A Data-Driven Identification of Key Links in Power System Based on Random Matrix Theory

Abstract

Large-scale power outages in the power system are usually caused by the instability of key links in the system. Therefore, the identification of key links is of great significance to ensure the safe and stable operation of the power system. For this reason, this paper proposes an identification method for key links of power system based on random matrix theory (RMT). Firstly, some theorems of the random matrix theory are introduced, and then the data matrix model is constructed. On this basis, combining the ring law and the Marchenko-Pastur (MP) law, key links evaluation indicators are proposed to evaluate the severity and uniformity of the fault impact, then the identification framework of the key links in the power system is constructed. Finally, this method is verified in the New England 39-bus system, and the simulation results show that the method proposed in this paper is effective to identify the key links in the power system.