Investigation on thermally aged natural ester oil for real-time monitoring and analysis of transformer insulation

Thermal aging of natural ester oil shows drastic reduction in partial discharge inception voltage(PDIV)and a significant variation is observed only above a certain aging time,under AC,DC,high frequency AC voltages and with harmonic voltages with different total harmonic distortion.Weibull distribution studies on PDIV measurements indicate a reduction in scale parameter(α)with increase in thermal aging temperature.A characteristic reduction in breakdown voltage was observed with the thermally aged ester oil,under AC,DC and standard lightning impulse voltage.The breakdown voltage variation with aged ester oil follows normal distribution.Ultraviolet(UV)analysis of ester oil thermally aged at 160°C has revealed a regular shift of the derived absorbance parameter to longer wavelengths.The interfacial tension and turbidity exhibits an inverse relationship with the thermally aged ester oil.Gas chromatography/mass spectrometric analysis of the thermally aged ester oil predicted the formation of more carboxylic acids and ketones with aging duration.The steady-state fluorescence on thermally aged ester oil exhibits a shift in its emission profile,which is in tandem with the UV absorption spectroscopic analysis.Fluorescence analysis can be adopted as a real-time monitoring tool in transformers,to understand the condition of liquid insulation.The viscosity dependence on the wavelength of derivative absorption maxima follows a direct relationship with the thermally aged natural ester oil.

Investigation on space charge and charge trap characteristics of gamma-irradiated epoxy micro–nano composites

Epoxy nano–micro composite specimen prepared with micro silica and ion trapping nanoparticle,by shear mixing process,was exposed to gamma radiation and its performance for space charge and charge trap characteristics were analysed.The threshold for space charge accumulation of epoxy nanocomposites reduces and rate of space charge accumulation increases with an increase in dosage of gamma irradiation.The average growth of space charge density during poling and charge decay rate during depoling are relatively higher for gamma-irradiated specimens than the virgin specimen.The initial surface potential has a marginal reduction with increase in the dosage of gamma radiation,but the surface potential decay rate has increased significantly.Trap distribution characteristics indicate more number of shallow traps and increase in charge mobility after irradiation.The relative permittivity and loss tangent of the specimens have high impact due to gamma irradiation.The activation energy calculated from DC conductivity by Arrhenius law reduces with increment in radiation dose.Laser-induced breakdown spectroscopy reflected no change in elemental composition with gamma-irradiated specimen.The variation in plasma temperature and ion line to atomic line intensity ratio with dosage of gamma radiation have direct correlation to the Vickers hardness number of the specimens.

Classification of thermal ageing impact of ester fluid-impregnated pressboard material adopting LIBS

This article reports the thermal ageing of ester fluid-impregnated pressboard material along with its performance evaluation using optical emission spectroscopy(OES)and classifi-cation using machine-learning algorithms adopting laser-induced breakdown spectroscopy(LIBS).The surface discharge analysis on ester-impregnated pressboard(EIP)is studied using OES and the plasma temperature was evaluated based on the Cu I emission lines which were higher under the negative DC compared with the positive DC and AC voltages.The LIBS analysis was performed on the EIP material operated at different energy levels in order to acquire the optimal energy required to be used for its classification algorithm.The intensity ratio and electron density evaluated from LIBS studies correlated well with the plasma temperature.The lower limit of detection(LOD)calculated based on linear regression analysis for copper peak was around 3.5 times higher than the identification of carbon peak.The machine-learning techniques like principal component analysis and neural network algorithm have been performed on the LIBS spectral dataset in order to classify the ageing of EIP material.Artificial neural network adopting LIBS provided a better classi-fication accuracy on all the aged samples compared with principal component analysis which classified only for the samples aged at higher temperatures.