计及高阶统计量和深度学习的抗噪孤岛检测方法

分布式电源持续的规模化接入给微电网运行引入了显著的不确定性与噪声,增加了配电网监视的难度。而孤岛检测设备易受电网扰动干扰而误动作,导致分布式电源被切除运行,孤岛检测装置必须能够在噪声环境中准确区分判别扰动与孤岛情形。文中将基于多尺度高阶奇异谱熵的深度学习概念应用于孤岛检测问题,提出一种结合经验模态分解与高阶奇异谱熵的新型混合深度学习架构。作为经验模态分解后的信号处理方法,多尺度高阶奇异谱熵结合多分辨率高阶统计分析与谱分析并以熵值作为特征提取输出,进而通过深度学习架构对所提取的孤岛与扰动特征量进行训练及测试。仿真结果表明所提方法能够实现孤岛的准确检测,从而避免分布式电源退出运行。

Anti-noise Islanding Detection Approach Based on High-order Statistics and Deep Learning

The increasing penetration of distributed generators(DGs)brings significant uncertainty and noises to microgrid, which leads to the increasing difficulty of microgrid monitoring. The islanding detection devices may make misjudgment because they are prone to be interfered by grid disturbance, thus causing the consequence of DGs out of service. The islanding detection devices must be able to accurately distinguish islands from grid disturbance in a noise environment. This paper introduces the concept of deep learning based on the multi-scale high-order singular spectrum entropy(MSHOSSE)into islanding detection. And a novel deep learning framework combining empirical mode decomposition(EMD)and high-order singular spectrum entropy is proposed. As a signal processing method after EMD, the MSHOSSE combines multi-resolution high-order statistics analysis and spectrum analysis, and take the entropy as the feature to output. Then the intrinsic features of islanding and grid disturbance can be extracted for training and testing in the deep learning framework. The simulation results show that the proposed method can achieve accurate detection of islands, thus avoiding running out of DGs.