A Review of Hydrophobicity Measurement and Classification Methods for High-Voltage Insulators

Hydrophobicity is a critical property influencing the performance and reliability of high-voltage insulators, particularly nonceramic types, by mitigating surface contamination and reducing the risk of flashover. This paper presents a comprehensive review of hydrophobicity assessment techniques, spanning traditional laboratory-based techniques, alternative experimental approaches, and modern artificial intelligence (AI)–assisted systems. Standard methods such as the contact angle, surface tension, and spray techniques are evaluated for their accuracy and limitations in field deployment. Alternative methods, including salt fog and dynamic drop tests, offer enhanced insights but remain constrained by laboratory requirements. The review emphasizes the rapid evolution of AI-driven techniques, ranging from image processing and pattern recognition to deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs), which enable scalable, automated, and objective surface condition categorization. Despite their promise, these methods face challenges related to image quality, environmental variability, and computational demands. To address these, the paper proposes a structured framework for developing a robust machine learning model tailored for hydrophobicity classification. The framework outlines key stages including data acquisition, model architecture, training strategies, evaluation metrics, and deployment considerations, with a focus on real-time, field-ready applications. This review not only synthesizes the current state of the art but also provides a roadmap for future research and standardization efforts aimed at integrating AI-based solutions into practical insulator condition monitoring.