LAUSR

Hydrophobicity is an important property of polymeric insulators that allow formation of discrete water droplets on the insulator surface. However, with aging or under extreme humidification, the insulator surface loses its hydrophobic property, resulting in continuous water channel formation, which may lead to dry band arcing and subsequently flashover, thereby affecting the long-term performance of the insulator. Therefore, accurate recognition of hydrophobic class (HC) is important as it provides information regarding the surface condition of the insulator. Considering the above-said facts, this article investigates the feasibility of employing residual morphological neural network (Res-Morph-NN) to accurately recognize different HCs of polymeric insulators. In addition, for better understanding of the distribution of water droplets on the insulator surface, a novel approach based on multiscale mathematical morphology, i.e., granulometry, is used in this article. Our investigations revealed significant alterations in the granulometric patterns with the change in HC of polymeric insulators. The efficacy of the proposed framework employing Res-Morph-NN has been validated on HC images obtained from the experiment as well as on large set of images obtained from an online database, which returned satisfactory results. The proposed method can be efficiently used to discriminate different hydrophobicity classes, which can be implemented for monitoring insulator surface condition in real time.