The insulation of mineral oil-based nanofluids was found to vary with different concentration level of nanoparticles. However, the mechanisms behind this research finding are not well studied. In this paper, mineral oil-based nanofluids were prepared by suspending TiO₂ nanoparticles with weight percentages ranging from 0.0057% to 0.0681%. The breakdown voltage and chop time of nanofluids were observed under standard lightning impulse waveform. The experimental results show that the presence of TiO₂ nanoparticles increases the breakdown voltage of mineral oil under positive polarity. The enhancement of breakdown strength tends to saturate when the concentration of nanoparticle exceeds 0.0227 wt%. Electronic traps formed at the interfacial region of nanoparticles, which could capture fast electrons in bulk oil and reduce the net density of space charge in front of prebreakdown streamers, are responsible for the breakdown strength enhancement. When the particle concentration level is higher, the overlap of Gouy–Chapman diffusion layers results in the saturation of trap density in nanofluids. Consequently, the breakdown strength of nanofluids is saturated. Under negative polarity, the electrons are likely to be scattered by the nanoparticles on the way towards the anode, resulting in enhanced electric fields near the streamer tip and the decrement of breakdown voltage.

The vibration spectroscopy (Raman and infrared) of widely concerned molecules in sulfur corrosion phenomenon (Dibenzyl Disulfide, Dibenzyl Sulphide, and Bibenzyl) is detailedly analyzed based on density functional theory and experimental measurement. The dominant conformations of these molecules are determined according to Boltzmann distribution in relative Gibbs free energy. Additionally, noncovalent interaction analysis is conducted to indicate intramolecular interaction. Vibration normal mode is assigned based on potential energy distribution, which comprehensively reveals the molecular vibrational behaviors. Conformations weighted spectra are obtained and compared with experimentally measured spectra. We found that experimental spectra are in good agreement with the theoretical spectra in B3LYP-D3(BJ)/6-311G** level with a frequency correction factor. Furthermore, the divergence among these molecules is discussed. The vibrational behavior of the methylene group in the molecule shows a trend with the presence of the sulfur atom.

Li-ion batteries are expected to become the mainstream devices for green energy storage or power supply in the future due to their advantages of high energy and power density and long cycle life. Monitoring the temperature and strain change characteristics of Li-ion batteries during operation is conducive to judging their safety performance. The hinged differential lever sensitization structure was used for strain sensitization in the design of an FBG sensor, which also allowed the simultaneous measurement of strain and temperature. The temperature and strain variation characteristics on the surface of a Li-ion soft-packed battery were measured using the des.igned sensor. This report found that the charging and discharging processes of Li-ion batteries are both exothermic processes, and exothermic heat release is greater when discharging than when charging. The strain on the surface of Li-ion batteries depends on electrochemical changes and thermal expansion effects during the charge and discharge processes. The charging process showed an increasing strain, and the discharging process showed a decreasing strain. Thermal expansion was found to be the primary cause of strain at high rates.

In order to overcome the shortcomings of traditional methods for corrosive sulfur detection in transformer oil, Raman spectroscopy based detection is proposed in this paper. The widely concerned corrosive sulfur, Dibenzyl Disulfide (DBDS), was chosen as the characteristic molecule to be detected. A series of oil samples with different DBDS concentrations was prepared. And these samples were extracted by 1‐methyl‐2‐pyrrolidinone for DBDS enrichment to improve the sensitivity of detection. Then Raman spectra of samples were obtained, and a linear model was established by analysing the relationship between the characteristic peak and the DBDS concentration. The limit of detection reached 7.98 mg/kg. For determining the DBDS concentrations causing sulfur corrosion, the sulfur weight content on the copper conductor surface was measured by Scanning Electron Microscope‐Energy Dispersive Spectrometer after a corrosion test. The results show that the corrosion limitation highly depends on the type of transformer oil, and the Raman spectroscopy detection can meet the limit of detection requirement in practical condition. Finally, an on‐site oil sample and five Lab‐made samples were detected via our new method and the current Gas Chromatography‐Mass Spectrometry based method. It is found that there is no significant divergence between the measurement results. And good applicability was also demonstrated in on‐site sample test.

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

Ensuring stable operation of the power system, the condition monitoring and diagnosis of electrical equipment are paramount. While numerous spectroscopic methods have been adopted for online monitoring and on-site diagnosis, industrial devices, constrained by factors such as size, often lag behind laboratory equipment in key metrics like signal-to-noise ratio and resolution. This can compromise the accuracy of diagnostic results. Addressing this issue, our study introduces a data-driven spectroscopic super-resolution algorithm. By generating extensive training data based on infrared and Raman spectroscopic features, we trained a convolutional neural network, achieving super-resolution reconstruction of low-resolution spectra. This method not only boasts superior performance and high portability but also significantly enhances the performance of compact spectroscopic devices and the accuracy of electrical equipment diagnosis.