Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory

With the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing identification methods are based on complex network theory and use static graphs to model the propagation of cascading faults, which cannot consider the impact of wind power output fluctuations. To address this issue, this paper introduces and improves the maximum influence model of the community network to identify the critical lines. By developing the time-sequence cascading faults graph, the proposed method can comprehensively consider the impact of wind power output fluctuations on the propagation of cascading faults. Based on the influence maximization model in time-sequence cascading faults graph (IMTG), the line fault influence calculation algorithm (LFIC) and improved critical line identification algorithm (ILIT) are proposed to identify the critical transmission lines. In order to verify the effectiveness of the proposed method, case studies are carried out on the IEEE 39-bus system and the IEEE 118-bus system. The results show that the critical lines identified by the proposed method can effectively cope with the fluctuation of wind power output in the future, and can provide timely feedback for power system operators.