By Topic

Analysis of Frequency Dynamics in Power Grid: A Bayesian Structure Learning Approach

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

2 Author(s)
Hannan Ma ; Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville ; Husheng Li

The oscillation of frequency in power grid is studied in this paper. The possibility association of frequencies measured at different locations are modeled by a Bayesian network with the logical structure learned using Bayesian structure learning and real measurements in the U.S. power grid. Frequency data analysis and the detection of incorrect frequency measurements (caused by equipment error or malicious attack) are performed over the logical Bayesian network structure. Such application of Bayesian network is a powerful mathematical tool in computational intelligence. Without the physical power network topology information, a two-branch search-and-score structure learning algorithm with L -1 regulation is proposed to learn the logical structure, achieving around 97% correct prediction rate for future frequency and 92% detection rate for false frequency data with 2% false alarm rate. The tool of epidemic propagation over this logical network is also exploited to analyze the propagation of frequency changes. Using the Kolmogorov-Smirnov test, such logical structure is demonstrated to be well approximated by the Small World network model. And the propagation of frequency changes is demonstrated to be described by the Susceptible-Infectious-Susceptible (SIS) model quite well. The Bayesian structure obtained from the real measurement is statistically validated using a 5-fold training data and the Pearson system.

Published in:

IEEE Transactions on Smart Grid  (Volume:4 ,  Issue: 1 )