Multi-timescale Electricity Theft Detection and Localization in Distribution Systems Based on State Estimation and PMU Measurements

Electricity theft is a serious issue for distribution companies around the world. Often linked to criminal activities, it is dangerous for the grid and the neighborhoods. While placing measurement points at each bus would allow an easy detection, it is not a practical approach. In this paper, a multi-timescale theft estimation (MISTE) method that takes advantage of smart-meters as well as the sparse grid sensing infrastructure that is being envisaged for state estimation is proposed. It combines power and voltage measurement across time to detect any inconsistency caused by electricity theft. Contrary to existing approaches which are snapshot-based and assume smart-meters to be able to measure instantaneous power consumption, the proposed method models smart-meters as energy measurement devices and combines the measurement timescales of the smart-meters and the PMUs in the computations. The detection performance of the proposed approach is compared to the state of the art theft detection methods. Both the true positive rate as well as the false negative rate are considered, which few papers have discussed previously. Insights on the impact of theft location on theft detection are also given.

[1]  Saman A. Zonouz,et al.  AMIDS: A multi-sensor energy theft detection framework for advanced metering infrastructures , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[2]  Lingfeng Wang,et al.  Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft , 2011 .

[3]  Syed Khaleel Ahmed,et al.  Detection of abnormalities and electricity theft using genetic Support Vector Machines , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[4]  Zita A. Vale,et al.  Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection , 2013, 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America).

[5]  Caio C. O. Ramos,et al.  Fast Non-Technical Losses Identification Through Optimum-Path Forest , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[6]  Arlan Luiz Bettiol,et al.  Non-technical losses identification using Optimum-Path Forest and state estimation , 2015, 2015 IEEE Eindhoven PowerTech.

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

[8]  Catherine Rosenberg,et al.  State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering , 2017, IEEE Transactions on Power Systems.

[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]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[11]  Nirwan Ansari,et al.  CONSUMER: A Novel Hybrid Intrusion Detection System for Distribution Networks in Smart Grid , 2013, IEEE Transactions on Emerging Topics in Computing.

[12]  A. Monticelli,et al.  State estimation in electric power systems , 1999 .

[13]  A.H. Nizar,et al.  A Data Mining Based NTL Analysis Method , 2007, 2007 IEEE Power Engineering Society General Meeting.

[14]  Johan Driesen,et al.  Parameter identification of unknown radial grids for theft detection , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[15]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.

[16]  Vijay Arya,et al.  Loss localisation in smart distribution networks , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[17]  A. Monticelli,et al.  Electric power system state estimation , 2000, Proceedings of the IEEE.

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

[19]  Maneesha Vinodini Ramesh,et al.  Power theft detection in microgrids , 2015, 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS).

[20]  David E. Culler,et al.  Micro-synchrophasors for distribution systems , 2014, ISGT 2014.

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

[22]  Zhenhua Wang,et al.  Smart Meter Data Analysis for Power Theft Detection , 2013, MLDM.

[23]  Lingfeng Wang,et al.  High performance computing for detection of electricity theft , 2013 .

[24]  Rafael Nilson Rodrigues,et al.  A low cost prototype of a Phasor Measurement Unit using Digital Signal Processor , 2016, 2016 IEEE Biennial Congress of Argentina (ARGENCON).

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

[26]  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).

[27]  Xiaohui Xu,et al.  Research on anti-electricity stealing method base on state estimation , 2011, 2011 IEEE Power Engineering and Automation Conference.

[28]  Michael A. Saunders,et al.  MINOS 5. 0 user's guide , 1983 .

[29]  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).

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

[31]  Pan Li,et al.  Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach , 2016, IEEE Transactions on Power Systems.

[32]  Yun Gu,et al.  A novel method to detect bad data injection attack in smart grid , 2013, INFOCOM Workshops.

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

[34]  Tom A. Short,et al.  Advanced Metering for Phase Identification, Transformer Identification, and Secondary Modeling , 2013, IEEE Transactions on Smart Grid.

[35]  Yun Gu,et al.  Bad data detection method for smart grids based on distributed state estimation , 2013, 2013 IEEE International Conference on Communications (ICC).

[36]  Jun Luo,et al.  Energy-theft detection issues for advanced metering infrastructure in smart grid , 2014, Tsinghua Science and Technology.

[37]  Wil L. Kling,et al.  Theft detection and smart metering practices and expectations in the Netherlands , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[38]  Pan Li,et al.  Privacy-preserving energy theft detection in smart grids , 2012, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[39]  Chao-Kai Wen,et al.  Electricity theft detection in low voltage networks with smart meters using state estimation , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[40]  Yang Xiao,et al.  Exploring Malicious Meter Inspection in Neighborhood Area Smart Grids , 2013, IEEE Transactions on Smart Grid.

[41]  Djordje Atanackovic,et al.  Deployment of real-time state estimator and load flow in BC Hydro DMS - challenges and opportunities , 2013, 2013 IEEE Power & Energy Society General Meeting.

[42]  David Macii,et al.  Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[43]  L. Brinson,et al.  DEFENSE TECHNICAL INFORMATION CENTER , 2001 .

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

[45]  Yang Xiao,et al.  NFD: A practical scheme to detect non-technical loss fraud in smart grid , 2014, 2014 IEEE International Conference on Communications (ICC).

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

[47]  Catherine Rosenberg,et al.  Markovian models for home electricity consumption , 2011, GreenNets '11.

[48]  Alexandra von Meier,et al.  UC Berkeley Sustainable Infrastructures Title A Linear Power Flow Formulation for Three-Phase Distribution Systems Permalink , 2016 .

[49]  Lingfeng Wang,et al.  A hybrid neural network model and encoding technique for enhanced classification of energy consumption data , 2011, 2011 IEEE Power and Energy Society General Meeting.

[50]  J. Teng A direct approach for distribution system load flow solutions , 2003 .

[51]  Zikun Xu,et al.  A Design of Theft Detection Framework for Smart Grid Network , 2015 .