Insulator nondestructive testing based on VGGNet algorithm

Ma Lixin; Dou Chenfei; Song Chencan; Yang Tianxiao

doi:10.12086/oee.2021.200072

Article navigation > Opto-Electronic Engineering > 2021 Vol. 48 > No. 1 > 200072

Next Article Previous Article

Ma L X, Dou C F, Song C C, et al. Insulator nondestructive testing based on VGGNet algorithm[J]. Opto-Electron Eng, 2021, 48(1): 200072. doi: 10.12086/oee.2021.200072

Citation:

Ma L X, Dou C F, Song C C, et al. Insulator nondestructive testing based on VGGNet algorithm[J]. Opto-Electron Eng, 2021, 48(1): 200072. doi: 10.12086/oee.2021.200072

Insulator nondestructive testing based on VGGNet algorithm

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Fund Project: National Natural Science Foundation of China (61205076)

More Information

^*Corresponding author: Ma Lixin, E-mail: ma_eeepsi@163.com

Received Date 06 March 2020

Revised Date 19 May 2020

Published Date 15 January 2021

Abstract

Abstract

In the power system, it is difficult to detect the insulator's deterioration in operation. Aiming at this problem, this thesis applies the convolution neural network algorithm to evaluate the insulator's deterioration degree based on the deep analysis of the principle and structure of the convolution neural network model. Firstly, the power frequency flashover test was conducted on the insulator to produce three states as follows: no discharge, weak discharge, and strong discharge. Moreover, the Ultraviolet imager was applied to collect the insulator's ultraviolet images in different discharge state to establish the ultraviolet images sample library. Subsequently, the VGGNet framework neural network algorithm was applied to perform the classification training and the state-prediction evaluation on the samples so as to eventually achieve the purpose of judging whether the insulator is degraded. From the experimental results, it can be seen that the accuracy rate of the algorithm is as high as 98.4%, which has broad application prospects in the insulator's degradation detection. Furthermore, it provides a mentality for the reliability detection of other power equipments.
- CNN /
- ultraviolet discharge /
- nondestructive testing /
- insulator

FullText(HTML)

