[1]王武亮,江辉.基于多任务贝叶斯压缩感知的电能质量信号重构[J].深圳大学学报理工版,2021,38(1):77-84.[doi:10.3724/SP.J.1249.2021.01077]
　WANG Wuliang and JIANG Hui.Power quality signal reconstruction based on multitask Bayesian compressive sensing[J].Journal of Shenzhen University Science and Engineering,2021,38(1):77-84.[doi:10.3724/SP.J.1249.2021.01077]
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基于多任务贝叶斯压缩感知的电能质量信号重构()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第38卷
期数:
2021年第1期
页码:
77-84
栏目:
电子与信息科学
出版日期:
2021-01-12

文章信息/Info

Title:
Power quality signal reconstruction based on multitask Bayesian compressive sensing
文章编号:
202101010
作者:
王武亮; 江辉
深圳大学物理与光电工程学院，广东深圳 518060
Author(s):
WANG Wuliang and JIANG Hui
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
关键词:
信息处理技术; 压缩感知; 电能质量; 多任务贝叶斯; 信号重构
Keywords:
information processing technology;  compressive sensing;  power quality;  multitask Bayesian;  signal reconstruction
分类号:
TM711；TM93
DOI:
10.3724/SP.J.1249.2021.01077
文献标志码:
A
摘要:
压缩感知技术突破奈奎斯特采样定理限制，能够有效降低数据的存储和传输成本，只需较少的样本就能对电能质量信号进行重构，对电能质量的检测分析具有重要意义．针对电能质量信号，提出一种基于多任务贝叶斯压缩感知理论的电能质量信号压缩重构算法，该算法选择快速傅里叶变换基作为稀疏基对电能质量信号进行稀疏处理，将所得稀疏向量的实部和虚部构成两个压缩重构任务；利用超参数估计的共享机制，考虑两个任务间数据的内在相关性，对电能质量信号进行重构．仿真结果表明，该算法在压缩重构含复杂扰动的电能质量信号时，其抗噪性能和重构精度均优于正交匹配追踪算法和贝叶斯压缩感知算法，更加适用于含有复杂扰动的电能质量信号的压缩重构．
Abstract:
Compressed sensing technology breaks through the limitation of Nyquist sampling theorem, and can effectively reduce the cost of data storage and transmission. Only a few samples are needed to reconstruct the power quality signal by using the compressed sensing technology, which is of great significance for the detection and analysis of power quality. We propose an algorithm based on multitask Bayesian compressed sensing (MT-BCS) theory for power quality signals compression and reconstruction in this paper. The power quality signals are changed to sparse vectors by taking the fast Fourier transform basis as the sparse basis. The real and imaginary parts of sparse vectors are then treated as two compression and reconstruction tasks. Considering the internal correlation of data corresponding to these two tasks, the power quality signal is reconstructed using the sharing mechanism of hyperparameter estimation. The simulation results show that the algorithm is superior to the orthogonal matching pursuit algorithm and the Bayesian compressed sensing algorithm in anti-noise performance and reconstruction accuracy, and is more suitable for compressing and reconstructing power quality signals with complex disturbance.

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备注/Memo

备注/Memo:
Received：2020-06-02；Accepted：2020-06-28
Foundation：National Natural Sicence Foundation of China (51707123); Shenzhen Basic Research Foundation (JCYJ201908081652 01648)
Corresponding author：Professor JIANG Hui. E-mail: huijiang@szu.edu.cn
Citation：WANG Wuliang, JIANG Hui. Power quality signal reconstruction based on multitask Bayesian compressive sensing［J］. Journal of Shenzhen University Science and Engineering, 2021, 38(1): 77-84.（in Chinese）
基金项目：国家自然科学基金资助项目(51707123)；深圳市基础研究计划资助项目(JCYJ20190808165201648)
作者简介：王武亮(1995—)，深圳大学硕士研究生．研究方向：智能信息处理．E-mail：szu_wwl@163.com
引文：王武亮，江辉．基于多任务贝叶斯压缩感知的电能质量信号重构［J］. 深圳大学学报理工版，2021，38(1)：77-84.
更新日期/Last Update: 2021-01-26