Ayman H. El-Hag

Fundamental and low frequency harmonic components of leakage current as a diagnostic tool to study aging of RTV and HTV silicone rubber in salt-fog

The paper presents the results of using the fundamental and the low frequency harmonic components of leakage current to study aging of silicone rubber in salt-fog. Experiments have been conducted on RTV and HTV coated rods at different fields (0.25-0.6 kV/cm) and conductivities (1000 to 2500 /spl mu/S/cm). The onset of dry-band arcing on samples could be determined by measuring the low frequency harmonic components. A correlation has been found between the fundamental and low harmonic components of leakage current and different forms of aging. Where erosion could be associated with an increase in the level of both the fundamental and low frequency harmonic components of leakage current. For example, surface damage for HTV rods occurred when the fundamental component of leakage current was greater than 2 mA. On the other hand, when the samples approached failure, the fundamental component of leakage current reached relatively high values ( > 6 mA for HTV rods and > 2 mA for RTV rods) and the low frequency harmonic components of the leakage current tended to decrease. The results suggest that both the fundamental and low frequency harmonics of leakage current can be used as a tool to determine both the beginning of aging and end of life of silicone rubber in salt-fog.

Erosion resistance of nano-filled silicone rubber

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.

On-line detection and measurement of partial discharge signals in a noisy environment

In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity. One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi- resolution analysis (WMRA). However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks. This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels. The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaisers window. Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal. A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction. The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise.

Inactivation of Naturally Grown Microorganisms in Orange Juice Using Pulsed Electric Fields

In this paper, the effect of pulsed electric fields (PEFs) on naturally contaminated orange juice was investigated. A 2-log reduction in a colony count for the naturally grown microorganisms was achieved after application of 120 pulses/mL at a PEF level of 46 kV/cm. Also, the effect of the PEF on microorganisms inoculated in orange juice was studied. Compared to the naturally grown microorganisms, about 1-log more reduction was observed with inoculated microorganisms that were subjected to the same PEF conditions. The results suggest that other factors like the increased temperature and antimicrobial additives might be required to enhance the killing efficiency. A 20 degC increase in temperature prior to the application of the PEF resulted in about 1-log more reduction in microorganisms

De-noising of partial discharge signal using eigen-decomposition technique

The Rogowski coil method for the measurements and detection of PD signals represents a cost effective and practical way to measure PD signals in electrical power apparatus; however it is subject to excessive noise pick-up because of its inductive nature. This paper discusses the implementation of a de-noising algorithm using eigen-decomposition approach, which can be utilized in order to minimize the extraneous noise encountered with on-line tests in the field. The proposed algorithm possesses the inherent advantage of decomposing the signal space and separates it from the noise subspaces. Results discussed in the paper demonstrate the performance of the proposed technique for both simulated and experimental PD signals.

Classification of common partial discharge types in oil-paper insulation system using acoustic signals

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.

A cascade of artificial neural networks to predict transformers oil parameters

In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.

Influence of shed parameters on the aging performance of silicone rubber insulators in salt-fog

The paper presents the results of a study on the effect of shed design on the aging performance of silicone rubber (SIR) insulators in salt-fog. The experiments on a single shed design at 35 V/mm average stress and 0.25 S/m salt-fog conductivity level are reported. Shed diameter, location and inclination angle are the parameters investigated in the study. The low frequency harmonics of the leakage current, early aging period, equivalent salt deposit density (ESDD), and physical damage are used to evaluate the aging performance of various designs. Insulators having sheds with 100 mm diameter show a better performance than those having 80, 60 and 40 mm diameter sheds. The inclination angle of the sheds is shown to have a significant effect in reducing the leakage current to a very low value but simulation results using FEMLAB/sup /spl reg// show that a steep inclination angle results in field enhancement beneath the shed that may lead to corona damage of the shed. A cup shaped design for the shed is shown to have a low leakage current level and a low field enhancement beneath the shed.

Artificial neural networks with stepwise regression for predicting transformer oil furan content

In this paper a prediction model is proposed for estimation of furan content in transformer oil using oil quality parameters and dissolved gases as inputs. Multi-layer perceptron feed forward neural networks were used to model the relationships between various transformer oil parameters and furan content. Seven transformer oil parameters, which are breakdown voltage, water content, acidity, total combustible hydrocarbon gases and hydrogen, total combustible gases, carbon monoxide and carbon dioxide concentrations, are proposed to be predictors of furan content in transformer oil. The predictors were chosen based on the physical nature of oil/paper insulation degradation under transformer operating conditions. Moreover, stepwise regression was used to further tune the prediction model by selecting the most significant predictors. The proposed model has been tested on in-service power transformers and prediction accuracy of 90% for furan content in transformer oil has been achieved.

Evaluation of ZigBee Wireless Sensor Networks Under High Power Disturbances

The goal of the research presented in this paper is to investigate the reliability of ZigBee-based wireless sensor networks in transforming existing power systems into future smart grids. The performance of the communication network for a specific propagation environment, channel modulation, and frequency band is investigated. The high-power interrupting disturbances generated from harsh normal and abnormal operating conditions in a power system environment are investigated. Laboratory-simulated switching transient events have been generated under different conditions. The interruption limits due to the radio-frequency signals generated during high-power switching transients are defined for ZigBee coordinator and device units.