Signal features for classification of power system disturbances using PMU data

Abstract

Event identification is one among numerous applications being researched for PMU data. This application is intended to increase visualization of power system events, as well as for protection and control, including verification of relay operation to detect any misoperations. This paper uses data from field as well as from simulation to test a large variety of features using two well-known classifiers on a common dataset to find the most suitable features for disturbance data recorded by PMUs. The approach also uses data from only one PMU instead of data from multiple PMUs used by researchers so far, thus significantly reducing the data to be processed. It is shown that simple observation-based features capturing shape and statistics of disturbance waveforms work better than some well-known features derived from domain transformations. Classification accuracy and speed achieved with these features are shown to be satisfactory and suitable for the intended applications.