  With the advancement of sensing and communication in power networks,
high-frequency real-time data from a power network can be used as a resource to
develop better monitoring capabilities. In this work, a systematic approach
based on data-driven operator theoretic methods involving Koopman operator is
proposed for the online identification of power system dynamics. In particular,
a new algorithm is provided, which unlike any previously existing algorithms,
updates the Koopman operator iteratively as new data points are acquired. The
proposed algorithm has three advantages: a) allows for real-time monitoring of
the power system dynamics b) linear power system dynamics (this linear system
is usually in a higher dimensional feature space and is not same as
linearization of the underlying nonlinear dynamics) and c) computationally fast
and less intensive when compared to the popular Extended Dynamic Mode
Decomposition (EDMD) algorithm. The efficiency of the proposed algorithm is
illustrated on an IEEE 9 bus system using synthetic data from the nonlinear
model and on IEEE 39 bus system using synthetic data from the linearized model.
