Koji Yamashita

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

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...