An identification method based on MTF visualization and improved DenseNet for power quality disturbances

doi:10.19781/j.issn.1673-9140.2024.04.012

Home > Archive>Volume 39, Issue 4, 2024 >102-111. DOI:10.19781/j.issn.1673-9140.2024.04.012

An identification method based on MTF visualization and improved DenseNet for power quality disturbances
DOI:
                        10.19781/j.issn.1673-9140.2024.04.012
                    
CSTR:
                        [cstr]
                    
Author:
                        SHI Shuai1SHI Shuai
(College of Electrical Power Engineering,Shanghai University of Electric Power, Shanghai 200090, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
CHEN Ziwen1CHEN Ziwen
(College of Electrical Power Engineering,Shanghai University of Electric Power, Shanghai 200090, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
HUANG Dongmei2HUANG Dongmei
College of Computer Science and Technology, Shanghai University of Electric Power,Shanghai 200090, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
HE Qi3HE Qi
College of Information Technology,Shanghai Ocean University,Shanghai 201306, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
SUN Yuan4SUN Yuan
College of Mathematics and Physics,Shanghai University of Electric Power, Shanghai 200090,China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
HU Wei5HU Wei
College of Economics and Management,Shanghai University of Electric Power, Shanghai 200090, China)
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:((1.College of Electrical Power Engineering,Shanghai University of Electric Power, Shanghai 200090, China;2.College of Computer Science and Technology, Shanghai University of Electric Power,Shanghai 200090, China;3.College of Information Technology,Shanghai Ocean University,Shanghai 201306, China;4.College of Mathematics and Physics,Shanghai University of Electric Power, Shanghai 200090,China;5.College of Economics and Management,Shanghai University of Electric Power, Shanghai 200090, China))
Clc Number:TM721
Fund Project:

Article

Figures

Metrics

Reference

Cited by

Materials

Comments

Abstract:

Aiming at the problems of complex process and insufficient refinement of artificial feature selection in traditional power quality disturbances (PQDs) classifier, a new PQD recognition method based on Markov transition field visualization and improved DenseNet is proposed. Firstly, the one-dimensional PQD signal is mapped into a two-dimensional image by MTF. Then, the image is input into an improved DenseNet with a new channel attention mechanism. Finally, the network is trained to extract features from a large number of samples by itself, so as to realize the correct recognition of PQD signals. The example results show that: in the case of no noise and signal-to-noise ratio of 20dB and 30dB, the proposed improved DenseNet can effectively overcome the shortcomings of traditional methods, such as strong subjectivity of feature selection and poor anti-noise performance. It can better extract the feature information of complex PQD, and has a high recognition rate for complex PQD.

Key words:power quality disturbance;Markov translate field;visualization;dense convolutional networks;channel attention mechanism;classification and recognition

Get Citation

时帅,陈子文,黄冬梅,贺琪,孙园,胡伟.基于MTF可视化和改进DenseNet神经网络的电能质量扰动识别算法[J].电力科学与技术学报英文版,2024,39(4):102-111. SHI Shuai, CHEN Ziwen, HUANG Dongmei, HE Qi, SUN Yuan, HU Wei. An identification method based on MTF visualization and improved DenseNet for power quality disturbances[J]. Journal of Electric Power Science and Technology,2024,39(4):102-111.

Copy

Article Metrics

Abstract:
PDF:
HTML:
Cited by:

History

Received:
Revised:
Adopted:
Online: September 10,2024
Published:

Get Citation

Related Videos

Share

Article Metrics

History

Article QR Code