2020/07/12 by Mohammad Rasoul Narimani, Narimani, Mohammad Rasoul, Hao Huang +10
Engineering · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Mathematics #Optimization and Control (math.OC) #Railway Systems and Energy Efficiency
paper · pdf · doi:10.48550/arxiv.2007.07009
openalex publication_date 2020/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying the multiple critical components in power systems whose absence\ntogether has severe impact on system performance is a crucial problem for power\nsystems known as (N-x) contingency analysis. However, the inherent\ncombinatorial feature of the N-x contingency analysis problem incurs by the\nincrease of x in the (N-x) term, making the problem intractable for even\nrelatively small test systems. We present a new framework for identifying the\nN-x contingencies that captures both topology and physics of the network. Graph\ntheory provides many ways to measure power grid graphs, i.e. buses as nodes and\nlines as edges, allowing researchers to characterize system structure and\noptimize algorithms. This paper proposes a scalable approach based on the group\nbetweenness centrality (GBC) concept that measures the impact of multiple\ncomponents in the electric power grid as well as line outage distribution\nfactors (LODFs) that find the lines whose loss has the highest impact on the\npower flow in the network. The proposed approach is a quick and efficient\nsolution for identifying the most critical lines in power networks. The\nproposed approach is validated using various test cases, and results show that\nthe proposed approach is able to quickly identify multiple contingencies that\nresult in violations.\n