PMU abnormal data identification algorithm based on stream clustering

Abstract

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.

Keywords

• phasor measurement unit (pmu)
• abnormal data
• event data
• identification framework
• information entropy
• stream clustering