Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

Abstract

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

Evaluation of aging and degradation for silicone rubber composite insulator based on machine learning

“…After the hardening of composite insulators, the mechanical properties of the sheds are seriously affected, accompanied by more serious performance deterioration, including the decline or even loss of hydrophobicity. The hydrophobic characteristic of SIR composite insulators is a key factor to ensure safe and stable operation [17,18], which is mainly divided into seven levels, HC1-HC7 from strong to weak [19]. In general, the water contact angle (WCA) of HC1 ranges from 80 • to 120 • , and composite insulators maintain strong hydrophobicity.…”