References

[1]	吕志宁. 输电线路常见故障分析与检测方法综述[J]. 自动化与仪器仪表, 2020(1): 161–164, 168. Google Scholar Lv Z N. A survey of common faults analysis and detection methods for transmission line[J]. Autom Instrum, 2020(1): 161–164, 168. Google Scholar
[2]	黄云程, 郑云海, 许萍萍. 应用红外检测技术评估复合绝缘子劣化状态[J]. 电工电气, 2019(6): 48–52. Google Scholar Huang Y C, Zheng Y H, Xu P P. Degradation state of composite insulator evaluated by infrared detection technology[J]. Electrotech Electr, 2019(6): 48–52. Google Scholar
[3]	田治仁, 金立军. 基于彩色可见光图像的绝缘子污秽等级判别[J]. 电工电能新技术, 2015, 34(9): 70–74. doi: 10.3969/j.issn.1003-3076.2015.09.012 CrossRef Google Scholar Tian Z R, Jin L J. Detection of insulator contamination grades based on digital image processing[J]. Adv Technol Electr Eng Energy, 2015, 34(9): 70–74. doi: 10.3969/j.issn.1003-3076.2015.09.012 CrossRef Google Scholar
[4]	马立新, 周小波, 朱润, 等. 紫外检测电晕放电强度量化分级[J]. 光电工程, 2016, 43(1): 1–5. doi: 10.3969/j.issn.1003-501X.2016.01.001 CrossRef Google Scholar Ma L X, Zhou X B, Zhu R, et al. The quantitative classification of corona discharge intensity of UV detection[J]. Opto-Electron Eng, 2016, 43(1): 1–5. doi: 10.3969/j.issn.1003-501X.2016.01.001 CrossRef Google Scholar
[5]	Asimakopoulou G E, Kontargyri V T, Tsekouras G J, et al. Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators[J]. IET Sci Meas Technol, 2009, 3(1): 90–104. doi: 10.1049/iet-smt:20080009 CrossRef Google Scholar
[6]	裴少通. 基于红外紫外成像检测技术的绝缘子运行状态分析与评估[D]. 北京: 华北电力大学(北京), 2019. Google Scholar Pei S T. Analysis and evaluation of insulator operation status based on infrared and ultraviolet imaging detection technology[D]. Beijing: North China Electric Power University (Beijing), 2019. Google Scholar
[7]	左国玉, 马蕾, 徐长福, 等. 基于跨连接卷积神经网络的绝缘子检测方法[J]. 电力系统自动化, 2019, 43(4): 101–106. Google Scholar Zuo G Y, Ma L, Xu C F, et al. Insulator detection method based on cross-connected convolutional neural network[J]. Autom Electr Power Syst, 2019, 43(4): 101–106. Google Scholar
[8]	Woon W L, El-Hag A, Harbaji M. Machine learning techniques for robust classification of partial discharges in oil–paper insulation systems[J]. IET Sci Meas Technol, 2016, 10(3): 221–227. doi: 10.1049/iet-smt.2015.0076 CrossRef Google Scholar
[9]	Pei S T, Liu Y P, Ji X X, et al. UV-flashover evaluation of porcelain insulators based on deep learning[J]. IET Sci Meas Technol, 2018, 12(6): 770–776. doi: 10.1049/iet-smt.2017.0465 CrossRef Google Scholar
[10]	Czajka A, Bowyer K W, Krumdick M, et al. Recognition of image-orientation-based iris spoofing[J]. IEEE Trans Inf For Secur, 2017, 12(9): 2184–2196. doi: 10.1109/TIFS.2017.2701332 CrossRef Google Scholar
[11]	Chen Y, Xu Y. Detection and localization of untwisted strands in transmission lines using cascaded shape filtering and color filtering[C]//2015 IEEE Workshop on Signal Processing Systems (SIPS), 2015: 1–6. Google Scholar
[12]	Zhang Z J, Zhang W, Zhang D D, et al. Comparison of different characteristic parameters acquired by UV imager in detecting corona discharge[J]. IEEE Trans Dielectr Electr Insul, 2016, 23(3): 1597–1604. doi: 10.1109/TDEI.2016.005499 CrossRef Google Scholar
[13]	Wang S H, Lv F C, Liu Y P. Estimation of discharge magnitude of composite insulator surface corona discharge based on ultraviolet imaging method[J]. IEEE Trans Dielectr Electr Insul, 2014, 21(4): 1697–1704. doi: 10.1109/TDEI.2014.004358 CrossRef Google Scholar
[14]	林锦发. 基于深度学习的遥感图像语义分割方法研究[D]. 广州: 广东工业大学, 2019. Google Scholar Lin J F. Research on semantic segmentation of remote sensing image based on deep learning[D]. Guangzhou: Guangdong University of Technology, 2019. Google Scholar
[15]	裴斐. 基于深度卷积神经网络的图像风格迁移系统研究[D]. 银川: 宁夏大学, 2019. Google Scholar Pei F. Research on image style migration system based on deep convolutional neural network[D]. Yinchuan: Ningxia University, 2019. Google Scholar
[16]	裴少通, 刘云鹏, 陈同凡, 等. 基于BOA-SVM的劣化绝缘子红外图谱诊断方法[J]. 电测与仪表, 2018, 55(24): 11–16. Google Scholar Pei S T, Liu Y P, Chen T F, et al. Infrared spectrum diagnosis method of deteriorated insulators based on BOA-SVM[J]. Electr Meas Instrum, 2018, 55(24): 11–16. Google Scholar

Overview

Overview

Overview: The electricity system structure of our country is very complicated. To maintain the stability and the reliability of the electricity system, we need to have all kinds of reliable and stable equipments, and the insulator is one of them. Insulators are devices which are installed between the conductors of different potentials or conductors and the grounding components. They can also tolerate the effect of voltage and mechanical stress. The main function of the insulators is to realize electrical insulation and mechanical fastening. They are important devices of the electricity system. Whether the insulation function of the insulator is in good condition will influence the lifespan and safely running of the whole circuit. Therefore, how to test the deterioration level of the working insulator is a substantial research topic. What this paper works on is using UV image camera to collect ultraviolet images of the insulators under different discharging states and building an ultraviolet images sample bank. This paper uses the VGGNET framework neural network algorithm to classify the training and statement, and forecast assess the sample in order to estimate whether insulators are deteriorated, and contrast and analysis to other algorithms. VGGNET model: by repeatedly stacking the convolution kernel whose receptive field is 3×3, the non-linearity of the model is improved, so that it has stronger feature learning ability and better recognition effect for the image data with small feature difference of insulator UV discharge images. In addition, it is better than using the large-scale coil. Compared with the product kernel, it effectively reduces the number of parameters and makes it have higher training efficiency. According to the results of experiment, the accuracy of this algorithm is high up to 98.4%. It has the advantages such as high accuracy, short training time, and the generalization of the model is good. It will have broad using prospects in the deterioration test of the insulators, and it also provides a new way to the reliability testing of other electrical devices. With the development of UAV and communication technology, the UAV with high mobility, high control ability, and other characteristics has become an ideal power inspection platform. The emergence of the 5G technology makes it possible to transmit high-quality images in real time. Taking UAV platform as carrier, equipped with UV imager, transmitting UV image in real time through 5G technology, and using non-destructive detection algorithm to detect the fault points will become possible. Therefore, the research in this paper has broad application prospects, and we will explore further in the future.