Condition Monitoring of Polymeric Insulators: A Remote Diagnostic Framework With Improved Intermediate Hydrophobicity Grade Detection

Polymeric insulators deliver superior pollution performance owing to their excellent hydrophobic characteristics. However, due to aging, temporary loss of hydrophobicity occurs and continuous water films start to form, leading to dry-band arcing and subsequently, flashover of the insulators. Hence, accurate identification of different hydrophobicity grades (HGs) is essential. Considering this issue, a novel image visibility theory-aided remote HG detection framework with improved intermediate HG identification has been proposed in this contribution. To achieve this, image visibility patches ($VP$s) were extracted to quantify the intricate pixel-level alterations taking place particularly for the intermediate HGs. In addition, the concept of uniform visibility patches is proposed to reduce computational burden of the conventional $VP$ extraction process along with providing additional noise resiliency. The employed HG detection methodology infused with a transformer encoder-based classifier module was trained on experimental HG images acquired from an 11kV silicone rubber (SiR) insulator and tested on benchmark data utilizing a cross-dataset learning strategy, which yielded satisfactorily high HG detection performance, Ablation study in addition to comparison with existing studies revealed superior efficiency of the proposed framework with highly impressive reliability and robustness in identifying different HGs, suggesting it's potential application for remote condition monitoring of outdoor polymeric insulators.