TY - GEN
T1 - Graph-based multiple-line outage identification in power transmission systems
AU - Wang, Boyu
AU - Gan, Chao
AU - Yang, Jing
AU - Hedge, Chinmay
AU - Wu, Jingxian
N1 - Funding Information:
This work was supported in part by the U. S. National Science Foundation (NSF) under Grants ECCS-1405403, ECCS-1650299 and CCF-1566281.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/29
Y1 - 2018/1/29
N2 - Fast and accurate detection and identification of power line outage is of paramount importance for the prevention of cascading failures in power systems, as well as prompt and effective restoration following the outage. Traditional approaches can only detect single and double line outages due to the combinatorial complexity challenges involved in the algorithms. A novel approach is to cast it to a sparse vector estimation problem, which can be solved efficiently by taking advantage of the recent progress in compressive sensing and variable selection. In this work, we adopt a similar approach to formulate the problem as a sparse binary-valued vector estimation problem, and leverage the cluster structure existing in most multiple-line outages to solve it. We propose two low-complexity graph-based algorithms to identify clustered line-outages. Simulated tests in IEEE-118 bus system confirm that the proposed algorithms can significantly improve the accuracy and efficiency of baseline algorithms that do not leverage the cluster structure of multiple-line outages.
AB - Fast and accurate detection and identification of power line outage is of paramount importance for the prevention of cascading failures in power systems, as well as prompt and effective restoration following the outage. Traditional approaches can only detect single and double line outages due to the combinatorial complexity challenges involved in the algorithms. A novel approach is to cast it to a sparse vector estimation problem, which can be solved efficiently by taking advantage of the recent progress in compressive sensing and variable selection. In this work, we adopt a similar approach to formulate the problem as a sparse binary-valued vector estimation problem, and leverage the cluster structure existing in most multiple-line outages to solve it. We propose two low-complexity graph-based algorithms to identify clustered line-outages. Simulated tests in IEEE-118 bus system confirm that the proposed algorithms can significantly improve the accuracy and efficiency of baseline algorithms that do not leverage the cluster structure of multiple-line outages.
UR - http://www.scopus.com/inward/record.url?scp=85046370845&partnerID=8YFLogxK
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U2 - 10.1109/PESGM.2017.8274462
DO - 10.1109/PESGM.2017.8274462
M3 - Conference contribution
AN - SCOPUS:85046370845
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -
