TY - JOUR
T1 - A dependency graph approach for fault detection and localization towards secure smart grid
AU - He, Miao
AU - Zhang, Junshan
N1 - Funding Information:
Junshan Zhang (S’98–M’00–SM’06) received the Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, in 2000. He joined the Electrical Engineering Department at Arizona State University, Tempe, in August 2000, where he has been Professor since 2010. His research interests include network modeling and optimization/ control, cyberphysical systems, stochastic analysis, and wireless communications. His current research focuses on fundamental problems in information networks and network science, including network management, smart grid, network security, and network information theory. Prof. Zhang is a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in 2003. He received the Outstanding Research Award from the IEEE Phoenix Section in 2003. He served as TPC cochair for WICON 2008 and IPCCC’06, TPC vice chair for ICCCN’06, and a member of the technical program committees of INFOCOM, SECON, GLOBECOM, ICC, MOBIHOC, BROADNETS, and SPIE ITCOM. He was the general chair for IEEE Communication Theory Workshop 2007. He was an Associate Editor for IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS. He is currently an editor for Computer Network and IEEE Wireless Communication Magazine. He coauthored a paper that won IEEE ICC 2008 best paper award, and one of his papers was selected as the INFOCOM 2009 Best Paper Award Runner-up. He will be TPC cochair for INFOCOM 2012.
Funding Information:
Manuscript received September 28, 2010; accepted February 14, 2011. Date of current version May 25, 2011. This work was supported in part by the U.S. National Science Foundation under Grant CNS-1035906 and DoD DTRA Project HDTRA1-09-1-0032. Paper no. TSG-00142-2010.
PY - 2011/6
Y1 - 2011/6
N2 - Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.
AB - Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.
KW - Dependency graph
KW - Markov random field
KW - fault localization
KW - multiscale decomposition
KW - network inference
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=79957710498&partnerID=8YFLogxK
U2 - 10.1109/TSG.2011.2129544
DO - 10.1109/TSG.2011.2129544
M3 - Article
AN - SCOPUS:79957710498
SN - 1949-3053
VL - 2
SP - 342
EP - 351
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 2
M1 - 5767534
ER -