The power quality detection and synchrophasor measurement based on compressive sensing

被引:1
作者
Chen, Yufang [1 ]
Liu, Zhixin [2 ]
机构
[1] Inner Mongolia Minzu Univ, Coll Engineer, Tongliao 028000, Inner Mongolia, Peoples R China
[2] Inner Mongolia Elect Informat Vocat Tech Coll, Hohhot 010070, Inner Mongolia, Peoples R China
来源
OPTIK | 2023年 / 272卷
关键词
Compressive sensing; Power quality; Synchrophasor; Measurement; MODEL;
D O I
10.1016/j.ijleo.2022.169922
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In order to improve the effect of power quality detection and synchrophasor measurement, this paper studies power quality detection and synchrophasor measurement with the support of compressive sensing technology. Based on the DFT synchrophasor measurement, this paper uses the RDFT recursive formula to simplify the DFT calculation amount, and uses the least squares estimation method to fit the phase angle sequence to calculate the system frequency. According to the obtained frequency estimation value, re-sampling is carried out to correct the original sam-pling point, and the frequency secondary calculation is iteratively performed, the detection ac-curacy of high frequency and phase angle, and the linear measurement of the signal is realized by using the Gaussian random measurement matrix, and the compressed sensing signal is obtained. The experimental data statistics show that the model proposed in this paper for power quality detection and synchrophasor measurement based on compressive sensing has a good effect.
引用
收藏
页数:14
相关论文
共 50 条
[41]   Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection [J].
Liao, Longlong ;
Li, Kenli ;
Yang, Canqun ;
Liu, Jie .
SENSORS, 2019, 19 (09)
[42]   Block-Wise Compressive Sensing Based Multiple Line Outage Detection for Smart Grid [J].
Yang, Fang ;
Tan, Jingbo ;
Song, Jian ;
Han, Zhu .
IEEE ACCESS, 2018, 6 :50984-50993
[43]   Saliency Detection for Compressive Sensing Measurements [J].
Li, Hongliang ;
Lu, Ke ;
Xue, Jian ;
Dai, Feng ;
Zhang, Yongdong .
SENSING AND IMAGING, 2021, 22 (01)
[44]   Saliency Detection for Compressive Sensing Measurements [J].
Hongliang Li ;
Ke Lu ;
Jian Xue ;
Feng Dai ;
Yongdong Zhang .
Sensing and Imaging, 2021, 22
[45]   Multiuser Detection via Compressive Sensing [J].
Shim, Byonghyo ;
Song, Byungkwen .
IEEE COMMUNICATIONS LETTERS, 2012, 16 (07) :972-974
[46]   Compressive Sensing Based Target Detection in Delay-Doppler Radars [J].
Teke, Oguzhan ;
Arikan, Orhan ;
Gurbuz, Ali Cafer .
2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
[47]   MIMO Signal Multiplexing and Detection Based on Compressive Sensing and Deep Learning [J].
Liu, Chanzi ;
Zhou, Qingfeng ;
Wang, Xindi ;
Chen, Kaiping .
IEEE ACCESS, 2019, 7 :127362-127372
[48]   Compressive Sensing Based Sparse Event Detection in Wireless Sensor Networks [J].
Yan, Wenjie ;
Wang, Qiang ;
Shen, Yi .
2011 6TH INTERNATIONAL ICST CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM), 2011, :964-969
[49]   A Performance Comparative Analysis of Block Based Compressive Sensing and Line Based Compressive Sensing [J].
Ebrahim, Mansoor ;
Adil, Syed Hasan ;
Nawaz, Daniyal .
ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2018, 8 (02) :2809-2813
[50]   Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction [J].
Lutovac, Budimir ;
Dakovic, Milos ;
Stankovic, Srdjan ;
Orovic, Irena .
RADIOENGINEERING, 2017, 26 (01) :309-315