|本期目录/Table of Contents|

[1]杨挺,尚昆,袁博,等.基于压缩感知的盲源信号分离检测方法[J].天津大学学报(自然科学版),2016,(11):1138-1143.[doi:10.11784/tdxbz201512031]
 Yang Ting,Shang Kun,Yuan Bo,et al.Blind Signal Separation Detection Method Based on Compressed Sensing[J].Journal of Tianjin University,2016,(11):1138-1143.[doi:10.11784/tdxbz201512031]
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基于压缩感知的盲源信号分离检测方法()
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《天津大学学报(自然科学版)》[ISSN:0493-2137/CN:12-1127/N]

卷:
期数:
2016年11
页码:
1138-1143
栏目:
电气与自动化工程
出版日期:
2016-11-15

文章信息/Info

Title:
Blind Signal Separation Detection Method Based on Compressed Sensing
作者:
杨挺1 尚昆1 袁博2 张燕萍3 盆海波1
1. 天津大学智能电网教育部重点实验室,天津 300072;2. 国网河北省电力公司经济技术研究院,石家庄 050000;3. 国网天津市电力公司培训中心,天津300181
Author(s):
Yang Ting1 Shang Kun 1 Yuan Bo 2 Zhang Yanping3 Pen Haibo 1
1.Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
2.State Grid Hebei Electric Power Company Economic Research Institute, Shijiazhuang 050000, China
3. Training Centre of State Grid Tianjin Electric Power Corporation, Tianjin 300181, China
关键词:
电能质量信号 压缩感知 稀疏成分分析 盲分离
Keywords:
power quality signal compressed sensing sparse component analysis blind separation
分类号:
TM73;TP393
DOI:
10.11784/tdxbz201512031
文献标志码:
A
摘要:
接入电网的各种分布式电源、非线性负荷使得电能质量污染问题日益严重, 对各种电能质量信号进行特征提取与正确分离是改善电能质量的切入点.针对电能质量信号的结构特点, 构建了压缩感知电能质量信号分离模型, 并针对该模型提出一种基于压缩感知的盲源信号分离检测算法CS-SCA(compressed sensing-sparse component analysis).根据已有的电能质量信号理论知识, 确定电能质量信号在频域的稀疏性, 进而对信号预处理降噪.通过两步法解决预处理后电能质量观测信号的分离检测问题.第1步通过观测信号向量方向特性估计出电能质量源信号个数, 并利用线性聚类估计混合矩阵; 第2步采用压缩感知恢复算法分离得出电能质量源信号.通过实验验证, 提出算法所分离出基波、各次谐波信号分离信干比均大于10 dB.
Abstract:
Large numbers of distributed generations and non-linear loads make the power quality environment increasingly complex. How to feature extract and separate all kinds of power quality signals have become the key to improving the power quality. Based on the characteristics of power quality signals,this paper established the underdetermined blind source separation model,analyzed the similarities in basic mathematical model between compressed sensing(CS)and blind source separation(BSS),and then proposed a novel compressed sensing-sparse component analysis algorithm CS-SCA. Firstly,the sparsity of power quality signals was obtained by the time-frequency domain analysis,and then each of the observed signals was preprocessed to reduce the noise. Two-step CS-SCA was presented to achieve the power quality signals source separation. In the first step,the mixing matrix was estimated using linear clustering based on the estimation of the number of source signals through the direction characteristics of observed signals vector. In the second step,a CS reconstruction algorithm was used to separate the power quality source signals. The simulation results show that the SIR(signal-to-interference-ratio)of fundamental and harmonic components is higher than 10 dB,which proves that the proposed method can effectively separate each power quality source signal.

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备注/Memo

备注/Memo:
收稿日期: 2015-12-08; 修回日期: 2016-03-29.
基金项目: 国际科技合作专项资助项目(2013DFA11040); 国家自然科学基金资助项目(61571324); 国家高技术研究发展计划(863计划)资助项目(2015AA050202); 天津市自然科学基金重点资助项目(16JCZDJC30900).
作者简介: 杨挺(1979—), 男, 博士, 教授, 博士生导师.
通讯作者: 杨挺, yangting@tju.edu.cn.
更新日期/Last Update: 2016-11-10