  Medium-voltage direct-current (MVDC) ship-board microgrids (SMGs) are the
state-of-the-art architecture for onboard power distribution in navy. These
systems are considered to be highly dynamic due to high penetration of power
electronic converters and volatile load patterns such as pulsed-power load
(PPL) and propulsion motors demand variation. Obtaining the dynamic model of an
MVDC SMG is a challenging task due to the confidentiality of system components
models and uncertainty in the dynamic models through time. In this paper, a
dynamic identification framework based on a temporal convolutional neural
network (TCN) is developed to learn the system dynamics from measurement data.
Different kinds of testing scenarios are implemented, and the testing results
show that this approach achieves an exceptional performance and high
generalization ability, thus holding substantial promise for development of
advanced data-driven control strategies and stability prediction of the system.
