A Physics-Informed Deep Learning Paradigm for Transient Power Angle Stability Assessment
Xiang Li, Siyuan Chen, Jun Zhang, Jiemai Gao, Yuyang Bai
Abstract
Transient stability assessment (TSA) plays an essential role in the safe and stable operation of power system. The traditional time-domain simulation method and direct method have great limitation in modeling and computing, which can not meet the need of fast and accurate assessment of power system stability. Therefore, combined physics model with data-driven algorithm, this paper proposes a physics-informed deep learning paradigm to solve this problem. In the data-driven algorithm, recurrent neural networks are trained to predict the rotor speed and power angle with the guiding of the power system’s physical information. Finally, the simulation on IEEE14 system is carried out by using PSS/E software, which proved that the proposed method can predict the power angle trajectory quickly and accurately.