  This paper presents a wide-area event classification in transmission power
grids. The deep neural network (DNN) based classifier is developed based on the
availability of data from time-synchronized phasor measurement units (PMUs).
The proposed DNN is trained using Bayesian optimization to search for the best
hyperparameters. The effectiveness of the proposed event classification is
validated through the real-world dataset of the U.S. transmission grids. This
dataset includes line outage, transformer outage, frequency event, and
oscillation events. The validation process also includes different PMU outputs,
such as voltage magnitude, angle, current magnitude, frequency, and rate of
change of frequency (ROCOF). The simulation results show that ROCOF as input
feature gives the best classification performance. In addition, it is shown
that the classifier trained with higher sampling rate PMUs and a larger dataset
has higher accuracy.
