Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach identify classify events by leveraging real-world measurements from hundreds phasor measurement units (PMUs) labels thousands events. Two innovative designs embedded into baseline model built on convolutional networks (CNNs) improve accuracy. First, propose graph signal processing PMU sorting algorithm learning efficiency CNNs. Second, deploy information loading regularization strike right balance between memorization generalization for DNN. Numerical results dataset Eastern Interconnection U.S grid show that combination techniques help proposed DNN achieve highly accurate results.