Transformer oil insulation aging based on Raman spectral data processing and peak identification

被引:0
|
作者
Liu Q. [1 ]
Zhang Y. [1 ]
Yan R. [2 ]
机构
[1] Fujian Key Laboratory of New Energy Generation and Power Conversion, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou
[2] School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou
基金
中国国家自然科学基金;
关键词
denoising; fluorescence background; local weighted signal-to-noise ratio; spectral peak identification; transformer oil aging evaluation;
D O I
10.19783/j.cnki.pspc.230997
中图分类号
学科分类号
摘要
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. © 2024 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码:158 / 166
页数:8
相关论文
共 28 条
  • [11] WAN Fu, YANG Manlin, HE Peng, Et al., Raman spectroscopy detection and signal processing method for the gases in transformer oil, Chinese Journal of Scientific Instrument, 37, 11, pp. 2482-2488, (2016)
  • [12] LU Luogeng, SHI Jinfang, QIU Rong, Et al., Peak detection algorithm for laser-induced breakdown spectroscopy based on scale adaptation of wavelet transform, Manufacturing Automation, 42, 11, pp. 51-55, (2020)
  • [13] LIU Wei, Research on IMS-TOFMS adaptive denoising and spectral peak identification algorithm and implementation of analysis software, (2022)
  • [14] JIANG Chengzhi, SUN Qiang, LIU Ying, Et al., A new peak detection algorithm of Raman spectra, Spectroscopy and Spectral Analysis, 34, 1, pp. 103-107, (2014)
  • [15] LI Qichen, LI Minzan, YANG Wei, Et al., Research progress on the rapid detection of soil components using Raman spectroscopy: a review, Transactions of the Chinese Society of Agricultural Engineering, 39, 7, pp. 1-9, (2023)
  • [16] LEI Linping, Curve smooth denoising based on Savitzky-Golay algorithm, Computer and Information Technology, 22, 5, pp. 30-31, (2014)
  • [17] WANG Haipeng, CHU Xiaoli, CHEN Pu, Et al., Research and application progress of algorithms for spectral baseline correction, Chinese Journal of Analytical Chemistry, 49, 8, pp. 1270-1281, (2021)
  • [18] SI Ganshang, LIU Jiaxiang, LI Zhengang, Et al., Fluorescence background subtraction algorithm of UV Raman based on morphology and polynomial fitting, Acta Optica Sinica, 42, 22, pp. 200-208
  • [19] XIE Jiawen, Background model and system design of Raman spectroscopy based on Ipro-Poly algorithm, (2020)
  • [20] HU Haibing, BAI Jing, XIA Guo, Et al., Improved baseline correction method based on polynomial fitting for Raman spectroscopy, Photonic Sensors, 8, 4, pp. 332-340, (2018)