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- Mahidhar, G. D. P., et al.
(författare)
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Dielectric Properties of Silica based Synthetic Ester Nanofluid
- 2020
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Ingår i: IEEE transactions on dielectrics and electrical insulation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9878 .- 1558-4135. ; 27:5, s. 1508-1515
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Tidskriftsartikel (refereegranskat)abstract
- This work studies the impact of silica nanoparticles on the dielectric properties of synthetic ester oil. A stable nanofluid is prepared by addition of surfactant. With addition of nanoparticles there is an increment in dielectric constant and decrement in dielectric loss constant. An attempt is made to understand the conduction mechanisms in uniform field and non-uniform electric field configuration by means of analysing the mobility of ions. Under uniform electric field stress (<0.1 kV/mm), by means of conduction current measurement with polarity reversal, the ionic mobility, conductivity, ionic concentration and ionic radius are deduced. There is a reduction in ionic mobility by addition of nanoparticle and surfactant. To understand the conduction mechanisms in high electric field condition, the current-voltage characteristics of the nanofluid are measured. Using this data, apparent mobility due to electrohydrodynamic motion is estimated, giving a lower mobility in the case of nanofluid, which could be the cause of the increased corona inception voltage.
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| 2. |
- Mahidhar, G. D. P., et al.
(författare)
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Dielectric Properties of Silica‐Based Synthetic Ester Nanofluid
- 2022
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Ingår i: Alternative Liquid Dielectrics for High Voltage Transformer Insulation Systems: Performance Analysis and Applications. - : John Wiley & Sons. ; , s. 273-303
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Bokkapitel (refereegranskat)abstract
- In recent times, nanoparticles-dispersed ester fluids are reported to exhibit improved thermal and electrical properties for its application in insulation system. In the present study, insulating nanoparticles of silica were dispersed in synthetic ester fluid to improve electrical characteristics of the base fluid. A two-step process involving mechanical shear mixing and ultrasonication was adopted for producing nanofluids. Physical and chemical characterization of nanofluids were carried out to estimate stability of the nanofluid with optimum concentration of nanoparticles and surfactants. To substantiate the influence of nanoparticles and surfactant on the dielectric properties of the nanofluid, conductivity measurements were made at low and high electric field conditions. It was observed that in the presence of electric field, the motion of ions is inhibited due to its trapping by the surface of nanoparticle, leading to lower ionic mobility. To explore the governing mechanisms further, electrical insulating performance of the nanofluid and corona discharge activity were investigated under AC and DC voltages using ultrahigh frequency (UHF) technique. This study has also shown an increase of 30% improvement in the corona inception voltage (CIV) upon addition of nanoparticles. The discharge activity was also mitigated due to incorporation of nanofillers in the fluid. The energy of discharge was found to be lower in nanofluid as compared to base fluid. It was evident from the present studies that silica-based nanofluids have shown superior dielectric performance compared to the base synthetic ester fluid.
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| 3. |
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| 4. |
- Mahidhar, G. D. P., et al.
(författare)
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Understanding of Incipient discharges in Transformer Insulation by reconstruction of Digital Twins for the discharges using Generative Adversarial Networks
- 2021
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Ingår i: 2021 Ieee Electrical Insulation Conference (Eic). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 631-634
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Konferensbidrag (refereegranskat)abstract
- Partial discharge (PD) monitoring is one of the diagnostic technique adopted for identifying the variety of defects in transformer insulation. Ultra high frequency (UHF) technique is gaining importance in PD monitoring applications of transformer due to various advantages. Different type of incipient discharges arose from defects in transformer insulation that needs to be identified. In an actual test site there can be noises that can hinder data acquisition and defect identification can become difficult. By using artificially reconstructed signals of known practically occurring defect models, the loss in data can be overcome. In the present study, Deep Convolutional Generative Adversarial Networks (DCGAN) technique is adopted to reconstruct the UHF partial discharge signals with high fidelity. Time-Frequency characteristics of the signals were used to build the DCGAN network and the reconstructed UHF signals are evaluated by studying the frequency characteristics of the generated signal.
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