  The dynamics of a power system with a significant presence of renewable
energy resources are growing increasingly nonlinear. This nonlinearity is a
result of the intermittent nature of these resources and the switching behavior
of their power electronic devices. Therefore, it is crucial to address these
nonlinearity in the blind source separation methods. In this paper, we propose
a blind source separation of a linear mixture of dependent sources based on
copula statistics that measure the non-linear dependence between source
component signals structured as copula density functions. The source signals
are assumed to be stationary. The method minimizes the Kullback-Leibler
divergence between the copula density functions of the estimated sources and of
the dependency structure. The proposed method is applied to data obtained from
the time-domain analysis of the classical 11-Bus 4-Machine system. Extensive
simulation results demonstrate that the proposed method based on copula
statistics converges faster and outperforms the state-of-the-art blind source
separation method for dependent sources in terms of interference-to-signal
ratio.
