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In the context of high penetration of renewables, the need to build dynamicmodels of power system components based on accessible measurement data hasbecome urgent. To address this challenge, firstly, a neural ordinarydifferential equations (ODE) module and a neural differential-algebraicequations (DAE) module are proposed to form a data-driven modeling frameworkthat accurately captures components' dynamic characteristics and flexiblyadapts to various interface settings. Secondly, analytical models anddata-driven models learned by the neural ODE and DAE modules are integratedtogether and simulated simultaneously using unified transient stabilitysimulation methods. Finally, the neural ODE and DAE modules are implementedwith Python and made public on GitHub. Using the portal measurements, threesimple but representative cases of excitation controller modeling, photovoltaicpower plant modeling, and equivalent load modeling of a regional power networkare carried out in the IEEE-39 system and 2383wp system. Neural dynamicmodel-integrated simulations are compared with the original model-based ones toverify the feasibility and potentiality of the proposed neural ODE and DAEmodules.

Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling