Transformer oil insulation aging based on Raman spectral data processing and peak identification
DOI：10.19783/j.cnki.pspc.230997
Key Words:denoising  fluorescence background  spectral peak identification  local weighted signal-to-noise ratio  transformer oil aging evaluation
Author NameAffiliation
LIU Qingzhen1 1. Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China 
ZHANG Yi1 1. Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China 
YAN Renwu2 1. Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China 
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Abstract:There are problems in that the Raman analysis of transformer oil is usually interfered with by noise and fluorescent background, and it is difficult to identify the position of the spectral peak. Thus this paper proposes an improved data processing and spectral peak recognition algorithm for the Raman analysis of transformer oil aging evaluation. An adaptive Savitzky-Golay filtering method is proposed, and adaptive window-size Raman spectral data is introduced for denoising. An improved polynomial fitting algorithm is used to remove the fluorescence background processing of the de-noised data to reduce its influence on the fitting results. Each data point is weighted according to the distance between the data point and the expected Raman signal, so as to achieve more accurate de-fluorescence background processing. The aging degree of transformer oil is identified by spectral peak recognition technology, and the spectral peak is identified by the Gaussian window discrimination method with two scales, and the authenticity of the suspected Raman spectral peak is judged by the local weighted signal-to-noise ratio (LW_SNR). Finally, the effectiveness of the proposed algorithm in transformer oil aging evaluation is proved by experiment.
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