Comparison of Principal Component Analysis Techniques for PMU Data Event Detection

Abstract

Principal component analysis (PCA) is a dimensionality reduction technique often applied to process and detect events in large amounts of data collected by phasor measurement units (PMU) at transmission and distribution level. This article considers five different approaches to select an appropriate number of principal components, builds the statistical model of the PMU data online over a sliding window of 10 seconds and 1 minute, and evaluates the computation times and the accuracy of correct event detections with use of two statistical tests in a 1−hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities collected at different PMU substations