Simultaneous detection of multiple aging characteristic components in oil-paper insulation using sensitive Raman technology and microfluidics.

The aging characteristic components of oil-paper insulation reflect the aging status of the power equipment. In this study, we designed a novel microfluidic chip capable of automatic and rapid extraction of aging components from insulating oil. Combined with Raman spectroscopy technology, it enables simultaneous detection of various aging components. By optimizing the microfluidic chip structural and adopting an optical window encapsulation, it eliminates interference from the Polydimethylsiloxane (PDMS). Measurements and analyses were carried out on multiple oil samples containing three aging products (furfural, acetone, and methanol). The results indicate that this novel microfluidic chip facilitates simultaneous detection of multiple components, significantly improving the detection sensitivity of complex oil. The detection limits for furfural, acetone, and methanol in insulating oil are 0.43 mg/L, 1.04 mg/L, and 2.31 mg/L, respectively. This provides a new approach for the online detection of oil-paper insulation equipment.

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment.

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model.

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.

Degree of polymerization mapping Raman feature extraction of oil-paper insulation aging based on quadratic mutual information.

To assess the aging of oil-paper insulation, an accelerated aging experiment is executed. Raman spectroscopy, a nondestructive detection method with access to component identification and fault diagnosis, is used to analyze the aging of oil-paper insulation. Raman feature of oil-paper insulation aging with a close relationship with the degree of polymerization is obtained based on the concept of quadratic mutual information. By analyzing the relationship between the extracted feature and the degree of polymerization of samples at different aging degrees, the feasibility of reflecting the aging degree of oil-paper insulation is determined. By load analysis, the chemical correlation between the extracted feature and oil-paper insulation aging is clarified. For eight test samples, the prediction error of degree of polymerization based on Raman features is less than 50. These results show that the features extracted in this paper are helpful to realizing the Raman spectrum diagnosis of oil-paper insulation aging.

Multiple reflections enhanced fiber-optic photoacoustic sensor for gas micro-leakage.

A multiple reflections-enhanced fiber-optic photoacoustic (PA) gas sensor for gas micro-leakage is introduced. Multiple reflections of the excitation laser occur on the inner surface of a reflective ring to enhance the PA signal. The PA signal is obtained by measuring the deflection of the gold-coated poly (phenylene sulfide) (PPS) diaphragm with a Fabry-Perot interferometer (FPI). The second harmonic wavelength modulation spectrum (2f-WMS) technology can essentially eliminate the fundamental frequency noise generated by the wavelength-independent absorption of the reflective ring. Experimental results show that the PA signal can be effectively enhanced 11.7 times by the multiple reflections optical path compare with the double-pass optical path. The minimum detection limit of the system is achieved to be 23.6 ppb. The designed PA gas sensor is suited for remote detection of gas micro-leakage.