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

TL;DR

This paper introduces neural ODE and DAE modules for data-driven power system component modeling, enabling accurate dynamic simulations that adapt to various measurement data and interface settings, validated on IEEE systems.

Contribution

It proposes a novel neural ODE and DAE framework for power system modeling, integrating analytical and data-driven models for flexible, accurate dynamic component simulation.

Findings

Neural modules accurately model power system components.

Integrated simulation matches traditional model-based results.

Modules are implemented in Python and publicly available.

Abstract

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.