Insulator nondestructive testing based on VGGNet algorithm

Ma Lixin; Dou Chenfei; Song Chencan; Yang Tianxiao

doi:10.12086/oee.2021.200072

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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

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References

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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.