  Continuous monitoring of the spatio-temporal dynamic behavior of critical
infrastructure networks, such as the power systems, is a challenging but
important task. In particular, accurate and timely prediction of the
(electro-mechanical) transient dynamic trajectories of the power grid is
necessary for early detection of any instability and prevention of catastrophic
failures. Existing approaches for the prediction of dynamic trajectories either
rely on the availability of accurate physical models of the system, use
computationally expensive time-domain simulations, or are applicable only at
local prediction problems (e.g., a single generator). In this paper, we report
the application of two broad classes of data-driven learning models -- along
with their algorithmic implementation and performance evaluation -- in
predicting transient trajectories in power networks using only streaming
measurements and the network topology as input. One class of models is based on
the Koopman operator theory which allows for capturing the nonlinear dynamic
behavior via an infinite-dimensional linear operator. The other class of models
is based on the graph convolutional neural networks which are adept at
capturing the inherent spatio-temporal correlations within the power network.
Transient dynamic datasets for training and testing the models are synthesized
by simulating a wide variety of load change events in the IEEE 68-bus system,
categorized by the load change magnitudes, as well as by the degree of
connectivity and the distance to nearest generator nodes. The results confirm
that the proposed predictive models can successfully predict the
post-disturbance transient evolution of the system with a high level of
accuracy.
