  The outage of a transmission line may change the system phase angle
differences to the point that the system experience stress conditions. Hence,
the angle differences for post-contingency condition of a transmission lines
should be predicted in real time operation. However, online line-based phase
angle difference monitoring and prediction for power system stress assessment
is not a universal operating practice yet. Thus, in this paper, an online power
system stress assessment framework is proposed by developing a convolutional
neural network (CNN) module trained through Deep Learning approach. In the
proposed framework, the continuously streaming system phase angle data, driven
from phasor measurement units (PMUs) or a state estimator (SE), is used to
construct power system stress indices adaptive to the structure parameters of
the CNN module. Using this approach, any hidden patterns between phase angles
of buses and system stress conditions are revealed at low computation cost
while yielding accurate stress status and the severity of the stress. The
effectiveness and scalability of the proposed method has been verified on the
IEEE 118-bus and more importantly, on the PJM Interconnection system. Moreover,
outperformance of the proposed method is verified by comparing the results with
artificial neural network (ANN) and decision tree (DT).
