矿物油中糠醛液芯光纤增强拉曼光谱原位检测方法

doi:10.19595/j.cnki.1000-6753.tces.221789

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (3547 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Oil-immersed power equipment is one of the core equipment in the power system. It undertakes the tasks of voltage level conversion, energy transmission, power distribution, and line support in the power grid. Its insulation status is crucial. The paper insulation of oil-immersed power equipment is mainly composed of cellulose. Affected by electricity and heat, the cellulose in paper insulation undergoes decomposition, resulting in the reduction of polymerization and mechanical strength of paper insulation. The process is accompanied by the dissolution of the cracking products in the insulating oil, such as furfural, methanol, acetone, ethyl acetate, etc. Furfural content in the mineral oil can effectively represent the insulation aging status of oil-immersed power equipment. Furfural, as an important aging characteristic, can effectively reflect the degree of polymerization of insulation paper. Therefore, accurately detecting furfural content in the oil is critical for the safe and dependable operation of oil-immersed power equipment, even the power grid.
Compared with traditional methods, Raman spectroscopy is a non-invasive detection method without sample pre-processing. It is reliable and straightforward. However, it is challenging to meet the demand for detecting dissolved furfural in oil of oil-immersed power equipment precisely due to its weak signal intensity. Liquid-core fiber can enhance spatial transmission efficiency and improve the collection efficiency of Raman scattering due to its optical properties. In this paper, the research on the in-situ detection method of furfural in oil based on liquid-core fiber-enhanced Raman spectroscopy is carried out; An adapter to simultaneously realize position fixing, laser coupling, and liquid feeding of liquid-core fibers has been developed; An experimental platform for liquid-core fiber-enhanced Raman spectroscopy was set up. The transmission characteristics of the liquid-core fiber for insulating oil were obtained, and the suitable fiber length for insulating oil detection was determined to be 50 cm.
In this paper, the vibration model of the furfural molecule was simulated and calculated based on DFT density functional theory using Gaussian simulation software, and its vibration attribution was determined. A model of multi-molecular furfural and its composite model with Icosane was established to research its vibration characteristics in insulating oil further, and it provides a theoretical basis for experimental analysis.
Insulating oils containing different concentrations of furfural were measured. The mathematical model of the furfural concentration in oil and its internal standardized Raman peak intensity was established, and the accurate quantitative detection of furfural in oil was realized. The limit of detection concentration reached 0.164 mg/L, corresponding to the range of 656~751 that the degree of polymerization of insulation paper. In this paper, a linear model, and a model modified by the internal standard method were used to perform ten independent backtesting on samples with known concentrations. The internal standard modified model is more stable than the linear one, with a maximum offset of -1.32%. Insulating oil samples containing furfural were also tested for stability experiments, and the coefficient of variation of the characteristic peak intensity of furfural in 120 sets of data was 0.68%.
The research results show that the detection method of furfural in oil based on liquid-core fiber-enhanced Raman spectroscopy (LC-FERS) features simple, good stability, high sensitivity, and fast. It provides a new method for rapid and accurate detection of furfural in the oil of oil-immersed power equipment.

Key words： Oil-immersed power equipment liquid-core fiber enhancement furfural in oil Raman spectroscopy

Received: 21 September 2022

PACS:

TM614

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Song Ruimin
	Wang Jianxin
	Chen Weigen
	Wang Ziyi
	Zhang Xinyuan

Cite this article:

Song Ruimin,Wang Jianxin,Chen Weigen等. Detection Method for Furfural Dissolved in Mineral Oil Based on Liquid-Core Fiber Enhanced Raman Spectroscopy[J]. Transactions of China Electrotechnical Society, 2023, 38(24): 6828-6838.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.221789 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I24/6828

