Temperature gradient dependence on electrical tree in epoxy resin with harmonic superimposed DC voltage

2020 ◽  
Vol 27 (1) ◽  
pp. 270-278 ◽  
Author(s):  
B. X. Du ◽  
Meng Tian ◽  
J. G. Su ◽  
T. Han
2018 ◽  
Vol 25 (6) ◽  
pp. 2183-2190 ◽  
Author(s):  
Ibrahim Iddrissu ◽  
Simon M Rowland ◽  
Hualong Zheng ◽  
Zepeng Lv ◽  
Roger Schurch

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47273-47281 ◽  
Author(s):  
Boxue Du ◽  
Meng Tian ◽  
Jingang Su ◽  
Tao Han

2009 ◽  
Vol 129 (12) ◽  
pp. 915-921 ◽  
Author(s):  
Hideki Ueno ◽  
Takashi Nagamachi ◽  
Masaki Nakamura ◽  
Hiroshi Nakayama ◽  
Kunihiko Kakihana

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 69522-69531
Author(s):  
Yongqiang Wang ◽  
Changhui Feng ◽  
Yu Luo

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2562
Author(s):  
Abdullahi Abubakar Mas’ud ◽  
Arunachalam Sundaram ◽  
Jorge Alfredo Ardila-Rey ◽  
Roger Schurch ◽  
Firdaus Muhammad-Sukki ◽  
...  

In high-voltage (HV) insulation, electrical trees are an important degradation phenomenon strongly linked to partial discharge (PD) activity. Their initiation and development have attracted the attention of the research community and better understanding and characterization of the phenomenon are needed. They are very damaging and develop through the insulation material forming a discharge conduction path. Therefore, it is important to adequately measure and characterize tree growth before it can lead to complete failure of the system. In this paper, the Gaussian mixture model (GMM) has been applied to cluster and classify the different growth stages of electrical trees in epoxy resin insulation. First, tree growth experiments were conducted, and PD data captured from the initial to breakdown stage of the tree growth in epoxy resin insulation. Second, the GMM was applied to categorize the different electrical tree stages into clusters. The results show that PD dynamics vary with different stress voltages and tree growth stages. The electrical tree patterns with shorter breakdown times had identical clusters throughout the degradation stages. The breakdown time can be a key factor in determining the degradation levels of PD patterns emanating from trees in epoxy resin. This is important in order to determine the severity of electrical treeing degradation, and, therefore, to perform efficient asset management. The novelty of the work presented in this paper is that for the first time the GMM has been applied for electrical tree growth classification and the optimal values for the hyperparameters, i.e., the number of clusters and the appropriate covariance structure, have been determined for the different electrical tree clusters.


Author(s):  
Zhengwei Ge ◽  
Chun Yang

Microfluidic concentration of sample species is achieved using the temperature gradient focusing (TGF) in a microchannel with a step change in the cross-section under a pure direct current (DC) field or a combined alternating current (AC) and DC electric field. Experiments were carried out to study the effects of applied voltage, buffer concentration and channel size on sample concentration in the TGF processes. These effects were analyzed and summarized using a dimensionless Joule number that is introduced in this study. In addition, Joule number effect in the Poly-dimethylsiloxane (PDMS)/PDMS microdevice was compared with the PDMS/Glass microdevice. A more than 450-fold concentration enhancement was obtained within 75 seconds in the PDMS/PDMS microdevice. Results also showed that the high frequency AC electric field which contributes to produce the temperature gradient and reduces the required DC voltage for the sample concentration. The lower DC voltage has generated slower electroosmotic flow (EOF), which reduces the backpressure effect associated with the finite reservoir size. Finally, a more than 2500-fold concentration enhancement was obtained within 14 minutes in the PDMS/PDMS microdevice, which was a great achievement in this TGF technique using inherent Joule heating effects.


Author(s):  
Yimeng Li ◽  
Boxue Du ◽  
Jin Li ◽  
Zhonglei Li ◽  
Tao Han ◽  
...  

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