  In the context of high penetration of renewables, the need to build dynamic
models of power system components based on accessible measurement data has
become urgent. To address this challenge, firstly, a neural ordinary
differential equations (ODE) module and a neural differential-algebraic
equations (DAE) module are proposed to form a data-driven modeling framework
that accurately captures components' dynamic characteristics and flexibly
adapts to various interface settings. Secondly, analytical models and
data-driven models learned by the neural ODE and DAE modules are integrated
together and simulated simultaneously using unified transient stability
simulation methods. Finally, the neural ODE and DAE modules are implemented
with Python and made public on GitHub. Using the portal measurements, three
simple but representative cases of excitation controller modeling, photovoltaic
power plant modeling, and equivalent load modeling of a regional power network
are carried out in the IEEE-39 system and 2383wp system. Neural dynamic
model-integrated simulations are compared with the original model-based ones to
verify the feasibility and potentiality of the proposed neural ODE and DAE
modules.
