何时相信相量域设备模型用于构网型变流器主导微电网的机电稳定性分析？
When Can Phasor-Domain Device Models Be Trusted for Electromechanical Stability Analysis of Grid-Forming Converter-Dominated Microgrids?
AI总结：
针对构网型变流器主导微电网，提出一种基于鲁棒稳定性的认证方法，通过结构化奇异值判据判断忽略内环和滤波器动态的简化模型是否可靠，案例验证了有效性。
摘要：
构网型变流器主导的微电网通常使用降阶的相量域机电模型进行分析，但这些模型的有效性常被视为理所当然。假设终端电压参考的理想内环跟踪，这些模型忽略了电磁暂态时间尺度上的内环和滤波器动态以简化稳定性分析。本文指出，这些被忽略的动态可能导致系统失稳，从而使基于理想内环跟踪模型得出的稳定性结论无效。为解决这一跨时间尺度稳定性问题，我们将理想内环跟踪稳定性结论的有效性表述为一个鲁棒稳定性认证问题。由电磁暂态引起的降阶变流器模型与实际变流器模型之间的失配被表示为嵌入在理想内环跟踪反馈回路周围的结构化不确定性。这产生了一个频率分辨的交互指数和一个结构化奇异值充分判据，用于确定在给定的电磁暂态不确定性权重下，理想内环跟踪模型的稳定性结论何时可以被认证。不确定性权重可以从详细的电磁暂态模型或终端参考响应测量中获得。案例研究证实，所提出的判据能正确认证模型有效性并识别可信度的丧失。我们还证明了基于测量的不确定性权重与基于模型的权重高度匹配，这使得无需访问内环模型即可部署该方法。
英文摘要：
Grid-forming (GFM) converter-dominated microgrids are often analyzed using reduced-order phasor-domain electromechanical GFM models, but the validity of these models is often taken for granted. Assuming ideal inner-loop tracking (IILT) of terminal-voltage references, these models neglect the inner-loop and filter dynamics at the electromagnetic-transient (EMT) timescale to simplify stability analysis. This paper argues that such neglected dynamics can destabilize the system, invalidating the stability conclusions drawn from the IILT model. To address this cross-timescale stability issue, we formulate the validity of the IILT stability conclusion as a robust-stability certification problem. The EMT-induced model mismatch between the reduced-order converter model and the actual converter model is represented as a structured uncertainty embedded around the IILT feedback loop. This yields a frequency-resolved interaction index and a structured singular-value sufficient certificate for determining when the stability conclusion of the IILT model can be certified with respect to a prescribed EMT uncertainty weight. The uncertainty weight can be obtained from detailed EMT models or terminal reference-response measurements. Case studies confirm that the proposed certificate correctly certifies model validity and identifies the loss of trustworthiness. We also demonstrate that the measurement-based uncertainty weights closely match the model-based ones, which enables deployment without accessing inner-loop models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
