Vulnerable Sequence Identification for Sequential Cascading Failure Analysis in Power Grid

Sizhe He, Yadong Zhou, Yujie Yang, Xinlu Li, Yang Liu, Ting Liu

Abstract: Over the past two decades, frequent blackouts have highlighted the critical importance of ensuring the security of power grid. The integration of cyber and physical domains through intelligent devices has increased the risk of asynchronous attacks that can trigger cascading failures. One effective approach to mitigating this threat is to identify vulnerable sequences, which are sequences of transmission lines that can cause large-scale failures in the power grid. This article proposes an event-triggered hybrid system model to characterize the generation and propagation mechanism of sequential cascading failures. In addition, the problem of identifying vulnerable sequences is formulated as a Markov decision process. To solve the sequential decision problem in approximately contiguous states, a vulnerable sequence identification method based on reinforcement learning is designed. Furthermore, a topological feature embedding algorithm based on matrix decomposition is proposed to improve identification performance. To evaluate the effectiveness of the proposed method, various numerical experiments are conducted on IEEE 30-bus and ACTIVSg 200-bus systems. The results of these experiments demonstrate the excellent performance of the proposed method.

External IDs:dblp:journals/tii/HeZYLLL25