|
|
|
|
|
|
| Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy |
| LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen |
College of Engineering and Technology, Southwest University, Chongqing 400715, China
|
|
|
|
|
Abstract Furfural content in transformer oil is one of the important indexes of insulation aging in power transformers. Infrared and Raman spectroscopy have the advantages of fast detection speed, fast analysis speed and non-destructive detection in effectively identifying substances. This paper compares furfural detection methods in transformer oil based on infrared and Raman spectroscopy. Based on the Fourier transform infrared spectrometer and laser confocal Raman spectroscopy instrument configuration of furfural transformer sample testing for the laboratory and its spectral data, using wavelet transform, the multinomial least square method and locally weighted regression fitting the collected infrared and Raman spectra is preprocessed, careful consideration of noise, resolution and effective information loss, etc., The polynomial least square method has the best preprocessing effect. The molecular model of furfural was established based on Gaussian software, and the attribution of the infrared and Raman absorption peaks of furfural was studied by density functional simulation calculation. The Raman and infrared detection characteristic peaks of furfural in transformer oil were determined to be 1 703 and 1 704 cm-1, respectively, combined with the experimental spectra. The repeatability experiments of infrared and Raman detection were carried out, and the relative standard deviations of the two detection methods were 7.21% and 8.67%, respectively. The relationship between the infrared and Raman 3D in situ spectrographs was analysed by drawing furfural infrared and Raman characteristic peak areas and transformer oil with different furfural concentrations. The infrared and Raman quantitative analysis models for furfural detection in transformer oil were established based on the least square method, and the goodness of fit was 0.998 and 0.885, respectively. Compared to the two kinds of detection methods of quantitative analysis of the model prediction results, the results show that Raman spectroscopy detection lower limit is lower than the infrared spectrometry, pretreatment by the multinomial least square method of spectral data of transformer oil furfural quantitative analysis of infrared and Raman model can well predict furfural content in transformer oil, to provide technical reference for related research.
|
|
Received: 2022-07-04
Accepted: 2022-11-03
|
|
|
|
Corresponding Authors:
ZHOU Qu
E-mail: zhouqu@swu.edu.cn
|
|
[1] Okabe S, Ueta G, Tsuboi T. IEEE Trans Dielect Electr In, 2013, 20(1): 346.
[2] LI Guang-mao, QIAO Sheng-ya, ZHU Chen, et al（李光茂，乔胜亚，朱 晨，等）. High Voltage Technology（高电压技术）, 2021, 47(6): 2007.
[3] QIAO Sheng-ya, LI Guang-mao, YANG Sen, et al（乔胜亚，李光茂，杨 森，等）. Transformer（变压器）, 2021, 58(1): 47.
[4] JIAO Qing-liang，LIU Ming，YU Kun，et al（焦青亮，刘 明，于 坤，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2022，42(1): 292.
[5] LI Shang-ke, LI Pao, DU Guo-rong, et al（李尚科，李 跑，杜国荣，等）. Journal of Food Safety and Quality（食品安全质量检测学报）, 2019, 10(24): 8204.
[6] GU Kun-shan, WANG Ji-fen（古锟山，王继芬）. Progress in Laser and Optoelectronics（激光与光电子学进展）, 2022, 59(10): 1030004.
[7] YANG Ding-kun, CHEN Wei-gen, WAN Fu, et al（杨定坤，陈伟根，万 福，等）. Proceedings of the CSEE（中国电机工程学报）, 2021, 41(13): 4710.
[8] FAN Zhou, CHEN Wei-gen, ZOU Jing-xin, et al（范 舟，陈伟根，邹经鑫，等）. Journal of Light Scattering（光散射学报）, 2018, 30(1): 46.
|
| [1] |
CHENG Jia-wei1, 2，LIU Xin-xing1, 2*，ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
| [2] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
| [3] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [4] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
| [5] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
| [6] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
| [7] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [8] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [9] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [10] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
| [11] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
| [12] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
| [13] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
| [14] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [15] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
|
|
|
|