[1] 李元, 刘宁, 梁钰, 等. 基于温升特性的油浸式变压器负荷能力评估模型[J]. 中国电机工程学报, 2018, 38(22): 6737-6746.
Li Yuan, Liu Ning, Liang Yu, et al.A model of load capacity assessment for oil-immersed transformer by using temperature rise characteristics[J]. Proceedings of the CSEE, 2018, 38(22): 6737-6746.
[2] 陈图南, 马凤翔, 王刘芳, 等. 高分子渗透膜在变压器油中溶解气体分析中的应用[J]. 电工技术学报, 2022, 37(3): 750-766.
Chen Tunan, Ma Fengxiang, Wang Liufang, et al.Application of polymers membrane in dissolved gas analysis: a review[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 750-766.
[3] 邓永清, 阮江军, 董旭柱, 等. 基于流线分析的10kV油浸式变压器绕组热点温度反演模型建立及验证研究[J]. 中国电机工程学报, 2023, 43(8): 3191-3204.
Deng Yongqing, Ruan Jiangjun, Dong Xuzhu, et al.Establishment and verification of 10kV oil immersed transformer winding hot spot temperature inversion model based on streamline analysis[J]. Proceedings of the CSEE, 2023, 43(8): 3191-3204.
[4] 陈伟根, 滕黎, 刘军, 等. 基于遗传优化支持向量机的变压器绕组热点温度预测模型[J]. 电工技术学报, 2014, 29(1): 44-51.
Chen Weigen, Teng Li, Liu Jun, et al.Transformer winding hot-spot temperature prediction model of support vector machine optimized by genetic algorithm[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 44-51.
[5] 董明, 王丽, 吴雪舟, 等. 油纸绝缘介电响应检测技术研究现状与发展[J]. 高电压技术, 2016, 42(4): 1179-1189.
Dong Ming, Wang Li, Wu Xuezhou, et al.Status and progress in study of dielectric response technology for oil-paper insulation[J]. High Voltage Engineering, 2016, 42(4): 1179-1189.
[6] 王建新, 陈伟根, 王品一, 等. 变压器故障特征气体空芯反谐振光纤增强拉曼光谱检测[J]. 中国电机工程学报, 2022, 42(16): 6136-6144, 6187.
Wang Jianxin, Chen Weigen, Wang Pinyi, et al.Analysis of fault characteristic gases dissolved in transformer oil based on hollow-core anti-resonant fiber-enhanced Raman spectroscopy[J]. Proceedings of the CSEE, 2022, 42(16): 6136-6144, 6187.
[7] 于永进, 姜雅男, 李长云. 基于鲸鱼优化-长短期记忆网络模型的机-热老化绝缘纸剩余寿命预测方法[J]. 电工技术学报, 2022, 37(12): 3162-3171.
Yu Yongjin, Jiang Yanan, Li Changyun.Prediction method of insulation paper remaining life with mechanical-thermal synergy based on whale optimization algorithm-long-short term memory model[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3162-3171.
[8] 陈伟根, 张知先, 李剑, 等. 电气设备状态参量智能传感技术[J]. 中国电机工程学报, 2020, 40(增刊1): 323-342.
Chen Weigen, Zhang Zhixian, Li Jian, et al.Intelligent sensing technology for power equipment state parameters[J]. Proceedings of the CSEE, 2020, 40(S1): 323-342.
[9] 姜雅男, 于永进, 李长云. 基于改进TOPSIS模型的绝缘纸机-热老化状态评估方法[J]. 电工技术学报, 2022, 37(6): 1572-1582.
Jiang Yanan, Yu Yongjin, Li Changyun.Evaluation method of insulation paper deterioration status with mechanical-thermal synergy based on improved TOPSIS model[J]. Transactions of China Electrotechnical Society, 2022, 37(6): 1572-1582.
[10] 赵莉华, 徐立, 刘艳, 等. 基于点对称变换与图像匹配的变压器机械故障诊断方法[J]. 电工技术学报, 2021, 36(17): 3614-3626.
Zhao Lihua, Xu Li, Liu Yan, et al.Transformer mechanical fault diagnosis method based on symmetrized dot patter and image matching[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3614-3626.
[11] 刘骥, 程炜超, 张明泽, 等. 换流变压器油纸绝缘多因子老化介电响应评估方法[J]. 高电压技术, 2022, 48(5): 1684-1694.
