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Online power system event identification and classification is crucial toenhancing the reliability of transmission systems. In this paper, we develop adeep neural network (DNN) based approach to identify and classify power systemevents by leveraging real-world measurements from hundreds of phasormeasurement units (PMUs) and labels from thousands of events. Two innovativedesigns are embedded into the baseline model built on convolutional neuralnetworks (CNNs) to improve the event classification accuracy. First, we proposea graph signal processing based PMU sorting algorithm to improve the learningefficiency of CNNs. Second, we deploy information loading based regularizationto strike the right balance between memorization and generalization for theDNN. Numerical studies results based on real-world dataset from the EasternInterconnection of the U.S power transmission grid show that the combination ofPMU based sorting and the information loading based regularization techniqueshelp the proposed DNN approach achieve highly accurate event identification andclassification results.

Power System Event Identification based on Deep Neural Network with Information Loading