High voltage outdoor insulator surface condition evaluation using aerial insulator images

作     者：Damira Pernebayeva Aidana Irmanova Diana Sadykova Mehdi Bagheri Alex James 

作者机构：Department of Electrical and Computer EngineeringSchool of Engineering and Digital Sciences Nazarbayev UniversityAstanaKazakhstan 

出 版 物：《High Voltage》 (高电压（英文）)

年 卷 期：2019年第4卷第3期

页      面：178-185页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 

主　　题：insulator surface condition 

摘      要：High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains.The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods.Two different types of classification approaches are compared:one method is based on neural networks(e.g.CNN,InceptionV3,MobileNet,VGG16,and ResNet50)and the other method is based on traditional machine learning classifiers(e.g.Bayes Net,Decision Tree,Lazy,Rules,and Meta classifiers).They are evaluated to discriminate the images of insulator surface exposed to freezing,wet,and snowing conditions.The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy.The classification of the insulator surfaces will assist in determining the insulator conditions,and take preventive measures for its protection.