In this paper, a data-driven approach to characterize influence in a power network is presented. The characterization is based on the notion of information transfer in a dynamical system. In particular, we use the information transfer based definition of influence in a dynamical system and provide a data-driven approach to identify the influential state(s) and generators in a power network. Moreover, we show how the data-based information transfer measure can be used to characterize the type of instability of a power network and also identify the states causing the instability.
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