ABSTRACT High renewable energy penetration introduces significant uncertainty into power system operation and increases the risk of static security violations. To address this issue, this paper proposes a framework for extracting operational safety rules and identifying vulnerable nodes in power grids with high renewable energy penetration. First, an improved time‐series association rule mining algorithm is developed by incorporating sliding time windows, consequent constraints, and rule‐size constraints, thereby improving mining efficiency and reducing redundant rules. Then, a topology‐constrained complex network is constructed based on the lift of node voltage state‐change rules and the electrical proximity between nodes. Four centrality indicators are combined through the CRITIC‐TOPSIS method to obtain the final vulnerability ranking. The proposed method is validated on the modified IEEE 10‐machine 39‐bus system. Regional‐level and component‐level time‐series association rules are extracted, revealing implicit relationships between operating characteristics and security‐limit violations, as well as interactions among component‐level violation state changes. The results demonstrate that the proposed method can effectively identify vulnerable nodes and provide useful support for static security assessment of power grids.
Huang et al. (Thu,) studied this question.