Failure Propagation Graphs for Studying Cascading Failure Propagation in Power Networks

Abstract

Cascading failure, characterized by the widespread propagation of failure events, is a common cause for severe blackouts in power networks. Strengthening critical branches in a power network is crucial for mitigating the risk of blackouts resulting from cascading failures. In this article, we propose a time-efficient greedy search method to identify critical branches in a power network. We address the challenge of computational constraints by using a failure propagation graph, which accurately captures the critical failure propagation patterns based on cascading failure simulation. Our approach minimizes cascading failure risk while strategically reinforcing a limited number of branches. The failure-propagation-graph greedy-search (FPG-GS) algorithm selects candidate branches based on cascading failure simulation and iteratively identifies the most crucial branches. Our experimental results on different power systems demonstrate the superior performance and efficiency of the FPG-GS algorithm compared to existing methods. In addition, our study highlights the importance of strategic branch selection, showing that reinforcing one-fifth of the branches can achieve a mitigation rate exceeding 80%.