  This paper proposes a novel transient stability assessment tool for networked
microgrids based on neural Lyapunov methods. Assessing transient stability is
formulated as a problem of estimating the dynamic security region of networked
microgrids. We leverage neural networks to learn a local Lyapunov function in
the state space. The largest security region is estimated based on the learned
neural Lyapunov function, and it is used for characterizing disturbances that
the networked microgrids can tolerate. The proposed method is tested and
validated in a grid-connected microgrid, three networked microgrids with mixed
interface dynamics, and the IEEE 123-node feeder. Case studies suggest that the
proposed method can address networked microgrids with heterogeneous interface
dynamics, and in comparison with conventional methods that are based on
quadratic Lyapunov functions, can characterize the security regions with much
less conservativeness.
