Analysis of furfural dissolved in transformer oil based on confocal laser Raman spectroscopy

Chen, Weigen; Gu, Zhaoliang; Zou, Jian; Wan, Fu; Xiang, Yingzhu

doi:10.1109/tdei.2015.005434

Cited by 65 publications

(26 citation statements)

References 23 publications

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“…Thus, the loading plots show us which vibrational bands have significantly contributed to the differences seen in the PCA plot, and provide more information on the Raman spectra of oil in each aging stage. Some relatively high (positive and negative) values are marked and associated with their corresponding variables in the Raman spectra, such as peaks from furfural (1418 cm −1 , 1470 cm −1 and 1670 cm −1 ), CO (2144 cm −1 ), CO2 (2802 cm −1 ); acetone, which is the recently proposed aging characteristic, generated peaks at 526 cm −1 , 780 cm −1 , 1211 cm −1 and 1712 cm −1 [29,37]. From the loading plot we can see that PC1 has a high correlation ranging all the Raman bands.…”

Section: Classification Of the Training Samplesmentioning

confidence: 99%

Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers

et al. 2016

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Abstract:The aging of oil-paper insulation in power transformers may cause serious power failures. Thus, effective monitoring of the condition of the transformer insulation is the key to prevent major accidents. The purpose of this study was to explore the feasibility of confocal laser Raman spectroscopy (CLRS) for assessing the aging condition of oil-paper insulation. Oil-paper insulation samples were subjected to thermal accelerated ageing at 120 • C for up to 160 days according to the procedure described in the IEEE Guide. Meanwhile, the dimension of the Raman spectrum of the insulation oil was reduced by principal component analysis (PCA). The 160 oil-paper insulation samples were divided into five aging stages as training samples by clustering analysis and with the use of the degree of polymerization of the insulating papers. In addition, the features of the Raman spectrum were used as the inputs of a multi-classification support vector machine. Finally, 105 oil-paper insulation testing samples aged at a temperature of 130 • C were used to further test the diagnostic capability and universality of the established algorithm. Results demonstrated that CLRS in conjunction with the PCA-SVM technique provides a new way for aging stage assessment of oil-paper insulation equipment in the field.

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Section: Classification Of the Training Samplesmentioning

confidence: 99%

Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers

et al. 2016

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“…Furfural is a furan derivative in which the hydrogen atom is replaced by an aldehyde group, also called furan formaldehyde [ 20 ]. In the process of transformer aging, the total source of furfural is the hydrolysis ring of cellulose molecules (the component of insulating paper) in the pyrolysis reaction [ 21 , 22 ]. Therefore, the content of furfural directly reflects the degree of aging of the instrument.…”

Section: Introductionmentioning

confidence: 99%

Charge Transfer Effect on Raman and Surface Enhanced Raman Spectroscopy of Furfural Molecules

et al. 2017

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The detection of furfural in transformer oil through surface enhanced Raman spectroscopy (SERS) is one of the most promising online monitoring techniques in the process of transformer aging. In this work, the Raman of individual furfural molecules and SERS of furfural-Mx (M = Ag, Au, Cu) complexes are investigated through density functional theory (DFT). In the Raman spectrum of individual furfural molecules, the vibration mode of each Raman peak is figured out, and the deviation from experimental data is analyzed by surface charge distribution. In the SERS of furfural-Mx complexes, the influence of atom number and species on SERS chemical enhancement factors (EFs) are studied, and are further analyzed by charge transfer effect. Our studies strengthen the understanding of charge transfer effect in the SERS of furfural molecules, which is important in the online monitoring of the transformer aging process through SERS.

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“…Some relatively high (positive or negative) values were marked and associated with their corresponding variables in the Raman spectra, such as peaks of furfural (1371 cm −1 , 1418 cm −1 , 1470 cm −1 and 1670 cm −1 ), CO (2144 cm −1 ), CO 2 (1285 cm −1 , 2802 cm −1 ), methanol (1453 cm −1 ); acetone, which is the recently proposed aging characteristic, generated peaks at 939 cm −1 , 1211 cm −1 , 1712 cm −1 and 3080 cm −1 [22], [23]. Taking the above characteristic substances as examples, Raman spectroscopy can reflect the state information of oil-paper insulation.…”

Section: The Keca For Feature Extractionmentioning

confidence: 87%

“…Furfural in oil has been quantitatively analyzed with a detection limit of 14.4 mg/L by a characteristic Raman peak at 1707 cm −1 [21]. The further study reached a detection limit of 0.10 mg/L by utilizing extraction technology [22]. In addition, Raman spectra was used to detect eight typical fault characteristic gases in oil, and the detectability of the system satisfies the requirements of gas diagnosis in power transformer [23].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

et al. 2019

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This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the selfmade hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy. INDEX TERMS Surface enhanced Raman scattering, oil-paper insulation, aging stage, kernel entropy component analysis.

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Analysis of furfural dissolved in transformer oil based on confocal laser Raman spectroscopy

Cited by 65 publications

References 23 publications

Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers

Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers

Charge Transfer Effect on Raman and Surface Enhanced Raman Spectroscopy of Furfural Molecules

Recognition of Aging Stage of Oil–Paper Insulation Based on Surface Enhanced Raman Scattering and Kernel Entropy Component Analysis

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