  Stability analysis of a power network and its characterization (voltage or
angle) is an important problem in the power system community. However, these
problems are mostly studied using linearized models and participation factor
analysis. In this paper, we provide a purely data-driven technique for
small-signal stability classification (voltage or angle stability) and
influence characterization for a power network. In particular, we use Koopman
operator framework for data-driven discovery of the underlying power system
dynamics and then leverage the newly developed concept of information transfer
for discovering the causal structure. We further use it to not only identify
the influential states (subspaces) in a power network, but also to clearly
characterize and classify angle and voltage instabilities. We demonstrate the
efficacy of the proposed framework on two different systems, namely the 3-bus
system, where we reproduce the already known results regarding the types of
instabilities, and the IEEE 9-bus system where we identify the influential
generators and also the generator (and its states) which contribute to the
system instability, thus identifying the type of instability.
