基于神经网络模型的带电设备故障红外诊断的研究  

Research on Infrared Diagnosis of Live Device Fault Based on Neural Network Model

作　　者：周迅 石惠承 莫加辉 陈湘如 ZHOU Xun;SHI Hui-cheng;MO Jia-hui;CHEN Xiang-ru(State Grid Jiaxing Power Supply Company,Jiaxing 314000,China)

机构地区：[1]国网嘉兴供电公司,浙江嘉兴314000

出　　处：《电气开关》2024年第6期88-90,94,共4页Electric Switchgear

摘　　要：近年来,红外热成像等带电检测技术在电力系统中得到了普遍应用,并随着技术的进一步成熟,已经成为在不停电条件下精准发现设备过热等缺陷的有力手段。利用红外热成像技术对高压电气设备进行带电检测,有利于及时发现设备发热部位及时判别发热原因,减少非计划停电,为检修决策提供依据,进一步提升电力设备安全管控水平,保障更优质可靠的电力供应,更好地服务经济社会发展和人民美好生活需要。Recently,infrared thermal imaging and other live detection technologies have been widely used in power systems.With the further maturity of the technology,it has become a powerful means to accurately find the defects such as equipment overheating under the condition of no power cut.The use of infrared thermal imaging technology for live detection of high-voltage electrical equipment is conducive to find the heating parts of the equipment timely,identify the causes of heating timely,reduce unplanned power outages,provide a basis for maintenance decisions,further improve the safety control level of power equipment,ensure a higher quality and reliable power supply,and better serve the economic and social development and the needs of people′s better life.

关 键 词：红外检测 故障诊断 神经网络 

分 类 号：TM93[电气工程—电力电子与电力传动]