Liu Ji, Cheng Weichao, Zhang Mingze, et al.Dielectric response evaluation method for multi-factor aging of converter transformer oil-paper insulation[J]. High Voltage Engineering, 2022, 48(5): 1684-1694.
[12] 廖瑞金, 刘捷丰, 杨丽君, 等. 电力变压器油纸绝缘状态评估的频域介电特征参量研究[J]. 电工技术学报, 2015, 30(6): 247-254.
Liao Ruijin, Liu Jiefeng, Yang Lijun, et al.Investigation on frequency domain dielectric characteristics for condition assessment of transformer oil-paper insulation[J]. Transactions of China Electrotechnical Society, 2015, 30(6): 247-254.
[13] Zhang Xiaorui, Sun Xue, Lü Tong, et al.Preparation of PI porous fiber membrane for recovering oil-paper insulation structure[J]. Journal of Materials Science: Materials in Electronics, 2020, 31(16): 13344-13351.
[14] 廖瑞金, 杨丽君, 郑含博, 等. 电力变压器油纸绝缘热老化研究综述[J]. 电工技术学报, 2012, 27(5): 1-12.
Liao Ruijin, Yang Lijun, Zheng Hanbo, et al.Reviews on oil-paper insulation thermal aging in power transformers[J]. Transactions of China Electrotechnical Society, 2012, 27(5): 1-12.
[15] 廖瑞金, 冯大伟, 李金忠, 等. 绝缘油中糠醛稳定性的影响因素[J]. 高电压技术, 2018, 44(6): 1729-1734.
Liao Ruijin, Feng Dawei, Li Jinzhong, et al.Influential factors on stability of furfural in insulating oil[J]. High Voltage Engineering, 2018, 44(6): 1729-1734.
[16] Feng Dawei, Yang Lijun, Zhou Luowei, et al.Effect of oil-paper-pressboard mass ratio on furfural content in transformer oil[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2019, 26(4): 1308-1315.
[17] 张夏子, 吴丽华, 巩子路, 等. 紫外分光法测定白酒中的糠醛[J]. 酿酒, 2021, 48(6): 86-87.
Zhang Xiazi, Wu Lihua, Gong Zilu, et al.Determination of furfural in Baijiu by UV spectrophotometry[J]. Liquor Making, 2021, 48(6): 86-87.
[18] 刘真, 朱丽霞. 5-羟甲基糠醛、糠醛、乙酰呋喃、呋喃酮、5-甲基糠醛的高效液相检测方法[J]. 食品研究与开发, 2019, 40(18): 166-170.
Liu Zhen, Zhu Lixia.High performance liquid phase method for determination of pentahydroxymethylfurfural, furfural, acetylfuran, furanone and pentamethylfurfural[J]. Food Research and Development, 2019, 40(18): 166-170.
[19] 黄露, 李玉真, 万嗣宝, 等. 电化学方法在糠醛类化合物检测中的研究进展[J]. 食品工业, 2021, 42(4): 359-363.
Huang Lu, Li Yuzhen, Wan Sibao, et al.Application progress of electrochemical methods in the detection of furfural compounds[J]. The Food Industry, 2021, 42(4): 359-363.
[20] 顾朝亮, 陈伟根, 邹经鑫, 等. 变压器油中溶解微量糠醛的激光拉曼光谱检测方法[J]. 电工技术学报, 2016, 31(20): 219-227.
Gu Zhaoliang, Chen Weigen, Zou Jingxin, et al.Detection of trace furfural dissolved in transformer oil using laser Raman spectroscopy[J]. Transactions of China Electrotechnical Society, 2016, 31(20): 219-227.
[21] Chen Weigen, Wang Jianxin, Wan Fu, et al.Review of optical fibre sensors for electrical equipment characteristic state parameters detection[J]. High Voltage, 2019, 4(4): 271-281.
[22] Song Ruimin, Chen Weigen, Yang Dingkun, et al.Aging assessment of oil-paper insulation based on visional recognition of the dimensional expanded Raman spectra[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-10.
[23] Langer J, De Aberasturi D J, Aizpurua J, et al. Present and future of surface-enhanced Raman scattering[J]. ACS Nano, 2020, 14(1): 28-117.
