Identification of Key Components in Power System Based on Improved Multi-Objective Genetic Algorithm

In recent years, extreme weather events have become increasingly frequent worldwide, greatly threatening the normal operation of power grids. The impact of disaster weather on switching equipment in power grids, particularly circuit breakers in overhead lines and utility poles, may trigger cascading failures throughout the entire system, leading to large-scale blackouts. To study the effects of disaster weather on power grid components, it is necessary to quantify the impact of failures in different overhead lines and nodes on the grid through multiple objectives, reflecting the degree of damage caused by disaster weather to the power grid and providing a reliable reference standard for pre-disaster prevention work. This paper proposes an improved multi-objective optimization algorithm based on power flow betweenness index, which takes into account the number of overloaded lines in case of cascading faults, the load loss rate of the grid, and the connectivity loss rate of the grid's functional zones, is proposed specifically for the fast and efficient identification of combinations of critical components that have the most severe impact on the grid when subjected to catastrophic weather. The results show that the methodology achieves an accurate assessment of the critical components of the distribution grid.