By Topic

Privacy-preserving energy theft detection in smart grids

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sergio Salinas ; Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762 ; Ming Li ; Pan Li

In the U.S., energy theft causes six billion dollar losses to utility companies (UCs) every year. With the smart grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the “smart meters” used in smart grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require the users to send their private information, e.g., load files or meter readings at certain times, to the UCs which invades users' privacy and raises serious concerns about privacy, safety, etc. As far as we know, we are the first to investigate the energy theft detection problem considering users' privacy issues. In this paper, we propose to solve in a distributed fashion a linear system of equations (LSE) for the users' “honesty coefficients”, which indicate the users are honest when equal to 1 and are fraudulent when larger than 1. In particular, we develop two distributed privacy-preserving energy theft detection algorithms based on LU decomposition, called LUD and LUPD, respectively, which can identify fraudulent users without invading any user's privacy. Compared to LUD, LUPD requires higher execution time but is stable even in large-size systems. Moreover, the LUD and LUPD algorithms are proposed in the case that users commit energy theft at a constant rate, i.e., with constant honesty coefficients. We also propose adaptive LUD/LUPD algorithms to account for the scenarios where the users have variable honesty coefficients. Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.

Published in:

Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2012 9th Annual IEEE Communications Society Conference on

Date of Conference:

18-21 June 2012