TY - JOUR
T1 - Robustness assessment and enhancement of power grids from a complex network's perspective using decision trees
AU - Liu, Dong
AU - Tse, Chi K.
AU - Zhang, Xi
N1 - Funding Information:
Manuscript received February 20, 2019; accepted March 26, 2019. Date of publication April 9, 2019; date of current version April 30, 2019. This work was supported by the Hong Kong Research Grants Council under Grant GRF PolyU152096/17E. This brief was recommended by Associate Editor T. Fernando. (Corresponding author: Dong Liu.) D. Liu and C. K. Tse are with the Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this brief, by examining the profile of the failure cascade of power systems, we identify a critical observable parameter, namely onset time, which is the time after which the propagation rate of a cascading failure increases rapidly. Based on the onset time and the scale of the failed grid in a cascading failure event, we categorize each component in a power network into three types, corresponding to three levels of severity of the failed grid upon the initial failure of that component. Moreover, to investigate robustness enhancement of power networks, we propose a decision-tree-based learning model to extract significant network-based features. By utilizing a number of power networks generated by means of edge re-arrangement targeting topology improvement of the original power system, a decision tree is generated. This tree identifies three network features, including average shortest path length, average clustering coefficient, and average effective resistance (distance) to the nearest generator, which exhibit strong correlation with the robustness of the power network. It is shown that using multiple network-based features can effectively enhance the robustness of power networks.
AB - In this brief, by examining the profile of the failure cascade of power systems, we identify a critical observable parameter, namely onset time, which is the time after which the propagation rate of a cascading failure increases rapidly. Based on the onset time and the scale of the failed grid in a cascading failure event, we categorize each component in a power network into three types, corresponding to three levels of severity of the failed grid upon the initial failure of that component. Moreover, to investigate robustness enhancement of power networks, we propose a decision-tree-based learning model to extract significant network-based features. By utilizing a number of power networks generated by means of edge re-arrangement targeting topology improvement of the original power system, a decision tree is generated. This tree identifies three network features, including average shortest path length, average clustering coefficient, and average effective resistance (distance) to the nearest generator, which exhibit strong correlation with the robustness of the power network. It is shown that using multiple network-based features can effectively enhance the robustness of power networks.
KW - Complex networks
KW - power grids
KW - robustness
UR - http://www.scopus.com/inward/record.url?scp=85065387465&partnerID=8YFLogxK
U2 - 10.1109/TCSII.2019.2909523
DO - 10.1109/TCSII.2019.2909523
M3 - Journal article
AN - SCOPUS:85065387465
SN - 1549-7747
VL - 66
SP - 833
EP - 837
JO - IEEE Transactions on Circuits and Systems II: Express Briefs
JF - IEEE Transactions on Circuits and Systems II: Express Briefs
IS - 5
M1 - 8682126
ER -