摘要
油中特征气体检测与分析是判断油浸式电力变压器早期潜伏性故障的有效方法之一。拉曼光谱技术具有利用单一波长的激光同时实现混合气体直接检测的独特优势。然而,拉曼信号弱导致气体检出限较高,无法满足变压器故障特征气体检测需求。鉴于此,该文开展变压器故障特征气体空芯反谐振光纤增强拉曼光谱检测研究,对H、CO、CO、CH、CH、CH、CH等气体的检出限分别达到了8、22、5、2、6、3、3μL/L;结合最小二乘法,建立各气体特征拉曼峰峰面积与其浓度间的定量分析模型,并对配置的气体样品进行检测与分析。研究表明,光纤增强拉曼光谱技术具有高选择性、低检出限、高准确性及重复性,为变压器故障特征气体拉曼光谱在线监测与变压器故障诊断奠定了基础。
The detection and analysis of characteristic gases dissolved in transformer oil is one of the most effective methods to diagnose the potential faults in oil-immersed power transformers.Raman spectroscopy provides a unique advantage of using one single-wavelength laser to directly achieve the simultaneous detection of multi-gas mixtures.However,the unsatisfied limits of detection caused by the intrinsic weak Raman signal limit its practical application.In this paper,we conducted a research on hollow-core anti-resonant fiber-based Raman sensing of transformer-fault characteristic gases and realized the limits of detection of 8,22,5,2,6,3,and 3μL/L for H,CO,CO,CH,CH,CH,and CH,respectively.Moreover,we established a quantitative analysis model between the characteristic Raman peak area and concentrations of each gas using the least square method and analyzed a configured gas sample.The results show that the fiber-enhanced Raman spectroscopy has high selectivity,sensitivity,accuracy and repeatability,and provides a foundation for the on-line monitoring transformer-fault characteristic gases based on Raman spectroscopy and diagnosis of power transformers.
作者
王建新
陈伟根
王品一
张知先
蔚超
万福
汤思蕊
WANG Jianxin;CHEN Weigen;WANG Pinyi;ZHANG Zhixian;WEI Chao;WAN Fu;TANG Sirui(State Key Laboratory of Power Transmission Equipment&System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400044,China;State Grid Jiangsu Electric Power Research Institute,Nanjing 210024,Jiangsu Province,China;Chongqing Electric Power Design Institute,Yubei District,Chongqing 401121,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第16期6136-6144,共9页
Proceedings of the CSEE
基金
国家自然科学基金项目(U1766217)。
关键词
油浸式电力变压器
故障特征气体
拉曼光谱
空芯反谐振光纤
oil-immersed power transformer
fault characteristic gases
Raman spectroscopy
hollow-core anti-resonant fiber
