基于VGGNet算法的绝缘子无损检测  

Insulator nondestructive testing based on VGGNet algorithm

作　　者：马立新 豆晨飞 宋晨灿 杨天笑 Ma Lixin;Dou Chenfei;Song Chencan;Yang Tianxiao(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学机械工程学院,上海200093

出　　处：《光电工程》2021年第1期35-42,共8页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(61205076)。

摘　　要：针对电力系统中存在的难以检测运营中的绝缘子劣化问题,本文在深入分析卷积神经网络模型的原理和结构的基础上,运用卷积神经网络算法对绝缘子劣化程度进行评估。通过绝缘子工频闪络试验使其产生无放电、弱放电、强放电三种状态,并使用紫外成像仪采集不同放电状态下的绝缘子紫外图像构建紫外图像样本库。利用VGGNet框架神经网络算法对样本进行分类训练和状态预测评估,最终达到判断绝缘子是否劣化的目的。由实验结果可知,该算法准确率高达98.4%,在绝缘子劣化检测上有宽广的应用前景,并为其他电力设备的可靠性检测提供了思路。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.

关 键 词：卷积神经网络 紫外放电 无损检测 绝缘子 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TB866[自动化与计算机技术—计算机科学与技术]