摘要
为实现用SF_6气体分解产物来诊断和评估气体绝缘金属封闭开关设备(GIS)绝缘状态,必须寻找出恰当的特征参量,建立气体分解组分与GIS绝缘劣化的判断法则。为此构建了SF_6气体放电分解平台,做针-板缺陷下长期持续性放电分解实验以模拟GIS绝缘劣化全过程。选取SO_2F_2、SOF_2、SO_2、CF4、CO_2为特征组分,获得其在不同阶段的体积分数变化趋势。其中SO_2、SOF_2和SO_2F_2呈类"Logistic"曲线走势,表现出较好的一致性。提出用φ(SO_2F_2+SOF_2+SO_2)/φ(CO_2+CF_4)体积分数比、SO_2体积分数φ(SO_2)和(SO_2F_2+SOF_2+SO_2)体积分数变化率作为特征参量并分析其理化意义,在不同阶段特征参量变化显著,据此采用模糊分析法识别放电严重程度,建立了相应的模糊判断模型,将GIS绝缘劣化趋势划分为"正常阶段"、"劣化阶段"和"饱和阶段",分类结果与实验效果相符合。
To diagnose and evaluate the gas-insulated metal-enclosed switchgear（GIS） operating state by analyzing the SF_6 decomposition products, it is necessary to find out the appropriate characteristic parameters and establish an association rule between the decomposed gas composition and GIS insulation deterioration. Consequently, we experimentally studied the long-term sustainable discharge decomposition by needle-plate defect model in order to simulate the whole process of GIS insulation deterioration. SO_2F_2, SOF_2, SO_2, CO_2,and CF_4 were selected as feature components, their concentration trend curves at different stages were captured. SO_2, SOF_2, and SO_2F_2 showed a trend of ＂Logistic＂ curve, which displayed a satisfied consistence. Concentration of SO_2, rate of（SO_2F_2＋SOF_2＋SO_2） and the ratio of φ（SO_2F_2＋SOF_2＋SO_2）/φ（CO_2＋CF_4） were proposed as characteristic parameters and their physical significance were also explained. Three characteristic parameters change significantly at different stages.Moreover, fuzzy analysis was used to identify the severity of discharge, then corresponding fuzzy judgment model was established, and GIS insulation deterioration trend was divided into three states of ＂normal stage＂, ＂deterioration stage＂, and ＂saturation stage＂. The classification results are consistent with the experimental ones.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2016年第6期1834-1840,共7页
High Voltage Engineering
基金
国家自然科学基金(50677047)
中国南方电网科技项目(K-GX2011-019)
湖北省科学条件专项(2013BEC010)
湖北省科技支撑计划项目(2015BCE074)~~
关键词
SF6分解产物
GIS
绝缘劣化
特征组分
模糊分析
趋势划分
故障诊断
SF6 decomposition products
gas-insulated metal-enclosed switchgear
insulation deterioration
feature components
fuzzy analysis
trend division
fault diagnosis
