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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 todevelop better monitoring capabilities. In this work, a systematic approachbased on data-driven operator theoretic methods involving Koopman operator isproposed 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. Theproposed algorithm has three advantages: a) allows for real-time monitoring ofthe power system dynamics b) linear power system dynamics (this linear systemis usually in a higher dimensional feature space and is not same aslinearization of the underlying nonlinear dynamics) and c) computationally fastand less intensive when compared to the popular Extended Dynamic ModeDecomposition (EDMD) algorithm. The efficiency of the proposed algorithm isillustrated on an IEEE 9 bus system using synthetic data from the nonlinearmodel and on IEEE 39 bus system using synthetic data from the linearized model.

Data Driven Online Learning of Power System Dynamics