A ppb-level online detection system for gas concentrations in CS2/SO2 mixtures based on UV-DOAS combined with VMD-CNN-TL model

Abstract

Carbon disulfide (CS2) and sulfur dioxide (SO2) are the main decomposition products of sulfur hexafluoride (SF6). Detecting the concentrations of these products plays a key role in early fault warning in SF6 electrical equipment. In this study, we report an online ppb-level detection system for a mixture of CS2 and SO2 based on ultraviolet differential optical absorption spectroscopy (UV-DOAS) combined with VMD-CNN-TL model. First, important features are extracted and recombined from the differential absorption spectra by variational modal decomposition (VMD) to obtain the combined differential absorption spectra (CDAS) data and mixed-gas differential absorption spectra (MDAS) data. A multilayer 1D convolutional neural network (CNN) model is then pre-trained on CDAS. Next, the parameters of the model are fine-tuned using transfer learning (TL) based on MDAS. TL solves the small sample size problem in detecting mixed-gas concentrations using neural network models. Lab-based results indicate that the system enables stable detection of CS2 (3.81–179.24 ppb) and SO2 (19.21–942.73 ppb) with mean absolute errors of 0.51 ppb and 1.12 ppb, respectively, which are the best results reported so far. Furthermore, the feasibility of the sensor system is verified for detection of gas mixtures at ppb-level by field testing.