Hybrid Swin Transformer Approach for Hydrophobicity Monitoring of Outdoor Polymeric Insulators in Transmission Lines

Hydrophobicity is one of the vital properties of outdoor polymeric insulators which prevents the water accumulation on the insulator’s surface. Due to extreme environmental conditions, polymeric insulators tend to lose its hydrophobicity property. Accurate hydrophobicity classification is essential to understand the condition of the insulators in operation within the power system network. In this study, a hybrid swin transformer (HST) is adopted to enhance the hydrophobicity classification accuracy. Traditional image classification methods struggle with variation in droplet shapes, sizes, and surface patterns which can complicate the classification process, however, the model adopted in this study integrates hybrid shifted windows with advanced vision transformer techniques to capture both short-range and long-range dependencies in images, providing a more robust understanding of complex visual patterns associated with different hydrophobicity levels. This Hybrid approach is evaluated with laboratory generated dataset (according to International Electrotechnical Commission (IEC) Standard 62073) and an online available dataset. Extensive experimental and analytical results demonstrate that the hybrid model outperforms existing state-of-the-art techniques. Additionally, an Android application for image classification is developed with a simple graphical user interface (GUI) to enhance the maintenance of the insulators.