Sai Akhil R. Konakalla, and Raymond A. de Callafon
Feature Based Grid Event Classification from Synchrophasor Data
This paper presents a method for automatic classification of power disturbance events in an electric grid by means of distributed parameter estimation and clustering techniques of synchro-phasor data produced by phasor measurement units (PMUs). Disturbance events detected in the PMU data are subjected to a parameter estimation routine to extract features that include oscillation frequency, participation factor, damping factor and post and pre-event frequency offset. The parameters are used to classify events and classification rules are deduced on the basis of a training set of known events using nonlinear programming. Once the classification rules are set, the approach can be used to automatically classify events not seen in the training set. The proposed algorithm is illustrated on a Power Standards Lab microPMU system data for which frequency disturbance events were measured at UCSD over several months.