Classification of polymers insulators hydrophobicity basead on digital image processing

Abstract

Although the hydrophobicity is usually an arduous parameter to be determined in the field, it has been pointed out as a good option to monitor aging of polymeric outdoor insulators. Concerning this purpose, digital image processing of photos taken from wet insulators has been the main technique nowadays. However, important challenges on this technique still remain to be overcome, such as; images from non-controlled illumination conditions can interfere on analyses and no existence of standard surfaces with different levels of hydrophobicity. In this paper, the photo image samples were digitally filtered to reduce the illumination influence, and hydrophobic surface samples were prepared from wetting silicon surfaces with solution of water-alcohol. Furthermore norevious studies triying to quantify and relate these properties in a mathematical function were found, that could be used in the field by the electrical companies. Based on such considerations, high quality images of countless hydrophobic surfaces were obtained and three different image processing methodologies, the fractal dimension and two Haralick textures descriptors, entropy and homogeneity, associated with several digital filters, were compared. The entropy parameter Haralick's descriptors filtered with the White Top-Hat filter presented the best result to classify the hydrophobicity.

Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods

“…Image processing is an effective method in classifying hydrophobic levels of insulators, and there are many image processing methods based on spraying images are proposed, such as WTH + EQU 16 (White Top-Hat + Histogram Equalization), image segmentation with multi-threshold, 18 etc. WTH+EQU is proposed by Thomazini et al, and they combine white top-hat, histogram equalization and sobel operator to obtain edges of droplets.…”

“…F o r o b t a i n i n g s a m p l e s w i t h v a r i o u s H C , Thomazini et al 16,19,20 artificially change the hydrophobicity of specimen with spraying WIA (Water and Isopropyl Alcohol) solution at different concentrations (from 0 to 100%). Although this method can make different levels of hydrophobicity with only one or two specimens, the images obtained are extremely standard which can't present the actual situations of the insulator's surface.…”

“…Therefore, some objective measuring methods based on image processing and feature extraction are proposed, 5,[9][10][11][12][13][14][15][16][17][18][19][20][21][22] such as fractal dimension, circular factor, goniometric measurement using Hough transformation, scaled entropy and histogram analysis, surface energy, and online hydrophobicity measurement methodology. However, only one or two characteristic parameters are adopted for classification in these methods which can't describe images comprehensively.…”

Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

“…The commonly‐used image features include the shape factor [14], the coverage rate of water and that of the maximum droplet [12], and so on. However, it is difficult to effectively extract these features from spray images with various ambient light, shooting angle and surface conditions [15].…”

Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

“…Digital image processing technique has been a promising tool to analyse the water dispersion pattern and to provide reliable identification of insulator's hydrophobicity [12]. Water is sprayed over the insulator surface, and images are acquired using the digital camera.…”