  Identifying the multiple critical components in power systems whose absence
together has severe impact on system performance is a crucial problem for power
systems known as (N-x) contingency analysis. However, the inherent
combinatorial feature of the N-x contingency analysis problem incurs by the
increase of x in the (N-x) term, making the problem intractable for even
relatively small test systems. We present a new framework for identifying the
N-x contingencies that captures both topology and physics of the network. Graph
theory provides many ways to measure power grid graphs, i.e. buses as nodes and
lines as edges, allowing researchers to characterize system structure and
optimize algorithms. This paper proposes a scalable approach based on the group
betweenness centrality (GBC) concept that measures the impact of multiple
components in the electric power grid as well as line outage distribution
factors (LODFs) that find the lines whose loss has the highest impact on the
power flow in the network. The proposed approach is a quick and efficient
solution for identifying the most critical lines in power networks. The
proposed approach is validated using various test cases, and results show that
the proposed approach is able to quickly identify multiple contingencies that
result in violations.
