发表机构
Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出电力系统动力学神经网络解释工具，基于NTK方法分析机器学习代理训练性能，开发自适应损失加权策略，在SM和换流器代理模型上验证，可改进NN架构与训练策略的设计。
AI 中文摘要
据我们所知，本文首次在电力系统文献中提出了解释电力系统动力学机器学习代理模型训练性能的分析工具。电力系统仿真日益受到换流器接口资源和快速控制回路产生的刚性及多时间尺度动力学的挑战。机器学习代理作为处理这种复杂性、加速动态仿真的有前景工具出现，但它们的性能仍难以解释，这限制了其应用。本文基于电力系统中的小信号特征值分析，采用神经正切核（NTK）方法。NTK对学习性能提供模态解释，识别出快速衰减的误差模态与缓慢收敛的误差模态。这种关联解释了电力系统动态模型中的物理刚性和时间尺度分离如何在神经网络（NN）训练中表现为优化刚性。基于此分析，我们开发了自适应损失加权策略，以改进并解释为何结构感知神经网络架构（如ActNet）比普通NN表现更好。我们在同步电机（SM）和电力电子换流器的物理信息机器学习代理模型上评估了所提出的方法。本文引入的方法可提供必要的分析工具，以解释和改进机器学习代理的性能，为系统的、物理感知的NN架构和训练策略设计铺平道路。通过超越试错开发，这些工具揭示了训练动态和故障模态，支持更可靠的设计决策，并增强工程应用中对机器学习代理的信心。
英文摘要
This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics. Power system simulations are increasingly challenged by stiff and multi-timescale dynamics arising from converter-interfaced resources and fast control loops. Machine learning surrogates emerge as promising tools to handle this complexity and accelerate dynamic simulations. However, their performance remains difficult to interpret, which limits their adoption. Building on the small-signal eigenvalue analysis in power systems, this paper uses the Neural Tangent Kernel (NTK) method. NTK delivers a modal interpretation of the learning performance, identifying error modes that decay rapidly versus others that converge slowly. This connection explains how physical stiffness and timescale separation in power system dynamic models appear as optimization stiffness during Neural Network (NN) training. Based on this analysis, we develop adaptive loss-weighting strategies to improve and explain why structure-aware neural architectures, such as ActNet, perform better than vanilla NNs. We assess the proposed approach on physics-informed machine learning surrogate models of \acp{SM} and power electronic converters. The methods introduced in this paper can deliver the necessary analytical tools to interpret and improve the performance of machine learning surrogates, paving the way for the systematic, physics-aware design of NN architectures and training strategies. By moving beyond trial-and-error development, these tools reveal training dynamics and failure modes, support more reliable design decisions, and strengthen confidence in machine-learning surrogates for engineering applications.
