首页|基于神经网络模型的带电设备故障红外诊断的研究

基于神经网络模型的带电设备故障红外诊断的研究

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

infrared detectionfault diagnosisneural network

周迅、石惠承、莫加辉、陈湘如

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国网嘉兴供电公司,浙江 嘉兴 314000

红外检测 故障诊断 神经网络

2024

电气开关
沈阳电气传动研究所

电气开关

影响因子:0.281
ISSN:1004-289X
年,卷(期):2024.62(6)