High voltage outdoor insulator surface condition evaluation using aerial insulator images

dc.contributor.authorPernebayeva, Damira
dc.contributor.authorIrmanova, Aidana
dc.contributor.authorSadykova, Diana
dc.contributor.authorBagheri, Mehdi
dc.contributor.authorJames, Alex
dc.date.accessioned2019-12-18T05:30:05Z
dc.date.available2019-12-18T05:30:05Z
dc.date.issued2019-09
dc.description.abstractHigh 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.en_US
dc.identifier.citationPernebayeva, D., Irmanova, A., Sadykova, D., Bagheri, M., & James, A. (2019). High voltage outdoor insulator surface condition evaluation using aerial insulator images. High Voltage, 4(3), 178-185.en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4462
dc.language.isoenen_US
dc.publisherIETen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.titleHigh voltage outdoor insulator surface condition evaluation using aerial insulator imagesen_US
dc.typeConference Paperen_US
workflow.import.sourcescience

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