电力系统中强迫振荡源的可辨识性极限
Identifiability Limits of Forced Oscillation Sources in Power Systems
- The University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究电力系统中强迫振荡源的可辨识性，提出射影射线框架和加权最近射线定位器，区分四种定位失败机制，并通过数值实验揭示源定位不等于通道唯一辨识及鲁棒性保证。
AI中文摘要：
强迫振荡源能否被唯一地定位，取决于可用的测量数据和候选干预字典。本文考虑一个通过某个物理干预通道作用的、具有未知恒定幅值的正弦信号。由于该信号的幅值和相位未知，每个候选谐波响应仅在一个非零复数标量倍数下可观测，因此在测量空间中定义了一条射影射线。本文推导了精确局部化的充要条件，并提出了加权最近射线定位器以及基于射影分离的确定性恢复界。本文提出了一个框架，区分了源定位失败的四种机制：机制失配、特征投影损失、结构不可辨识性以及病态条件或模型误差。在Kundur两区域系统上的数值研究展示了良好分离的恢复、依赖于机制的终端决策、测量引起的模糊性以及近乎共线的特征对模型扰动的敏感性。随后，使用IEEE--NASPI竞赛模型进一步检验了广泛候选字典、终端特征源覆盖以及通过测量增强消除模糊性。结果表明，正确定位源母线本身并不能建立内部强迫通道的唯一辨识，也不能保证对测量和模型不确定性的鲁棒性。
英文摘要：
Whether a forced-oscillation source can be uniquely localized depends jointly on the available measurements and the candidate intervention dictionary. This paper considers a single unknown constant-amplitude sinusoid acting through one of physical intervention channels. Because its amplitude and phase are unknown, each candidate harmonic response is observable only up to a nonzero complex scalar and therefore defines a projective ray in measurement space. A necessary-and-sufficient condition is derived for exact localization, together with a weighted nearest-ray locator and a deterministic recovery bound based on projective separation. The paper proposes a framework distinguishing four mechanisms of source localization failure: mechanism mismatch, feature-projection loss, structural nonidentifiability, and poor conditioning or model error. Numerical studies on the Kundur two-area system illustrate well-separated recovery, mechanism-dependent terminal decisions, measurement-induced ambiguity, and the sensitivity of nearly collinear signatures to model perturbations. Then, IEEE--NASPI Contest models are used to further examine broad candidate dictionaries, terminal-feature source coverage, and ambiguity removal through measurement augmentation. The results show that correctly locating the source bus does not by itself establish unique identification of the internal forcing channel or robustness to measurement and model uncertainty.
