A Statistical Framework for Power Theft Detection in Smart Grid Networks

• Activity Number: 130 - Statistical Computation, Simulation, and Computer Experiments
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Jin Tao* and George Michailidis
• Companies: University of Florida and University of Florida

Abstract

The electricity distribution networks in many countries are undergoing rapid upgrades with the introduction of sensing and communications capabilities that significantly improve measurement and control functions. Such upgrades give rise to the so called "Smart Grid". However, the same capabilities have enabled various cyber-attacks. A particular attack focuses on power theft, where the attacker alters (increases) the electricity consumption measurements recorded by the smart meter of other users, while reducing her own measurement. Thus, such attacks, since they maintain the total amount of power consumed at the distribution transformer are hard to detect by techniques that monitor mean levels of consumption patterns. To address this data integrity problem, we develop statistical techniques that utilize higher order information and thus are capable of detecting such attacks and also identify the users (attacker and victims) involved. The models work both for independent and correlated electricity consumption streams. The results are illustrated on synthetic and real consumption data.