Integrating Physics-Informed Neural Networks and Power Flow for Scalable Online Transient Analysis

Transient dynamic analysis is crucial for power system stability, particularly with increasing renewable integration, yet traditional numerical integration methods are computationally expensive for large-scale online applications. We propose a novel framework integrating Physics-Informed Neural Networks (PINNs) with an AC Power Flow (AC-PF) solver to efficiently simulate dynamic trajectories in power systems in a scalable fashion. A separate PINN is trained for each generator to accurately capture the differential equations of the generator dynamic model. The trained PINNs and an AC-PF solver iteratively update the dynamic and algebraic variables to simulate dynamic trajectories for the entire system. The PINN training process consists of an unsupervised stage to enforce physical laws, followed by a supervised stage leveraging simulation data for enhanced accuracy. As the predictor training is performed separately for individual generators, the method's computational complexity scales linearly with the number of generators in both training and testing. Experiments on a 3-generator 9-bus system demonstrate the very high accuracy of the developed method in simulating full system dynamic trajectories.