Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics

TL;DR

This paper introduces a novel blind source separation method using copula statistics to effectively handle non-linear dependencies in power system signals, especially with renewable energy sources, improving convergence and separation quality.

Contribution

It proposes a copula-based blind source separation technique that models non-linear dependencies, outperforming existing methods in power system signal analysis.

Findings

Faster convergence of the proposed method.

Better separation performance with lower interference-to-signal ratio.

Effective handling of non-linear dependencies in power system signals.

Abstract

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.