

[7]  Sangho Choe,et al.  Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing , 2019, IEEE Transactions on Smart Grid.

[8]  Sonal Jain,et al.  Rule‐based classification of energy theft and anomalies in consumers load demand profile , 2019, IET Smart Grid.

[9]  Abdulrahaman Okino Otuoze,et al.  Electricity theft detection by sources of threats for smart city planning , 2019, IET Smart Cities.

[10]  Victor C. M. Leung,et al.  Electricity Theft Detection in AMI Using Customers’ Consumption Patterns , 2016, IEEE Transactions on Smart Grid.

[11]  Antonio Padilha,et al.  Spatial-Temporal Estimation for Nontechnical Losses , 2016, IEEE Transactions on Power Delivery.

[12]  Pablo Massaferro,et al.  Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return , 2020, IEEE Transactions on Power Systems.

[13]  Nikos D. Hatziargyriou,et al.  A Hybrid Method for Non-Technical Loss Detection in Smart Distribution Grids , 2019, IEEE Transactions on Smart Grid.