Toward Efficient Wide-Area Identification of Multiple Element Contingencies in Power Systems

Power system $N-x$ contingency analysis has inherent challenges due to its combinatorial characteristic where outages grow exponentially with the increase of $x$ and $N$. to address these challenges, this paper proposes a method that utilizes Line Outage Distribution Factors (LODFs) and group betweenness centrality to identify subsets of critical branches. The proposed LODF metrics are used to select the high-impact branches. Based on each selected branch, the approach constructs the subgraph with parameters of distance and search level, while using branches' LODF metrics as the weights. A key innovation of this work is the use of the distance and search level parameters, which allow the subgraph to identify the most coupled critical elements that may be far away from a selected branch. The proposed approach is validated using the 200- and 500-bus test cases, and results show that the proposed approach can identify multiple $\mathrm{N-x}$ contingencies that cause violations.

[1]  M. Ferris,et al.  The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms , 2019, ArXiv.

[2]  Yousu Chen,et al.  A High-Performance Hybrid Computing Approach to Massive Contingency Analysis in the Power Grid , 2009, 2009 Fifth IEEE International Conference on e-Science.

[3]  Gilles Caporossi,et al.  A New Approach for Contingency Analysis Based on Centrality Measures , 2019, IEEE Systems Journal.

[4]  K. W. Hedman,et al.  Real-Time Contingency Analysis With Transmission Switching on Real Power System Data , 2016, IEEE Transactions on Power Systems.

[5]  Robin Podmore,et al.  Real-Time Contingency Analysis With Corrective Transmission Switching , 2016, IEEE Transactions on Power Systems.

[6]  Zhao Yang Dong,et al.  An improved model for structural vulnerability analysis of power networks , 2009 .

[7]  Fei Xue,et al.  Structural vulnerability of power systems: A topological approach , 2011 .

[8]  Anna Scaglione,et al.  Electrical centrality measures for electric power grid vulnerability analysis , 2010, 49th IEEE Conference on Decision and Control (CDC).

[9]  Shihong Miao,et al.  Hybrid flow betweenness approach for identification of vulnerable line in power system , 2015 .

[10]  Zeyu Mao,et al.  Generalized Contingency Analysis Based on Graph Theory and Line Outage Distribution Factor , 2020, IEEE Systems Journal.

[11]  Mark Rice,et al.  Towards Efficient N-x Contingency Selection Using Group betweenness Centrality , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[12]  T. J. Overbye,et al.  Multiple Element Contingency Screening , 2011, IEEE Transactions on Power Systems.

[13]  Haibo He,et al.  Supplementary File : Revealing Cascading Failure Vulnerability in Power Grids using Risk-Graph , 2013 .

[14]  I. Dobson,et al.  Risk Assessment of Cascading Outages: Methodologies and Challenges , 2012, IEEE Transactions on Power Systems.

[15]  Thomas J. Overbye,et al.  Easy SimAuto (ESA): A Python Package that Simplifies Interacting with PowerWorld Simulator , 2020, J. Open Source Softw..