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：随着可再生能源资源在电力系统中占比显著增加，系统动态特性呈现日益增强的非线性。这种非线性源于可再生能源的间歇性特征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统计学的线性混合依赖源盲源分离方法，该方法通过构建Copula密度函数度量源分量信号间的非线性依赖关系。源信号被假设为平稳信号。该方法最小化估计源Copula密度函数与依赖结构Copula密度函数之间的Kullback-Leibler散度。将所提方法应用于经典4机11节点系统的时域分析数据。大量仿真结果表明，与现有面向依赖源的先进盲源分离方法相比，基于Copula统计学的所提方法收敛速度更快，且在干扰信号比指标上表现更优。