Physics-Informed Neural Ordinary Differential Equations for Parameter Estimation in Rotating Generators

Abstract

Accurate estimation of dynamic parameters of generators is crucial for constructing high-fidelity models for dynamic studies and ensuring the reliable operation of power systems. This paper develops a physics-informed neural ordinary differential equations (ODE) approach to learn the parameters of a generator’s dynamic model using grid sensor data. A physics-informed neural network is designed to represent the ODEs governing power system dynamics. A loss function is defined as the discrepancy between the dynamic response generated by the physics-informed neural network and synthesized grid sensor data, which mimics real-world measurements. The model parameters are iteratively updated using neural ODEs and the adjoint method. Unlike moving window-based approaches, the proposed neural ODE framework enhances parameter estimation by leveraging a longer observation period with a mini-batch scheme. Numerical studies on a 3-machine 9-bus system demonstrate that the proposed model significantly outperforms state-of-the-art baseline methods in dynamic parameter estimation accuracy.