用神经微分方程建模电网型逆变器，实现高精度电磁暂态仿真。
Neural Controlled Differential Equations for EMT-Level Surrogate Modeling of Grid-Forming Inverters
- 用神经控制微分方程学习连续时间逆变器模型，支持多尺度分析。
- 在真实电磁暂态数据上复现暂态响应，长时滚动保持稳定。
- 融合物理约束正则化，适合电力系统仿真研究者使用。
人工智能在电力电子变换器建模中的应用日益广泛，但现有方法仍面临多时间尺度混合分析困难、缺乏物理感知的评估标准与约束等问题，导致性能不佳。本文提出一种神经控制微分方程（Neural CDE）框架，用于学习电网型逆变器的连续时间代理模型，以支持电磁暂态（EMT）仿真。该框架放宽了固定采样率的限制，实现多时间尺度控制分析。进一步提出带有双通道（慢/快）的仿射控制结构，捕捉变换器动态的层次化与多尺度特性，并引入物理启发的正则化方法增强模型稳定性与一致性。在基于EMT生成的轨迹上评估，模型准确再现暂态响应，保持有效阻尼与主导振荡特性，且长期滚动预测结果始终有界。结果表明，基于Neural CDE的元件建模为EMT级仿真研究提供了一种物理一致的代理建模方法。
原文摘要 · Abstract (English)
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.
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