TY - GEN
T1 - A distributed intelligent framework for electricity theft detection using benford's law and stackelberg game
AU - Wei, Longfei
AU - Sundararajan, Aditya
AU - Sarwat, Arif I.
AU - Biswas, Saroj
AU - Ibrahim, Erfan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/27
Y1 - 2017/10/27
N2 - Electricity theft is a major contributor of nontechnical losses in the distribution systems of the smart grid. However, owing to the resource-limitations of smart meters and the privacy requirement of electricity usage data, theft detection has become a challenging task for electric utilities. To address this problem, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed and implemented in this paper. It is equipped with Benford's Analysis for initial but powerful diagnostics on smart meter big data. A Stackelberg game-theoretic model is formulated to analyze the strategic interactions between one utility and multiple electricity thieves, which is applied to data flagged suspicious by Benford's Analysis. The Stackelberg equilibrium provides sampling rate and threshold to conduct a Likelihood Ratio Test (LRT) to detect potentially fraudulent meters. The framework is validated on real interval electricity usage data from an electric utility in Florida to filter fraudulent meters in a community.
AB - Electricity theft is a major contributor of nontechnical losses in the distribution systems of the smart grid. However, owing to the resource-limitations of smart meters and the privacy requirement of electricity usage data, theft detection has become a challenging task for electric utilities. To address this problem, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed and implemented in this paper. It is equipped with Benford's Analysis for initial but powerful diagnostics on smart meter big data. A Stackelberg game-theoretic model is formulated to analyze the strategic interactions between one utility and multiple electricity thieves, which is applied to data flagged suspicious by Benford's Analysis. The Stackelberg equilibrium provides sampling rate and threshold to conduct a Likelihood Ratio Test (LRT) to detect potentially fraudulent meters. The framework is validated on real interval electricity usage data from an electric utility in Florida to filter fraudulent meters in a community.
KW - Benford's Analysis
KW - Likelihood Ratio Test
KW - Stackelberg game
KW - electricity theft
KW - smart meter
UR - http://www.scopus.com/inward/record.url?scp=85040217918&partnerID=8YFLogxK
U2 - 10.1109/RWEEK.2017.8088640
DO - 10.1109/RWEEK.2017.8088640
M3 - Conference contribution
AN - SCOPUS:85040217918
T3 - Proceedings - 2017 Resilience Week, RWS 2017
SP - 5
EP - 11
BT - Proceedings - 2017 Resilience Week, RWS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Resilience Week, RWS 2017
Y2 - 18 September 2017 through 22 September 2017
ER -