Quantitative detection of multicomponent SF6 decomposition products based on Fourier transform infrared spectroscopy combined with SCARS-DNN

Abstract

Accurate and efficient quantitative analysis of the decomposition products of the insulating medium SF₆ in gas-insulated switchgear (GIS) is important for an effective assessment of its internal insulation status. In this work, a quantitative calibration model of Fourier Transform Infrared Spectroscopy (FTIR) combined with SCARS-DNN (Stability Competitive Adaptive Reweighted Sampling-Deep Neural Network) is proposed for the rapid non-destructive detection of SF₆ decomposition products. First, the interference of the background gas SF₆ on the absorption spectra of the decomposition products is eliminated according to the Lambert-Beer law, while baseline correction and Savitzky-Golay (S-G) smoothing are used to remove baseline drift and noise. Subsequently, a Monte Carlo cross-validation method is used to detect and eliminate the anomalous samples. Then feature selection is performed using uninformative variable elimination (UVE) and stability competitive adaptive reweighted sampling (SCARS), and finally quantitative calibration models of FULL-DNN (full spectral band), UVE-DNN, and SCARS-DNN are developed. For the quantitative detection of SF₆ decomposition products, the SCARS-DNN model had the best prediction performance with a maximum reduction of 96.18% in the root mean square error (RMSE) and 96.11% in the mean absolute percentage error (MAPE). Results reveal that the relative errors are basically kept below 1.36% when predicting the three decomposition products, even in the presence of a high level of SF₆ interference. Therefore, the SCARS-DNN model is suitable for high-precision quantitative detection of SF₆ decomposition gas.

Keywords: Deep neural network; Fourier transform infrared spectroscopy; Quantitative analysis; SF(6) decomposition products.