基于图像特征的风电场电气设备故障红外图像目标识别  

Target Recognition of Wind Farm Electrical Equipment Fault in Infrared Image Based on Image Features

作　　者：曾兴旺 ZENG Xingwang(Fujian Datang International New Energy Co.,Ltd.,Xiamen 361000,China)

机构地区：[1]福建大唐国际新能源有限公司,福建厦门361000

出　　处：《电工技术》2025年第4期160-162,共3页Electric Engineering

摘　　要：为实现对风电场电气设备故障红外图像目标的准确识别,为电气设备运行维护提供更可靠依据,引入图像特征技术,开展风电场电气设备故障红外图像目标识别研究。采用最大类间方差阈值分割法,增强风电场电气设备故障红外图像,结合图像特征,提取故障区域,最后实现电气设备故障诊断与目标识别。将所提识别方法应用于实际,可以精确识别各电气设备接头是否存在过热现象、是否因松动或损坏而温度异常,具有极大应用价值。In order to achieve accurate identification of fault infrared image targets of electrical equipment in wind farms and provide more reliable basis for the operation and maintenance of electrical equipment,image feature technology was introduced to carry out research on fault infrared image target recognition of electrical equipment in wind farms.The maximum inter-class variance threshold segmentation method is used to enhance the infrared image of electrical equipment fault in wind farm.Fault regions are extracted by combining image features.Finally,the fault diagnosis and target identification of electrical equipment are realized.The application of the new identification method can accurately identify whether the electrical equipment joints are overheating,whether the temperature is abnormal due to loosening or damage,which has great application value.

关 键 词：图像特征 电气设备 红外图像 风电场 

分 类 号：TN219[电子电信—物理电子学]