发表机构
Corporate Research Center, Midea Group（美的集团中央研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人工智能在电力电子变流器建模中面临的挑战，提出神经控制微分方程框架，用于电网形成逆变器电磁暂态仿真，通过仿射控制公式和正则化方法，实现多尺度分析，准确再现暂态响应，提供物理一致的替代建模方法。
AI 中文摘要
人工智能方法在电力电子变流器建模中的应用日益广泛，但现有应用仍面临诸多挑战，如多时间尺度混合分析困难以及缺乏物理感知评估标准和约束，导致性能不佳。本文提出一种神经控制微分方程（Neural CDE）框架，用于学习电网形成逆变器电磁暂态（EMT）仿真的连续时间替代模型，放宽了固定采样率的约束并实现多时间尺度控制分析。接着提出具有双慢/快路径的仿射控制公式来捕捉变流器动态的分层和多尺度行为，并利用物理启发的正则化方法增强稳定性和连贯性。在EMT生成的轨迹上评估，该模型能准确再现暂态响应，保留有效阻尼和主导振荡特性，并保持有界的长期滚动。结果表明基于Neural CDE的组件建模为EMT水平仿真研究提供了物理一致的替代建模方法。
英文摘要
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.
