基于模糊聚类的NExT-ERA低频振荡类噪声辨识

低频振荡模态分析为电网的安全稳定运行提供了最基本的信息要素。针对环境激励下PMU量测的类噪声信号，讨论了自然激励技术结合特征系统实现算法(NExT-ERA)进行低频振荡模态识别的适用性，对非同步量测信号采用数据截断预处理后，利用该方法同样可以实现有效辨识。引入模糊C均值聚类算法对辨识结果中真伪模态进行自动拾取，提高了辨识精度。通过对IEEE4机11节点系统和IEEE16机68节点系统的仿真数据分析，表明所提出的方法对低频振荡类噪声信号具有较高的模态辨识能力和计算效率，在低频振荡广域监测中具有很好的应用前景。

Modal parameter identification of low frequency oscillation through NExT-ERA based on fuzzy clustering

Low frequency oscillation modal analysis provides the most basic elements of information for the safe and stable operation of power grid. To identify low frequency oscillation modal parameters based on ambient excited data from a wide area monitoring system (PMU), this paper investigates the use of the natural excitation technique (NExT) in conjunction with the eigensystem realization algorithm (ERA) for the modal analysis of power systems. At the same time, it introduces the fuzzy C clustering algorithm to automatically pick up the results, to better identify the authenticity of a modal. The method is capable of utilizing synchronous measured data from WAMS as well as unsynchronous measurements by the truncation approach. By modal identification of system for the IEEE four machine and IEEE sixteen machine simulation signals to verify the validity and high efficiency of this method to extract the dominant mode, which can also meet the needs of online and offline applications. This work is supported by National Natural Science Foundation of China (No. U1134205) and China Railway Corporation Major S&T Projects (No. 2015J005-A).