摘要
SF_(6)作为气体绝缘设备中最常用的绝缘气体,掌握其分解组分的成分、含量等参数对于及时准确判断设备的绝缘老化状态十分重要。基于拉曼光谱技术,设计了一套拉曼检测分析系统,该系统由气体测量模块、功能模块、嵌入式控制模块、计算机模块等多个组成部分构成,具备温压调控、信号增强、混合气体检测分析的能力。通过配置一定浓度的SF_(6)及其分解组分混合气体进行了拉曼系统的实验测试,定性识别SF_(6)、SO_(2)F2、SO_(2)、CO_(2)、CO和H_(2)S的特征峰分别为778 cm^(-1)、861 cm^(-1)、1138 cm^(-1)/1378 cm^(-1)、1269 cm^(-1)、2185 cm^(-1)、2600 cm^(-1),与各自的NIST标准值一致,以证实该拉曼光谱检测系统的可行性,为SF_(6)分解组分的检测提供了良好的基础,对保障电网的安全稳定运行具有重要意义。
SF_(6)is the most commonly used insulating gas in gas insulated equipment.It is important to know the composition and content of decomposition components to timely and accurately judge the insulation and aging of equipment.Therefore,based on Raman spectroscopy technology,a set of Raman detection system is designed in this paper,which is composed of gas measurement module,function module,embedded control module,computer module and others,with the functions of temperature and pressure control,signal enhancement,mixed gas detection and analysis.The experiment of Raman system is carried out with SF_(6)and its decomposed components mixture.The characteristic peaks of SF_(6),SO_(2)F2,SO_(2),CO_(2),CO,and H_(2) S are 776 cm^(-1),861 cm^(-1),1138 cm^(-1)and 1378 cm^(-1),1269 cm^(-1),2185 cm^(-1),2600 cm^(-1)respectively,which are consistent with the NIST standard values.It is proved that the Raman spectrum detection system is feasible and provides a good basis for the detection of SF_(6)decomposition components,which is of great significance to ensure the safe and stable operation of power grid.
作者
张振宇
李永祥
阎寒冰
柳逢春
涂杉
高新
周渠
ZHANG Zhen-yu;LI Yong-xiang;YAN Han-bing;LIU Feng-chun;TU Shan;GAO Xin;ZHOU Qu(Shanxi Electric Power Research Institute,State Grid Shanxi Electric Power Company,Taiyuan 030001,China;College of Engineering and Technology,Southwest University,Chongqing 400715,China)
出处
《光学与光电技术》
2021年第6期11-18,共8页
Optics & Optoelectronic Technology
基金
国网山西省电力公司科技项目:基于拉曼光谱的SF6分解特征组分检测技术及应用研究(520530200003)资助。
关键词
气体绝缘设备
六氟化硫
分解特征组分
拉曼光谱
检测分析系统
gas insulated equipment
SF_(6)
decomposition characteristic components
Raman spectroscopy
detection and analysis system