发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高逆变器资源渗透率电力系统，提出集成EMT小信号分析工具EMT-SSA，从PSS/E快照构建全阶EMT模型并线性化，输出模态指标并归因设备，在Kundur系统验证。
AI 中文摘要
同步发电被逆变器资源（IBRs）持续替代，引入了快速变流器控制动态，其特征频率超出了经典机电频带。传统小信号稳定性工具通常以相量域构建，将电气量表示为基频下缓慢变化的相量，因此无法解析运行中日益出现的次同步和变流器驱动动态。电磁暂态（EMT）建模能够捕捉这些动态，但现有的EMT仿真器产生时域波形，而非评估稳定性风险所需的模态指标（特征值、阻尼比和参与因子）。本文提出EMT-SSA，一个面向高IBR渗透率电力系统EMT级小信号分析的综合工具套件。从标准PSS/E系统快照（.raw/.dyr）出发，它将该模型转换为保真度增强的EMT表示，求解扩展潮流以获得网络和设备控制器的稳态平衡点，并线性化全阶EMT模型以形成系统状态矩阵，进而生成特征值、振荡模式和参与因子分析结果，覆盖同步发电机、跟网型和构网型逆变器、输电线路及负荷的设备库。EMT-SSA工具套件识别弱阻尼或不稳定模态，并将其归因于特定设备。该工具在Kundur两区域系统上进行了演示，其模态结果与PSS/E NEVA进行了基准对比。
英文摘要
The ongoing replacement of synchronous generation by inverter-based resources (IBRs) introduces fast converter control dynamics whose characteristic frequencies extend beyond the classical electromechanical band. Conventional small-signal stability tools are commonly formulated in the phasor domain, representing electrical quantities as slowly varying phasors at the fundamental frequency, and therefore cannot resolve the sub-synchronous and converter-driven dynamics that increasingly arise in operation. Electromagnetic Transient (EMT) modeling captures these dynamics, but established EMT simulators produce time-domain waveforms rather than the modal indicators (eigenvalues, damping ratios, and participation factors) needed to assess stability risk. This paper presents EMT-SSA, an integrated tool suite for EMT-level small-signal analysis of IBR-rich power systems. From a standard PSS/E system snapshot (.raw/.dyr), it converts the model into an EMT representation with enhanced fidelity, solves an extended power flow for the steady-state equilibrium points of both the network and the device controllers, and linearizes a full-order EMT model to form the system state matrix, from which it produces eigenvalues, oscillation modes, and participation factor analysis results over a device library spanning synchronous generators, grid-following and grid-forming inverters, transmission lines, and loads. The EMT-SSA tool suite identifies poorly damped or unstable modes and attributes them to specific devices. The tool is demonstrated on the Kundur two-area system, with its modal results benchmarked against PSS/E NEVA.
Comments6 pages, 4 figures, 2 tables. Accepted for presentation at the 2026 IEEE Industry Applications Society (IAS) Annual Meeting
