AI 中文总结
研究电力系统变流器驱动稳定性问题，建立扰动空间交互灵敏度框架，揭示模式和状态对扰动的响应机制，提供解析表达式和可解释机制，为理解扰动驱动交互重新配置及复杂系统先进控制器设计提供基础。
AI 中文摘要
随着可再生能源的整合加速了能源部门的脱碳进程，电力系统越来越多地由变流器接口发电主导。这导致出现多种振荡，可能引发级联断开并可能损坏发电机。为解决这些关键问题，现代电力系统通常采用经典参与因子（PF）分析，将振荡模式（特征值）与控制器状态变量相关联。然而，一个关键但此前未探索的挑战是捕捉扰动发生后观察到的交互重新配置。由于PF分析限于固定运行点，在结构上无法分析扰动下这些与事件相关的模式 - 状态交互。在此，我们建立了一个在扰动空间中制定的交互灵敏度框架，揭示模式和状态对扰动的响应机制。该框架提供了解析表达式和可物理解释的机制，解释了为何特定控制调整会增强或降低稳定性，揭示了超越传统稳定性分析的因果关系。更广泛地说，该公式为理解由状态空间方程描述的高维动态网络中扰动驱动的交互重新配置提供了一般分析基础，并支持复杂系统中的先进控制器设计。
英文摘要
As the integration of renewables accelerates the decarbonization process in the energy sector, power systems are becoming increasingly dominated by converter-interfaced generation. However, this results in the emergence of multiple oscillations, which can trigger cascading disconnections and potentially damage generators. To address these critical issues, modern power systems commonly employ classic participation factor (PF) analyses, which relate oscillatory modes (eigenvalues) to controller state variables. While these methods are effective, a critical yet previously unexplored challenge involves capturing the interaction reconfigurations observed after a perturbation occurs. Because PF analyses are limited to fixed operating points, they are structurally unable to analyze these event-dependent mode--state interactions under perturbations. Here, we establish an interaction sensitivity framework that is formulated in perturbation space, uncovering the mechanisms by which modes and states respond to perturbations. This framework provides analytic expressions and physically interpretable mechanisms that explain why specific control adjustments enhance or reduce stability, revealing causal relationships beyond conventional stability analysis. More broadly, this formulation provides a general analytical basis for understanding perturbation-driven interaction reconfigurations in high-dimensional dynamical networks described by state-space equations, and supports advanced controller design in complex systems.
