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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, alleviates the difficulty of intensively handling high volume data and overcomes the curse of dimensionality. For each subsystem, Koopman bilinear form is established to efficiently identify its model by identifying isotypic eigenfunctions via the Extended Dynamic Mode Decomposition (EDMD) 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 with reconfiguration feature. The modularity feature of M-KBF enables the provision of fast and precise prediction for the power grid operation and control, paving the way towards online applications.