基于流聚类的PMU异常数据辨识算法

为保证同步相量测量装置(phasor measurement unit，PMU)采集数据的准确应用，须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此，文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发，提出基于该信息熵的异常数据辨识框架。在此框架基础上，基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies，BIRCH)算法提出PMU异常数据辨识算法；然后,对所提出的算法进行原型实现，并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明，与一类支持向量机(one-class support vector machine，OCSVM)算法与间隙统计算法相比，文中算法的准确度及实时性均具有较强的优势。

PMU abnormal data identification algorithm based on stream clustering

In order to ensure the accurate application of the data collected by the phasor measurement unit (PMU),it is necessary to eliminate the abnormal data in its measured values. The existing PMU abnormal data identification algorithm has the disadvantages of high algorithm complexity,difficulty in online updating,difficulty in the calibration of multi-source data,and difficulty in application relying on multi-source data. In this paper,an abnormal data identification framework is proposed based on the PMU event data and abnormal data model and the definition of PMU abnormal data identification information entropy. On the basis of the framework,a PMU abnormal data identification algorithm is proposed based on the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. The proposed algorithm is implemented,and an algorithm experiment is carried out for the PMU dataset of a substation. The experimental results show that the proposed algorithm has better accuracy and real-time performance than one-class support vector machine (OCSVM) algorithm and gap statistic algorithm.