  Artificial intelligence and machine learning is enhancing electric grids by
offering data analysis tools that can be used to operate the power grid more
reliably. However, the complex nonlinear dynamics, particularly when coupled
with multi-scale interactions among Inverter-based renewable energy Resources,
calls for effective algorithms for power system application. This paper
presents affective novel algorithm to detect various nonlinear dynamics, which
is built upon: the Sparse Identification of Nonlinear Dynamics method for
nonlinear dynamics detection; and Hankel Alternative View of Koopman method for
multi-scale decomposition. We show that, by an appropriate integration of the
strengths of the two, the mixed algorithm not only can detect the nonlinearity,
but also it distinguishes the nonlinearity caused by coupled Inverter-based
resources from the more familiar ones caused synchronous generators. This shows
that the proposal algorithm can be a promising application of artificial
intelligence and machine learning for data measure-based analysis to support
operation of power system with integrated renewables.
