Neural Controlled Differential Equations for EMT-Level Surrogate Modeling of Grid-Forming Inverters

Abstract:The application of artificial intelligence methods in power electronic converter modeling is becoming increasingly widespread, but existing applications still face many challenges, such as difficulties in multi-time-scale hybrid analysis and the lack of physics-aware evaluation criteria and constraints, resulting in poor performance. This paper proposes a Neural Controlled Differential Equation (Neural CDE) framework for learning continuous-time surrogate models of grid-forming inverters for electromagnetic transient (EMT) simulation, which relaxes the constraint of fixed sampling rates and enables multi-time-scale control analysis. Then, an affine-control formulation with dual slow/fast pathways is proposed to capture the hierarchical and multiscale behavior of converter dynamics, and a physics-inspired regularization method is utilized to enhance stability and coherence. Evaluated on EMT-generated trajectories, the model accurately reproduces transient responses, preserves effective damping and the dominant oscillatory characteristics, and maintains bounded long-horizon rollouts. The results show that Neural CDE-based component modeling offers a physically consistent surrogate modeling approach for EMT-level simulation studies.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.16258 [cs.LG]

(or arXiv:2607.16258v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16258 Focus to learn more arXiv-issued DOI via DataCite

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