Identification of Vulnerable Lines in Smart Grid Systems Based on Affinity Propagation Clustering

In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph, we investigate the problem of vulnerable line identification from a clustering perspective. By jointly considering the topological parameters and the electrical properties, we propose an affinity propagation based bus clustering algorithm to classify buses into clusters, where the center of each cluster represents the most influential bus in each partition. According to the clustering results, we design a vulnerable line identification scheme, which captures different types of potential critical lines in the smart grid system. Experiments over the IEEE-39 bus system demonstrate the effectiveness and correctness of our proposed algorithm.