Aoyun Zhuang

Glaze icing on superhydrophobic coating prepared by nanoparticles filling combined with etching method for insulators

Icing on insulators may cause flashover or even blackout accidents in the power transmission system. However, there are few anti-icing techniques for insulators which consume energy or manpower. Considering the water repelling property, the superhydrophobic surface is introduced for anti-icing of insulators. Among the icing forms, the glaze icing owns the highest density, strongest adhesion, and greatest risk to the power transmission system but lacks researches on superhydrophobic surface. In this paper, superhydrophobic surfaces with contact angle of 166.4°, contact angle hysteresis of 0.9°, and sliding angle of less than 1° are prepared by nanoparticle filling combined with etching method. The coated glass slide and glass insulator showed excellent anti-icing performance in the glaze icing test at -5°C. The superhydrophobicity and anti-icing property of the coatings benefit from the low surface energy and hierarchical rough structure containing micron scale pits and nanoscale coralloid bulges supported by scanning electron microscopy (SEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS) characterization.

Adaptive Optimal Kernel Time–Frequency Representation Technique for Partial Discharge Ultra-high-frequency Signals Classification

Abstract An intelligent system for automatic partial discharge pattern recognition is proposed using adaptive optimal kernel time-frequency representation and a fuzzy k-nearest neighbor classifier. The adaptive optimal kernel technique is employed to acquire the joint time-frequency information for partial discharge signals, which is characterized by the adaptive optimal kernel amplitude matrix. A new feature extraction algorithm, i.e., non-negative matrix factorization aided principal component analysis, is proposed to solve the difficulties of principal component analysis for feature extraction of partial discharge adaptive optimal kernel amplitude matrices due to the high dimensionality. Using an ultra-high frequency detector, 600 partial discharge signals sampled from 4 categories of typical artificial defect models in the laboratory are employed for testing. It is shown that the maximum classification accuracy of 94.33% is obtained considering different non-negative matrix factorization parameter r and various non-negative matrix factorization iterations T. Also, the classification performance of the non-negative matrix factorization–principal component analysis features is superior to that of principal component analysis features extracted from original partial discharge signals, two-dimensional non-negative matrix factorization features and phase-resolved partial discharge statistical operators. The proposed technique can be used for partial discharge pattern recognition based on ultra-high-frequency detection arrangements.

Preparation of a superhydrophobic surface by RF magnetron sputtering and its anti-icing performance

In this study, a nano-structured surface was prepared on glass slides by RF magnetron sputtering with zinc target. The superhydrophobicity of the surface was highly improved after thermal oxidation and decorated by hexadecyltrimethoxysilane. Field emission scanning electron microscopy (FESEM) and energy dispersive spectrometer (EDS) were utilized to evaluate the surface morphology, element types and levels of the samples. A portable low-temperature test chamber was used to investigate the anti-icing performance of the superhydrophobic surface and record the collision process of a cooling water drop to the sample surface by high-speed camera. The results showed that the frozen temperature of water droplet on the superhydrophobic surface was much lower than that of droplet on the bare glass. This study offers insight into understanding the anti-icing behavior of the superhydrophobic surface and may favor the application of superhydrophobic surfaces in power transmission system against ice accumulation.