Detection of CO and CO2 Dissolved in Transformer Oil Based on Raman Spectroscopy and Neural Network

Transformer is an important part of power system, its life mainly depends on the mechanical strength and electrical integrity of its insulation. CO and CO
2
are one of the main fault characteristic gases dissolved in transformer oil. Thus, monitoring the CO and CO
2
dissolved in transformer oil is considered by the power industry as an important means to ensure the safe operation of power systems. This work proposes the measurement of the CO and CO
2
dissolved in transformer oil using Raman spectroscopy and generalized regression neural network (GRNN). Firstly, 200 samples were divided into training sets and test sets. After that, Raman spectroscopy as the input of GRNN, and The concentration of CO and CO
2
dissolved in transformer oil as the output of GRNN. Afterwards, the experimental method was incorporated to get the best smoothing factor σ for GRNN. Then, the GRNN with the best smoothing factor σ=24 was trained with the training sets, and the model was verified with the test sets. The experimental results show that the prediction accuracy of the prediction model of the CO and CO
2
dissolved in transformer oil based on GRNN is 90%. It also provides a new method for evaluating the health of transformer.