Title
Quantify the Impact of Line Capacity Temporary Expansion on Blackout Risk by the State-Failure-Network Method.
Abstract
The blackout risks of cascading failures in power systems are notably associated with the failures of transmission lines. Line capacity temporary expansion can reduce blackout risk by decreasing the line failures due to the overloads during the cascading failures. To efficiently quantify the impact of line capacity temporary expansion on blackout risks, we propose a data based state-failure-network method in this paper. The state-failure network, which is formed by the cascading failure data generated by cascading failure simulations, contains the empirical probabilities that correspond to the failure probabilities of lines. Since implementing line capacity temporary expansion to the system can change the failure probabilities of lines and reduce the blackout risk, the empirical probabilities offer the link between line capacity temporary expansion and state-failure network. By updating the values in the state-failure network with changed empirical probabilities, the blackout risk after line capacity temporary expansion is implemented can be efficiently worked out by state-failure network. Thus, the impact of line capacity temporary expansion on blackout risk is quantified by comparing the newly calculated blackout risk with the risk before the line capacity temporary expansion is implemented. The advantage of the proposed method lies in the high accuracy and efficiency of quantifying the impacts of any line capacity temporary expansion schemes once the state-failure network is formed. Case studies verify the accuracy and efficiency of the proposed method.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2960306
IEEE ACCESS
Keywords
DocType
Volume
Blackout risk,cascading failures,state-failure-network method,line capacity temporary expansion
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Linzhi Li100.34
Hao Wu201.35
Yonghua Song3167.16
Dunwen Song400.34
Yi Liu500.34