  Modularized Koopman Bilinear Form (M-KBF) is presented to model and predict
the transient dynamics of microgrids in the presence of disturbances. As a
scalable data-driven approach, M-KBF divides the identification and prediction
of the high-dimensional nonlinear system into the individual study of
subsystems; and thus, alleviating the difficulty of intensively handling high
volume data and overcoming the curse of dimensionality. For each subsystem,
Koopman bilinear form is applied to efficiently identify its model by
developing eigenfunctions via the extended dynamic mode decomposition method
with an eigenvalue-based order truncation. Extensive tests show that M-KBF can
provide accurate transient dynamics prediction for the nonlinear microgrids and
verify the plug-and-play modeling and prediction function, which offers a
potent tool for identifying high-dimensional systems. The modularity feature of
M-KBF enables the provision of fast and precise prediction for the microgrid
operation and control, paving the way towards online applications.
