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.

翻译：随着可再生能源大规模接入，电力系统的动态特性因资源间歇性及电力电子器件切换而日益呈现非线性。因此，当该电力系统受到扰动时，准确识别其动态振荡模式对于启动适当的预防或校正控制措施至关重要。本文提出一种基于copula统计量的高阶盲源识别算法，以应对模态分析中的这些非线性动态特性。该方法结合希尔伯特变换（HOBI-HT）和迭代过程（HOBMI），能够从低至单个通道的观测信号中识别所有模态及模型阶数。我们通过数值仿真信号和经典4机11节点测试系统时域仿真记录数据验证了所提方法的性能。仿真结果表明，该方法在准确性和有效性上均优于现有最优方法。