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.

翻译：含有大量可再生能源的电力系统动态行为正日益呈现非线性特征。这种非线性源于可再生能源的间歇性特性及其电力电子器件的开关行为。因此，在盲源分离方法中处理这些非线性问题至关重要。本文提出一种基于Copula统计量的线性混合依赖源盲源分离方法，该方法通过构建Copula密度函数来度量源分量信号间的非线性依赖关系。假设源信号满足平稳性条件，该方法通过最小化估计源Copula密度函数与依赖结构Copula密度函数之间的Kullback-Leibler散度实现分离。将所提方法应用于经典4机11节点系统时域分析数据，大量仿真结果表明，基于Copula统计量的方法收敛速度更快，且在干扰信号比指标上优于当前最先进的依赖源盲源分离方法。