The high voltage insulator requires continuous monitoring and inspection to prevent failures and emergencies. Manual inspections are costly as it requires covering a large geographical area where insulators are often subjected to harsh weather conditions. Automatic detection of insulators from aerial images is the first step towards performing real-time classification of insulator conditions using Unmanned Aerial Vehicle (UAV). In this paper, we provide a cost-effective solution for detecting insulators under the conditions of an uncluttered background, varied object resolution and illumination conditions using You Only Look Once (YOLO) deep learning neural network model from aerial images. We apply data augmentation to avoid overfitting with a training set size of 56000 image samples. It is demonstrated experimentally that this method can accurately locate insulator on UAV based real-time image data. The detected insulator images are then successfully subjected to insulator surface condition assessment for the presence of ice, snow and water using different classifiers.

A novel Fusion Convolutional Network (FCN) is proposed in this research for potential real-time monitoring of insulators using unmanned aerial vehicle (UAV) edge devices. Precise airborne imaging of outdoor objects, such as high voltage insulators, suffers from varied object resolution, cluttered backgrounds, unclear or contaminated surfaces, and illumination conditions. Accurate information about the insulator surface condition is essential and is of a high priority since insulator breakdown is a leading cause of electrical failure. A multi-modal information fusion (MMIF) system is developed during this research to analyze and classify possible contaminations present on the electrical insulators. A novel system, referred to as FCN, consists of a Convolutional Neural Network (CNN) and a binary Multilayer Neural Network (MNN) sub-classifier. While constructing the MMIF dataset for training and testing the novel FCN, the image classification output of the CNN is combined with the leakage current values (LCV) obtained as the classification output of MNN. Each sample of the MMIF dataset is, therefore, represented as a series of fusions. Later, sub-classifiers, of the FCN, are trained to identify the contamination types in the fusion series by implementing a voting system of sub-classifiers which is trained to identify a given class. As a result of the implementation of the proposed FCN, the classification accuracy increased by 8.4%, i.e., from 92% to 99.76%. To compare and benchmark the performance of proposed FCN, conventional classification algorithms are also implemented on the fusion of features that are extracted employing the wavelet transform and PCA methods. State-of-the-art CNN architectures are also discussed on account of their time consumption and memory usage. The conceptualization of a potential hardware implementation of the proposed FCN, on emerging edge devices, is also provided for completeness of the discussion. Pertinent outcomes of this research can be further extended to other potential applications of airborne imaging.

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.

High voltage insulators are widely deployed in power systems to isolate the live- and dead-part of overhead lines as well as to support the power line conductors mechanically. Permanent, secure and safe operation of power transmission lines require that the high voltage insulators are inspected and monitor, regularly. Severe environment conditions will influence insulator surface and change creepage distance. Consequently, power utilities and transmission companies face significant problem in operation due to insulator damage or contamination. In this study, a new technique is developed for real-time inspection of insulator and estimating the snow, ice and water over the insulator surface which can be a potential risk of operation breakdown. To examine the proposed system, practical experiment is conducted using ceramic insulator for capturing the images with snow, ice and wet surface conditions. Gabor and Standard deviation filters are utilized for image feature extraction. The best achieved recognition accuracy rate was 87% using statistical approach the Standard deviation.