Temperature effect on space charge dynamics in XLPE insulation

This paper reports on space charge evolution in crosslinked polyethylene (XLPE) planar samples approximately 1.20 mm thick subjected to electric stress level of 30 kVdc/mm under four temperature 25 oC, 50 oC, 70 oC and 90 oC for 24 hours. Space charge profiles in both as-received and degassed samples were measured using the laser induced pressure pulse (LIPP) technique. The dc threshold stresses at which space charge initiates are greatly affected by testing temperatures. The results suggest that testing temperature has numerous effects on space charge dynamics such as enhancement of ionic dissociation of polar crosslinked by-products, charge injection, charge mobility and electrical conductivity. Space charge distributions of very different nature were seen at lower temperatures when comparing the results of as-received samples with degassed samples. However at higher temperature, the space charge distribution took the same form, although of lower concentration in degassed samples. Space charge distributions are dominated by positive charge when tested at high temperatures regardless of sample treatment and positive charge propagation enhances as testing temperature increases. This can be a major cause of concern as positive charge propagation has been reported to be related to insulation breakdown

The effect of degassing on morphology and space charge

It is believed that space charge buildup in cross-linked polyethylene (XLPE) insulation is the main cause for premature failure of underground power cables. The space charge activities in XLPE depend on many factors such as additives, material treatment, ambient temperature, insulator/electrode interface, etc. Degassing is one of the material treatment process commonly employ in cable manufacturing to improve insulation performance. In this paper, investigation on the effect of degassing period has on the morphology and space charge was carried out. Planar XLPE samples of the same composite were subjected to different degassing time. It is discovered that apart from removing volatile by-products, degassing also anneal XLPE material; changing the morphology as a result

Calibration of the Pulsed Electroacoustic Technique in the Presence of Trapped Charge

The influence of pulse voltage on the accuracy of charge density distribution in the pulsed electroacoustic technique (PEA) is discussed. It is shown that significant error can be introduced if a low dc voltage and high pulse voltage are used to calibrate charge density. However, our main focus in the present paper is to deal with one of the practical situations where space charge exists in the material prior to any measurements. The conventional calibration method can no longer be used to calibrate charge density due to the interference by the charge on the electrode induced by space charge. A method has been proposed which is based on two measurements. Firstly, the sample containing charge is measured without any applied voltage. The second measurement is carried out with a small external applied voltage. The applied voltage should be small enough so there is no disturbance of the existing charge in the sample. The difference of the two measurements can be used for calibration. An additional advantage of the proposed method avoids the influence of the pulse voltage on calibration and therefore gives a more accurate representation of space charge. The proposed method has been validated

Space charge and charge trapping characteristics of cross-linked polyethylene subjected to ac electric stresses

This paper reports on the result of space charge evolution in cross-linked polyethylene (XLPE) planar samples of approximately 220 ?m thick. The space charge measurement technique used in this study is the PEA method. There are two phases to this experiment. In the first phase, the samples were subjected to dc 30 kVdc/mm and ac (sinusoidal) electric stress level of 30 kVpk/mm at frequencies of 1 Hz, 10 Hz and 50 Hz ac. In addition, ac space charge under 30 kVrms/mm and 60 kVpk/mm electric stress at 50 Hz was also investigated. The volts off results showed that the amount of charge trapped in XLPE sample under dc electric stress is significantly bigger than samples under ac stress even when the applied ac stresses are substantially higher. The second phase of the experiment involves studying the dc space charge evolution in samples that were tested under ac stress during the first phase of the experiment. Ac ageing causes positive charge to become more dominant over negative charge. It was also discovered that ac ageing creates deeper traps, particularly for negative charge. This paper also gave a brief overview of the data processing methods used to analyse space charge under ac electric stress

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships