Surface enhanced substrate simulation optimized coupled PLS model for quantitative detection of acetone in transformer oil

Abstract

AbstractAcetone is a reliable index to evaluate the aging state of oil paper insulation. It is of great significance to study a fast, highly sensitive and accurate method of detecting acetone in oil to ensure the safe and stable operation of oil-immersed transformers. In this paper, a method based on surface enhanced Raman spectroscopy for the detection of acetone in oil is investigated. Based on the mechanism of surface-enhanced Raman spectroscopy (SERS), a simulation model of surface-enhanced substrate was constructed using COMSOL software, and it was determined that the enhancement effect was better when silver nanowires/zinc oxide nanorods (Ag/ZnO) composite structure was selected. Ag/ZnO substrates were prepared by solvothermal method and experimentally tuned and characterized to obtain SERS substrates with high enhancement properties. The substrate coupled partial least square (PLS) model was used to establish a method for the detection of acetone in insulating oils, and its limit of quantification for acetone was 0.003 mg/g, which meets the requirements for engineering testing.