Identification of Power System Oscillation Modes using Blind Source Separation based on Copula Statistic

TL;DR

This paper introduces a novel high-order blind source separation method based on copula statistics for accurately identifying oscillation modes in nonlinear power systems with renewable energy integration.

Contribution

It proposes a new algorithm combining copula-based blind source separation with Hilbert transform and iteration to identify system modes from minimal observation channels.

Findings

Outperforms existing methods in accuracy and effectiveness.

Successfully identifies all oscillation modes and model order.

Validated on numerical and recorded simulation data.

Abstract

The dynamics of a power system with large penetration of renewable energy resources are becoming more nonlinear due to the intermittence of these resources and the switching of their power electronic devices. Therefore, it is crucial to accurately identify the dynamical modes of oscillation of such a power system when it is subject to disturbances to initiate appropriate preventive or corrective control actions. In this paper, we propose a high-order blind source identification (HOBI) algorithm based on the copula statistic to address these non-linear dynamics in modal analysis. The method combined with Hilbert transform (HOBI-HT) and iteration procedure (HOBMI) can identify all the modes as well as the model order from the observation signals obtained from the number of channels as low as one. We access the performance of the proposed method on numerical simulation signals and recorded data from a simulation of time domain analysis on the classical 11-Bus 4-Machine test system. Our simulation results outperform the state-of-the-art method in accuracy and effectiveness.