  This paper presents a novel parameter calibration approach for power system
stability models using automatic data generation and advanced deep learning
technology. A PMU-measurement-based event playback approach is used to identify
potential inaccurate parameters and automatically generate extensive simulation
data, which are used for training a convolutional neural network (CNN). The
accurate parameters will be predicted by the well-trained CNN model and
validated by original PMU measurements. The accuracy and effectiveness of the
proposed deep learning approach have been validated through extensive
simulation and field data.
