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

Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network