Accurate Sensing of Insulator Surface Contamination Using Customized Convolutional Neural Network

Abstract

Accurate sensing of contamination on the insulator surface is vital for the reliable operation of transmission lines. Hence, the present work aims to develop a deep learning framework for remote and accurate contamination sensing on the surface of outdoor insulators. The experiment is conducted on an 11-kV porcelain disc insulator to generate an extensive database of images representing different insulator surface conditions, i.e., clean surface and surface with sand, mud, and ash contaminations. The captured images were fed to a customized convolutional neural network (CNN) architecture for automated feature extraction and recognition. The proposed CNN model delivers appreciably high recognition accuracy at significantly reduced training time for sensing various insulator contaminations compared with other benchmark CNN models (AlexNet, VGGNet16, and ResNet50). Moreover, the proposed framework delivers an excellent recognition performance in sensing surface contamination under different lighting conditions. Therefore, the proposed methodology can be implemented for the condition monitoring of real-life insulators.