Neural ODE and DAE Modules for Power System Dynamic Component Modeling

The time-domain simulation is fundamental for power system transient stability analysis. Accurate and reliable simulations depend on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a neural ODE module with external inputs and a neural differential-algebraic equations (DAEs) module are proposed for power system dynamic component modeling. Autoencoder-based frameworks of the proposed modules are put forward to improve the performance of trained models. The methodology of integrating neural dynamic models trained by the proposed neural modules into transient stability simulations is also demonstrated. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of black-box modeling, physics-data-integrated modeling, parameter inference, etc. Tests are carried out in the IEEE-39 system to prove the validity and potentiality of the proposed modules.