[24] 高嘉敏, 张卓旻, 李攻科. 表面增强拉曼光谱定量分析技术研究进展[J]. 分析测试学报, 2016, 35(12): 1647-1653.
Gao Jiamin, Zhang Zhuomin, Li Gongke.Research progress of quantitative analysis techniques for surface enhanced Raman spectroscopy[J]. Journal of Instrumental Analysis, 2016, 35(12): 1647-1653.
[25] Smekal A.Zur quantentheorie der dispersion[J]. Naturwissenschaften, 1923, 11(43): 873-875.
[26] Raman C V, Krishnan K S.A new type of secondary radiation[J]. Nature, 1928, 121(3048): 501-502.
[27] 吴雪瑞, 江军, 汪卓玮, 等. 基于微纳光纤倏逝场传感的变压器油中微水含量检测[J]. 高电压技术, 2020, 46(6): 1955-1961.
Wu Xuerui, Jiang Jun, Wang Zhuowei, et al.Detection of moisture in transformer oil based on micro-nano fiber evanescent field sensing[J]. High Voltage Engineering, 2020, 46(6): 1955-1961.
[28] Wang Ziyi, Song Ruimin, Chen Weigen, et al.Vibrational spectra and molecular vibrational behaviors of dibenzyl disulfide, dibenzyl sulphide and bibenzyl[J]. International Journal of Molecular Sciences, 2022, 23(4): 1958.
[29] Lu Tian, Chen Feiwu.Multiwfn: a multifunctional wavefunction analyzer[J]. Journal of Computational Chemistry, 2012, 33(5): 580-592.
[30] 刘枫林, 徐魏. 石蜡基和环烷基变压器油的性能比较[J]. 变压器, 2004, 41(7): 27-30.
Liu Fenglin, Xu Wei.Characteristic comparison between paraffine-base and naphthene-base transformer oils[J]. Transformer, 2004, 41(7): 27-30.
[31] Du Y, Mamishev A V, Lesieutre B C, et al.Moisture solubility for differently conditioned transformer oils[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(5): 805-811.
[32] Gao Ying, Dai Liankui, Zhu Huadong, et al.Quantitative analysis of main components of natural gas based on Raman spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(1): 67-76.
[33] Xue Chendong.Monitoring paper insulation aging by measuring furfural contents in oil[C]//7th International Symposium on High Voltage Engineering, Dresden, Germany, 1991: 139-142.
[34] Cheim L, Platts D, Prevost T, et al.Furan analysis for liquid power transformers[J]. IEEE Electrical Insulation Magazine, 2012, 28(2): 8-21.
[35] Stebbins R D, Myers D S, Shkolnik A B.Furanic compounds in dielectric liquid samples: review and update of diagnostic interpretation and estimation of insulation ageing[C]//Proceedings of the 7th International Conference on Properties and Applications of Dielectric Materials (Cat. No.03CH37417), Nagoya, Japan, 2003: 921-926.
[36] De Pablo A.Interpretation of furanic compounds analysis-degradation models[Z]. 1997.
[37] Heisler A, Banzer A, Ilgen R.Zustandsbeurteilung von transformatoren mit furfurol-bestimmung[J]. Das Magazin für die Energiewirtschaft, 2003, 102(16): 58-59.
[38] Li Enwen, Song Bin.Transformer health status evaluation model based on multi-feature factors[C]//2014 International Conference on Power System Technology, Chengdu, China, 2014: 1417-1422.
[39] Lin Chaohui, Zhang Bide, Yuan Yuchun.The aging diagnosis of solid insulation for oil-immersed power transformers and its remaining life prediction[C]// 2010 Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, 2010: 1-3.