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.
Highlights
Taking advantage of light weight, shatter-proof performance, the hydrophobic surface, and greater flashover performance under wet and polluted conditions, composite insulators are increasingly popular in the electrical power industry
The probabilistic neural networks (PNNs) [14], as a special Radial Basis Function (RBF), are a feed forward neural network in essence, and the basic idea lies in choosing an optimal decision with the least expectation risk based on the Bayesian minimum risk criterion in the multi-dimensional input space, and uses the linear learning algorithm to achieve the same effect as the nonlinear algorithm
Theit input summation layer can besimple determined according the actual situation before Since learning, is only layer and the summation layer can be the determined according to the situation beforeoflearning, necessary to determine number of learning samples so asactual to determine the structure the PNN. it is only necessary to determine the number of learning samples so as to determine the structure of the PNN
Summary
Taking advantage of light weight, shatter-proof performance, the hydrophobic surface, and greater flashover performance under wet and polluted conditions, composite insulators are increasingly popular in the electrical power industry. The basic principle lies in determining the hydrophobic grade based on the receding angle θ r of the water droplet on the shed surface This method is very convenient, and it has little requirement for testing instruments. The probabilistic neural networks (PNNs) [14], as a special RBF, are a feed forward neural network in essence, and the basic idea lies in choosing an optimal decision with the least expectation risk based on the Bayesian minimum risk criterion in the multi-dimensional input space, and uses the linear learning algorithm to achieve the same effect as the nonlinear algorithm This algorithm is very suitable for pattern recognition and has significant advantages in image classification. The results show that the method has high accuracy for a diversity of images and it can meet the requirements for practical application
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