  This paper develops a novel data-driven technique to compute the
participation factors for nonlinear systems based on the Koopman mode
decomposition. Provided that certain conditions are satisfied, it is shown that
the proposed technique generalizes the original definition of the linear
mode-in-state participation factors. Two numerical examples are provided to
demonstrate the performance of our approach: one relying on a canonical
nonlinear dynamical system, and the other based on the two-area four-machine
power system. The Koopman mode decomposition is capable of coping with a large
class of nonlinearity, thereby making our technique able to deal with
oscillations arising in practice due to nonlinearities while being fast to
compute and compatible with real-time applications.
