|
|
|
|
|
|
Quantitative Analysis of Trace O Concentration in SF6 with Laser-Induced Breakdown Spectroscopy |
YANG Wen-bin1,2, LI Bin-cheng1,3* |
1. The Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China |
|
|
Abstract Sulfur hexafluoride (SF6) is widely used in gas-insulated high-voltage equipments due to its excellent insulation and arc extinguishing performance. However, when impurities such as trace water and oxygen are present in SF6, the by-products, which are formed due to equipment faults and partial discharge, can react with these impuriti to form stable sulfur oxyfluorides. The equipment insulation performance can be degraded significantly by those stable sulfur oxyfluorides, causing threat to the safety of the power system. The detection and analysis of the impurities such as water, oxygen and decompositions in SF6 are therefore of great importance. In this paper, laser-induced breakdown spectroscopy is employed to measure trace O in SF6. CaF2 is used as window material to solve the degradation of the excitation energy caused by the corrosion of the window material by SF6 decompositions, to eliminate the pollution problem caused by the reaction between the window material and the breakdown products of SF6 gas, and to reduce the change of plasma state caused by the change of excitation condition. By correcting the spectral baseline with an iterative wavelet transform and suppressing the noise with a wavelet transform with soft thresholding, a limit of detection of 38ppm is achieved from the experimental calibration curve. Furthermore, a quantitative model based on partial least squares (PLS) is developed to achieve better stability and precision.
|
Received: 2017-01-13
Accepted: 2017-05-26
|
|
Corresponding Authors:
LI Bin-cheng
E-mail: bcli@uestc.edu.cn
|
|
[1] Suehiro J, Zhou G, Hara M. Sens. Actuators B Chem.,2005, 105:164.
[2] Tsai W T. J. Fluor. Chem.,2007, 128:1345.
[3] Christophorou L G, Olthoff J K. J. Phys. Chem. Ref. Data,2000, 29:267.
[4] Zhang X X, Liu H, Ren J B, et al. Spectrochim. Acta A, 2015, 136(B5): 884.
[5] Beyer C, Jenett H, Klockow D. IEEE Trans. Dielectr. Electr. Insul.,2000, 7:234.
[6] Kurte R, Heise H M, Klockow D J. Mol. Struct.,1999, 480-481:211.
[7] Heise H M, Kurte R, Fischer P, et al. Fresenius J. Anal. Chem., 1997, 358: 793.
[8] Meier R, Kneubühl F K, Schtzau H. J. Appl. Phys. B, 1989, 48:187.
[9] Han D, Lin T, Zhang G Q, et al. IEEE Trans. Dielectr. Electr. Insul.,2015, 22:799.
[10] TANG Ju, Zeng Fu-ping, ZHANG Xiao-xing, et al. IEEE Trans. Dielectr. Electr. Insul.,2014, 21:1226.
[11] Luo J, Fang Y H, Zhao Y D, et al. Anal. Methods,2015, 7:1200.
[12] Galloway C M, LeRu E C, Etchegoin P G. Appl. Spectrosc.,2009, 63(12):1370.
[13] Zhang B, Sun L X, Yu H B, et al. Spectrochim. Acta B, 2015, 107:32.
[14] Schlenka J, Hildebrand L, Moros J, et al. Anal. Chim. Acta, 2012, 754:8.
[15] Cremers D A, Radziemski L J. Handbook of Laser-Induced Breakdown Spectroscopy. John Wiley & Sons, England, 2006.
[16] YANG Ping, YAO Ming-yin, HUANG Lin, et al(杨 平,姚明印,黄 林,等). Journal of Optoelectronics·Laser(光电子·激光), 2015, 26:141.
[17] CONG Zhi-bo, SUN Lan-xiang, XIN Yong, et al(从智博,孙兰香,辛 勇,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(2):542.
|
[1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[2] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
[3] |
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. |
[4] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[5] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[6] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[7] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[8] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[9] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[10] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[11] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[12] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[13] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[14] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[15] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
|
|
|
|