The paper presents the experimental results obtained on the erosion resistance of silicone rubber (SIR) filled with 12 nm size fumed silica (nano filler) to those filled with 5 /spl mu/m size silica filler (micro filler). The ASTM 2303 inclined plane tracking and erosion test was used in the comparison as well as an infrared laser as the source of heat to erode the SIR samples. The erosion resistance of the SIR materials increased with increasing percentage of the fillers, and it was observed that 10% by weight of nano-filled SIR gives a performance that is similar to that obtained with 50% by weight of micro-filled SIR. The low frequency components of leakage current and the eroded mass are used to evaluate the relative erosion resistance of the composites and the third harmonic component of the leakage current shows good correlation to the measured eroded mass. The paper discusses the possible reasons for the improvement in the erosion resistance of nano-filled silicone composites.

Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem in PD signal detection and classification. The challenge lies in removing these noises from the online PD signal measurements effectively, while retaining its discriminant features and characteristics. In this paper, wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed. The proposed threshold estimation technique obtains two different threshold values for each wavelet sub-band and uses a prodigious thresholding function that conserves the original signal energy. Moreover, two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise. The proposed technique is applied on different acoustic and current measured PD signals to examine its performance under different noisy environments. The simulation results confirm the merits of the proposed denoising technique compared with other existing wavelet-based techniques by measuring four evaluation metrics: 1) SNR; 2) cross-correlation coefficient; 3) mean square error; and 4) reduction in noise level.

Defects in ceramic insulators like broken, cracked and punctured discs give rise to the initiation of partial discharge (PD) activities within the samples which has a detrimental effect on the insulator life. Hence it is important for the utilities to identify such defective samples as early as possible so that appropriate replacement strategies can be devised. The work presented in this paper involves the investigation of a number of cases of insulator defects, with the goal of developing an online RF-based PD technique for monitoring ceramic disc insulators. The three classes examined are a cracked ceramic insulator disc; a disc with a hole through the cap, and a completely broken insulator disc. The defective discs are considered individually and are also incorporated into strings of 2, 3, and 4 insulators. The captured RF pulses are processed by extracting wavelet packet based features. Feature reduction and selection is carried out and classification results are obtained. To classify the discharges arising from different types of defects, an artificial neural network (ANN) algorithm is applied to the extracted features, and recognition rates of more than 95% were reported for each class. The results of preliminary field tests carried out on a 40 feet high test transmission tower are also reported and their analysis showed good discrimination between the different defect types.

This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features by extracting the most influential ones when determining the transformers health index. Several filter and wrapper based feature selection methods are investigated. The effectiveness of the selected features is validated through performance evaluations of various classification models. The experimental results demonstrate that water content, acidity, breakdown voltage and FFA (Furan), are the most influential testing parameters in determining the health index

This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved.

One of the major attribute of the smart grid is to integrate renewable and storage energy resources at the consumption premises. This paper presents the design, implementation and testing of an embedded system that integrates solar and storage energy resources to a smart home. The proposed system provides and manages a smart home energy requirement by installing renewable energy; and scheduling and arranging the power flow during peak and off-peak period. In addition to that, a two-ways communication protocol is developed to enable the home owner and the utility provider to better optimize the energy flow and the consumption efficiency. A prototype for the proposed system was designed, implemented and tested using a controlled load bank to simulate a scaled random real house consumption behavior. Three different scenarios were tested and the results and findings are reported