Learning Power System Dynamics with Nearly-Hamiltonian Neural Network

The ability to learn power system dynamic model and predict transient trajectories using data is crucial to realizing closed-loop control of the system with artificial intelligence. This paper proposes a Nearly-Hamiltonian neural network to predict transient trajectories and dynamic parameters of the power system by embedding energy conservation laws in the proposed neural network architecture. This inductive bias empowers the proposed model to learn the power system dynamics without explicitly using the exact functional form of the power system dynamic equations. The numerical study results on the single machine infinite bus system show that the proposed model produces accurate system trajectories and damping coefficient predictions. Furthermore, the proposed model significantly outperforms the baseline and Hamiltonian neural network.