电力系统自动化2019，Vol.43Issue(1)：58-64,185,8.DOI:10.7500/AEPS20180404001
计及高阶统计量和深度学习的抗噪孤岛检测方法
Anti-noise Islanding Detection Approach Based on High-order Statistics and Deep Learning
摘要
Abstract
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.关键词
孤岛检测/高阶统计量/经验模态分解/多尺度奇异谱熵/深度学习Key words
islanding detection/high-order statistics/empirical mode decomposition (EMD)/multi-scale singular spectrum entropy (MSHOSSE)/deep learning引用本文复制引用
孔祥瑞,严正,徐潇源,谢伟..计及高阶统计量和深度学习的抗噪孤岛检测方法[J].电力系统自动化,2019,43(1):58-64,185,8.基金项目
国家重点研发计划资助项目(2017YFB0902800) This work is supported by National Key R&D Program of China (No. 2017YFB0902800). （2017YFB0902800）
