Mining complex power networks for blackout prevention

Zhao, Jun, Dong, Zhao Yang and Zhang, Pei (2007). Mining complex power networks for blackout prevention. In: P. Berkhin, R. Caruana, X. Wu and S. Gaffney, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, U.S.A., (986-994). 12-15 August 2007. doi:10.1145/1281192.1281298

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Author Zhao, Jun
Dong, Zhao Yang
Zhang, Pei
Title of paper Mining complex power networks for blackout prevention
Conference name 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Conference location San Jose, CA, U.S.A.
Conference dates 12-15 August 2007
Proceedings title Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Journal name Kdd-2007 Proceedings of the Thirteenth Acm Sigkdd International Conference On Knowledge Discovery and Data Mining
Place of Publication New York, U.S.A.
Publisher Association for Computing Machinery (ACM)
Publication Year 2007
Sub-type Fully published paper
DOI 10.1145/1281192.1281298
ISBN 9781595936097
Editor P. Berkhin
R. Caruana
X. Wu
S. Gaffney
Start page 986
End page 994
Total pages 9
Collection year 2008
Language eng
Abstract/Summary Following the recent devastating blackouts in North America, UK and Italy, blackout prevention has attracted significant attention, though it is known as a notoriously difficult task. To prevent the blackout, it is essential to accurately predict the instable status of power network components. In the large-scale power network however, existing analysis tools fail to perform accurate and in-time prediction of component instability, because of the sophisticated structure of real-world power networks and the huge amount of system variables to be analyzed. To prevent the blackout, we need an accurate and efficient method that (a) can discover interesting features and patterns relevant to the blackout, from the highly complex structure and ten thousands of system variables of a power network, and (b) can give accurate and fast prediction of system instability whenever required, so that the network operator can take necessary actions in time. In this paper, we report our tool developed for power network instability prediction. The proposed method consists of two major stages. In the first stage,a novel type of patterns namely Local Correlation Network Pattern (LCNP) is mined from the structure and system variables of the power network. Correlation rules, which are useful for the network operator to locate potentially instable components, can be further generated from the LCNP. In the second stage, a kernel based network classification method is developed to predict the system instability. By testing on a real world power network (the New England system), we demonstrate that the proposed tool is effective in predicting system instability and thus highly useful for blackout prevention.
Subjects 290901 Electrical Engineering
660301 Electricity transmission
Keyword Graph mining
Power networks
Blackout prevention
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Industrial and Government Track Paper

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Created: Tue, 06 May 2008, 12:25:09 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering