摘要
空气绝缘开关柜内部故障产生的O_(2)消耗和臭氧生成有明确指征特性,通过检测O_(2)、O_(3)等多元气体组合能实现故障早期识别。监控装置采用电化学传感器与紫外吸收光谱技术融合检测方案,结合贝叶斯网络算法建立故障概率推理模型。试验显示,该装置对局部放电故障检测精度达95%以上,且响应时间大幅缩短,为开关柜状态监测提供了有效的技术手段。
The oxygen consumption and ozone generation caused by internal faults in air insulated switchgear have clear indicative characteristics.Early identification of faults can be achieved by detecting the combination of multiple gases such as O_(2) and O_(3).The monitoring device adopts a fusion detection scheme of electrochemical sensors and ultraviolet absorption spectroscopy technology,combined with Bayesian network algorithm to establish a fault probability inference model.The experiment shows that the device has an accuracy of over 95% in detecting partial discharge faults,and the response time is shortened to less,providing an effective technical means for monitoring the status of switchgear.
作者
马海龙
赵小红
贾铭浩
李仕煊
张英卓
MA Hailong;ZHAO Xiaohong;JIA Minghao;LI Shixuan;ZHANG Yingzhuo
出处
《电力系统装备》
2025年第12期73-75,共3页
Electric Power System Equipment
关键词
气体分析
开关柜监控
氧气臭氧检测
故障诊断
贝叶斯网络
gas analysis
switchgear monitoring
oxygen and ozone detection
fault diagnosis
bayesian network
