A Physically Inspired Data-Driven Model for Electricity Theft Detection With Smart Meter Data

Electricity theft is the third largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence, a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kron-reduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers, respectively. The proposed model is validated with real-world smart-meter data. The results show that the model is effective in identifying electricity theft cases.

[1]  I. Markovsky,et al.  A Recursive Restricted Total Least-Squares Algorithm , 2014, IEEE Transactions on Signal Processing.

[2]  Tanveer Ahmad,et al.  Review of various modeling techniques for the detection of electricity theft in smart grid environment , 2018 .

[3]  Alvaro A. Cárdenas,et al.  Evaluating Electricity Theft Detectors in Smart Grid Networks , 2012, RAID.

[4]  Xu Peiliang,et al.  Overview of Total Least Squares Methods , 2013 .

[5]  P. Rousseeuw.  Least Median of Squares Regression , 1984 .

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

[7]  KokSheik Wong,et al.  Detection of energy theft and defective smart meters in smart grids using linear regression , 2017 .

[8]  Florian Dörfler,et al.  Fast power system analysis via implicit linearization of the power flow manifold , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  C C O Ramos,et al.  A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest , 2011, IEEE Transactions on Power Systems.

[10]  Dmitry Podkuiko,et al.  Energy Theft in the Advanced Metering Infrastructure , 2009, CRITIS.

[11]  Michael Chertkov,et al.  Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability , 2017, 2018 Power Systems Computation Conference (PSCC).

[12]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[13]  Jiaying Lin,et al.  Energy theft detection via integrated distribution state estimation based on AMI and SCADA measurements , 2015, 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[14]  Breno C. Costa,et al.  FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN -BASED KNOWLEDGE -DISCOVERY PROCESS , 2013 .

[15]  Lingfeng Wang,et al.  Support vector machine based data classification for detection of electricity theft , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[16]  B. Das,et al.  Estimation of Parameters of a Three-Phase Distribution Feeder , 2011, IEEE Transactions on Power Delivery.

[17]  Pan Li,et al.  State estimation for energy theft detection in microgrids , 2014, 9th International Conference on Communications and Networking in China.

[18]  Neeraj Kumar,et al.  Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid , 2016, IEEE Transactions on Industrial Informatics.

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  J.E.R. Alves,et al.  Identification of energy theft and tampered meters using a central observer meter: a mathematical approach , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[21]  Catherine Rosenberg,et al.  Multi-timescale Electricity Theft Detection and Localization in Distribution Systems Based on State Estimation and PMU Measurements , 2018, e-Energy.

[22]  Kelton A. P. Costa,et al.  Unsupervised non-technical losses identification through optimum-path forest , 2016 .

[23]  A.H. Nizar,et al.  Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.

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

[25]  Anupam Joshi,et al.  Entropy-based electricity theft detection in AMI network , 2018, IET Cyper-Phys. Syst.: Theory & Appl..

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

[27]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[28]  Zibin Zheng,et al.  Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids , 2018, IEEE Transactions on Industrial Informatics.

[29]  William Kersting,et al.  Distribution System Modeling and Analysis , 2001, Electric Power Generation, Transmission, and Distribution: The Electric Power Engineering Handbook.

[30]  Saman A. Zonouz,et al.  A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures , 2013, IEEE Journal on Selected Areas in Communications.

[31]  Dale Borowiak,et al.  Linear Models, Least Squares and Alternatives , 2001, Technometrics.

[32]  F. Perez,et al.  Detecting Non-Technical Losses in Radial Distribution System Transformation Point through the Real Time State Estimation Method , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

[33]  W. Wang,et al.  Advanced Metering Infrastructure Data Driven Phase Identification in Smart Grid , 2017 .

[34]  Jing Tian,et al.  Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm , 2014 .

[35]  Ram Rajagopal,et al.  Smart Meter Driven Segmentation: What Your Consumption Says About You , 2013, IEEE Transactions on Power Systems.

[36]  Zdenek Zumr,et al.  Last Mile Asset Monitoring: Low Cost Rapid Deployment Asset Monitoring , 2014 .

[37]  João O. P. Pinto,et al.  Fraud detection system for high and low voltage electricity consumers based on data mining , 2009, 2009 IEEE Power & Energy Society General Meeting.

[38]  Edgard Jamhour,et al.  A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns , 2019, IEEE Transactions on Smart Grid.

[39]  Muhammad Tariq,et al.  Electricity Theft Detection and Localization in Grid-Tied Microgrids , 2018, IEEE Transactions on Smart Grid.

[40]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[41]  Oskar Maria Baksalary,et al.  Particular formulae for the Moore-Penrose inverse of a columnwise partitioned matrix , 2007 .

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

[43]  Michael Chertkov,et al.  Structure Learning and Statistical Estimation in Distribution Networks - Part II , 2015, 1502.07820.

[44]  Thomas B. Smith,et al.  Electricity theft: a comparative analysis , 2004 .

[45]  S. Weisberg,et al.  Companion to Applied Regression , 2015 .

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