The time-domain simulation is the fundamental tool for power system transient
stability analysis. Accurate and reliable simulations rely 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 modified neural ODE module and a neural
differential-algebraic equations (DAEs) module for power system dynamic
component modeling are proposed. The modules adopt an autoencoder to raise the
dimension of state variables, model the dynamics of components with artificial
neural networks (ANNs), and keep the numerical integration structure. In the
neural DAE module, an additional ANN is used to calculate injection currents.
The neural models can be easily integrated into time-domain simulations. With
datasets consisting of sampled curves of input variables and output variables,
the proposed modules can be used to fulfill the tasks of parameter inference,
physics-data-integrated modeling, black-box modeling, etc., and can be easily
integrated into power system dynamic simulations. Some simple numerical tests
are carried out in the IEEE-39 system and prove the validity and potentiality
of the proposed modules.Comment: 8 pages, 4 figures, 1 tabl