Data-driven dynamic modeling for inverter-based resources using neural networks.

ABSTRACT: Dynamic models are a cornerstone of power system stability and control. The growing penetration of inverter-based resources, driven by global decarbonization, significantly complicates power system dynamics. For large-scale power systems, existing dynamic models of these resources have long struggled to accurately capture their complex behaviors, limited primarily by explicit formulations based on simplified physical governing equations. This study presents a data-driven modeling approach that uses neural networks to learn and represent these dynamics exclusively from accessible data. Its tailored architecture combining long short-term memory network for temporal dependencies with a cross layer to model nonlinear feature interactions. Physical constraints from an inverter dynamic model are

SUBMITTER: Yang K 

PROVIDER: S-EPMC12753704 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature