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The aim of this article is to present a review of the development and application of superhydrophobic insulation for outdoor high-voltage applications. The methods of preparing insulation exhibiting superhydrophobic properties to mitigate the pollution problem under high voltages in the outdoors are explored. In addition, the existing evaluation techniques at the material development stage are reviewed and potential new modifications are discussed. The self-cleaning and anti-icing properties, the electrical performance, and prospects of superhydrophobic insulators are also addressed. Although superhydrophobic insulators may show promising behavior in small-scale laboratory conditions that do not use electrical stress, the effect of surface micro–nano structure responsible for the superhydrophobicity on the performance under the actual outdoor and weathering conditions under voltage remains to be fully understood and verified. Therefore, there is a need to verify the feasibility of superhydrophobic insulators for outdoor high-voltage applications with a solid evaluation protocol, while using material screening methods in the laboratory, and with reference to field experience. [ABSTRACT FROM AUTHOR]

In high-voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise-free PD signals. It is found that Cepstrum-ANN yields the highest classification accuracy for noisy PD signals than the other methods tested. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination. [ABSTRACT FROM AUTHOR]

It is well known that a correlation exist between the pattern of partial discharge (PD) behavior and the insulation quality. Since different sources of partial discharge have their own unique effects on the degradation of insulation material, it is vital to investigate the relationship between the defect type and the PD to determine the insulation quality. Numerous work had been done to classify partial discharge patterns with variable success. Past research work in partial discharge classification varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment. Therefore it is necessary for a literature survey to access the state of the art development in partial discharge classification. [ABSTRACT FROM AUTHOR]

This study uses natural fibre, which is moisture absorbent, as an innovative and economical filler for insulating oil. Rice husk (RH) is a natural fibre known to have water-absorbing properties and used as a supplementary cementitious material. This research utilises the water-absorbing properties of RH to improve the physicochemical and dielectric properties of insulating oil. RH was refined into a fine powder at a diameter of less than 63 µm. Palm oil (PO) was synthesised with RH at concentrations of 0 g/L, 0.01 g/L, and 0.1 g/L. The moisture-absorbent properties of RH were analysed by using Fourier-transform infrared (FTIR) spectroscopy. The particle size and distribution of RH in PO were also obtained using a scanning electron microscope (SEM) and a Zeta particle analyser. The breakdown voltage (BDV) strength of PO with RH was measured according to IEC 60156, and the dielectric frequency response was investigated in the range of 102–105 Hz. RH is proven to absorb moisture from PO, as the O–H band intensity at the 3350 cm−1 is increased from the experimental results. Therefore, the mean BDV and dielectric insulation properties of PO dispersed with RH also increased accordingly. [ABSTRACT FROM AUTHOR]

Modeling of the partial discharge (PD) process allows a better understanding of the phenomena. In this paper, a simulation model for spherical cavities within a homogeneous dielectric material has been developed. The model is implemented using Finite Element Analysis (FEA) software in parallel with a mathematical package. This method provides many advantages over previous PD models because discharge events can be simulated dynamically and the electric field in the cavity can be calculated numerically. The model has been used to study the effect of different amplitudes and frequencies of the applied voltage and simulation results have been compared with experimental measurement results. It is found that certain model parameters are dependent on the applied stress and parameters that clearly affect PD activity can be readily identified, these parameters include; the electron detrapping time constant, the cavity surface conductivity, the initial electron generation rate and the extinction voltage. The influence of surface charge decay through conduction along the cavity wall on PD activity has also been studied. [ABSTRACT FROM AUTHOR]