摘要
针对视觉观察、红外测温、泄漏工频电流和直流分量等方法无法快速发现10 kV架空线路上的氧化锌避雷器(metal oxide arrester, MOA)内部受潮及绝缘缺陷问题,文中提出基于K-means智能识别缺陷类型的方法及原理,分别制作10 kV MOA内部受潮、阀片裂纹等缺陷实验样品,在无局放升压装置加压至额定电压10 kV的条件下,使用高频电流传感器(high frequency current sensor, HFCT)采集局部放电原始数据,提取特征量,建立对应的缺陷类型数据库;通过在带电运行现场不同测试点测得不同10 kV MOA亚稳态下的13组数据,结果表明该方法对10 kV MOA内部绝缘缺陷、受潮缺陷能够准确识别,验证了基于K-means高频局放10 kV MOA快速带电检测方法的实用性,具有较高的经济效应和社会使用价值。
In response to the inability of visual observation,infrared temperature measurement,leakage power frequency current,and DC component methods to quickly detect the internal moisture and insulation defects of metal oxide arrester(MOA)on 10 kV overhead lines,this paper proposes a method and principle based on K-means intelligent identification of defect types.Experimental samples of defects such as internal moisture and valve plate cracks in 10 kV MOA are made separately.Under the condition of pressurizing to the rated voltage of 10 kV without a partial discharge boosting device,a high frequency current sensor(HFCT)is used to collect raw partial discharge data,extract feature quantities,and establish a corresponding defect type database.By measuring 13 sets of data under different sub steady states of 10 kV MOA at different test points during live operation,the results show that this method can accurately identify internal insulation defects and moisture defects of 10 kV MOA,verifying the practicality of the K-means based high-frequency partial discharge 10 kV MOA fast live detection method,which has high economic and social value.
作者
李春锋
方春华
侯轩达
董锋
LI Chunfeng;FANG Chunhua;HOU Xuanda;DONG Feng(Henan Tiantong Electric Power Co.,Ltd.,Pingdingshan 467000,Henan,China;China Three Gorges University,Yichang 443002,Hubei,China;Saga University,Saga 840-8502,Japan;Wuhan Kangdian Electric Co.,Ltd.,Wuhan 430070,China)
出处
《电测与仪表》
北大核心
2024年第7期191-196,共6页
Electrical Measurement & Instrumentation
基金
中煤协会科研基金资助项目(MTKJ22-217)。
