Physics- Informed Neural Network Based Dynamic Estimation for Power Grid Using a Kuramoto-Like Model

Dynamic state estimation for power grid is an important task for power system monitoring and operation. In this paper, we build the power network dynamic model with the form of the Kuramoto model, and use the physics-informed neural network (PINN) to capture the dynamics of the power system. Physics-informed neural network is a new deep learning method that can incorporate the mathematical models describing power grid dynamics into network training to achieve high accurate estimation, while using less training data. We also verify the effectiveness by a simulation on a two generators-one machine power grid system.