High voltage outdoor insulator surface condition evaluation using aerial insulator images

Abstract

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.

Keywords

• feature extraction
• geophysical image processing
• pattern classification
• decision trees
• learning (artificial intelligence)
• insulators
• insulation
• insulator testing
• high voltage outdoor insulator surface condition evaluation
• aerial insulator images
• drone-based aerial images
• extreme winter conditions
• different surface conditions
• outdoor electrical insulator
• winter condition
• image processing techniques
• state-of-the-art classification methods
• traditional machine learning classifiers
• snowing conditions
• high classification accuracy
• insulator conditions