PI‐Formers: An AI4S Approach for Transient Rotor Angle Prediction in Renewable‐Integrated Power System Under Large Disturbances

The increasing integration of renewable energy sources and the deep fusion with power electronics have introduced new challenges to the stability of power systems, making traditional prediction methods inadequate for real‐time, high‐precision control. This paper presents a power system‐specific methodology designed to solve high‐dimensional, nonlinear dynamic prediction problems, which is demonstrated through an application to rotor‐angle trajectory prediction in power systems. Our proposed method, which combines model‐based approaches with data‐driven techniques, leverages the strengths of both paradigms. At its core, the method employs the Transformer deep learning architecture to capture the temporal dynamics of system transients. Additionally, physics‐informed neural networks are integrated to ensure that key physical principles are embedded within the model, enhancing both accuracy and convergence speed. While the proposed methodology has the potential for broader applications in dynamic modeling, this paper focuses on its application to rotor‐angle trajectory prediction, which is a critical task in transient stability assessment. This scenario was chosen as a representative use case to validate the effectiveness and feasibility of the approach under realistic power system conditions. The method was tested on the modified IEEE‐39 bus system, with results showing that it delivers fast, accurate, and physically consistent predictions without a large amount of labelled data. This demonstrates the method's potential for supporting online control and maintaining system stability in future power grids.