Estimation of the Hydrophobicity of a Composite Insulator Based on an Improved Probabilistic Neural Network

Qiuqin Sun, Fei Lin, Weitao Yan, Feng Wang, She Chen and Lipeng Zhong
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Qiuqin Sun: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Fei Lin: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Weitao Yan: State Grid Hunan Maintenance Company, Changsha 410000, China
Feng Wang: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
She Chen: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Lipeng Zhong: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Energies, 2018, vol. 11, issue 9, 1-20

Abstract: The estimation of hydrophobicity for composite insulators is of great importance for the purpose of predicting the surface degradation. The hydrophobic image is firstly decomposed by the 2-level wavelet, along with the multi-Retinex algorithm in this paper. The processed low frequency sub-band and high frequency sub-band images are then reconstructed. The 3 × 3 Sobel operator is performed to measure the basic spatial gradient in four directions, including the horizontal direction, the diagonal direction, and then the vertical direction. The shape factor, the area ratio of the largest water droplet, and the coverage rate of the water droplet are selected as the feature parameters and input into the classification network that has been trained to do the hydrophobic level recognition. The effect of the different expansion speed on the desired learning results is discussed. The threshold plays a key role in image processing. Considering that the difference between the water droplet edge and the composite insulator surface is relatively small, the asymptotic semi-soft threshold function is used in pretreatment, whereas the adaptive two-dimensional Otsu’s method is used in image segmentation. The experimental results show that the proposed method has high recognition accuracy up to 94.8% for a diversity of images, and it is superior to the improved Shape Factor Method, the Multi-fractal Method, and the RBF Neural Network.

Keywords: composite insulator; hydrophobicity; water-droplet profile; probability neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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