Identify Critical Branches with Cascading Failure Chain Statistics and Hypertext-Induced Topic Search Algorithm

Capacity expansion for transmission branches is an effective way to reduce the threat of cascading failure. However, decision making for the optimal expansion plan may incur high computational burden. A practical way to address this issue is to find out some critical branches as the candidates. Thus, this paper proposes a novel simulation data based analytical approach to identify those critical transmission branches that have higher importance in the propagation of cascading failures. First, a large number of cascading failure chains are sampled and then partitioned into stages. Second, a comprehensive metric is proposed to quantify the interaction strength among the failure branches in adjacent stages. Then, a directed weighted graph is constructed to depict the statistic features of the interactions among branches. Third, the hypertext-induced topic search (HITS) algorithm is used to rate and rank this graph's vertices, and finally the key branches are identified with this ranking Case studies on IEEE 118-bus benchmark show that the proposed approach is able to identify the critical branches that are more favorable in the capacity upgrade.