深度学习在输变电设备故障状态检测中的应用研究  

Research on the Application of Deep Learning in Fault State Detection of Power Transmission and Transformation Equipment

作　　者：樊慧文 FAN Huiwen(Gansu Construction Investment Green Building Materials Industry Development Group Co.,Ltd.,Lanzhou 730000,China)

机构地区：[1]甘肃建投绿色建材产业发展集团公司,甘肃兰州730000

出　　处：《电工技术》2025年第2期95-97,101,共4页Electric Engineering

摘　　要：输变电站设备在长期运行中受表面老化、自然灾害等多种因素的影响,极易出现各种故障,且伴随着局部异常发热现象,因此开展深度学习在输变电设备故障状态检测中的应用研究。通过四旋翼无人机搭载红外热像仪采集输变电设备红外图像,并对采集到的图像做灰度化、去噪、增强等预处理;基于深度学习理论构建卷积神经网络模型,输入预处理后的红外图像,识别图像中局部异常发热目标,实现输变电设备故障状态检测。实验结果表明,应用设计的卷积神经网络模型可准确检测出输变电设备红外图像上局部异常发热故障目标,验证了深度学习在输变电设备故障状态检测中的实际应用效果良好。The equipment of transmission and substation is susceptible to various faults and local abnormal heating phenomena due to various factors such as surface aging and natural disasters during long-term operation.Therefore,this study focuses on the application of deep learning in fault state detection of power transmission and transformation equipment.The article uses a quadcopter drone equipped with an infrared thermal imager to capture infrared images of power transmission and transformation equipment,and performs preprocessing such as grayscale,denoising,and enhancement on the collected images.Based on deep learning theory,a convolutional neural network model is constructed to input the preprocessed infrared images,identify local abnormal heating targets in the images,and achieve fault state detection of power transmission and transformation equipment.The experimental results show that the designed convolutional neural network model can accurately detect local abnormal heating fault targets in infrared images of power transmission and transformation equipment,verifying the practical application effect of deep learning in fault state detection of power transmission and transformation equipment.

关 键 词：深度学习 输变电设备 设备故障 故障状态 故障检测 

分 类 号：TM855[电气工程—高电压与绝缘技术]