A Vulnerability Identification Method of Power System Based on Infomap Community Discovery Algorithm

Large-scale blackouts are often caused by cascading failures. When some components in the power system fail, they often spread and eventually cause serious accidents. Therefore, the analysis and research on the vulnerable parts of power system can help improve the overall robustness of power grid. Based on the N-l safety test of power system, correlation matrix is constructed to quantify the relationship between different lines. We construct a directed weighted correlation network which can consider both static topology and dynamic power flow information of power system. For the correlation network, Infomap community discovery algorithm is used to divide the grid community, and the interaction between different power system components is visualized to obtain the community with the highest vulnerability level. Finally, we adopt cascading failures simulation and find that almost all the fault chains appear in the communities with the highest vulnerability. Therefore, the correlation network obtained by N-l security test can dig out the scope of chain faults, which provides guidance for the rapid identification of vulnerable components in power grid.