基于Faster R-CNN图像处理的变电站异常设备红外检测方法

Infrared detection method based on Faster R-CNN image processing for abnormal equipment in substation

针对智能变电站内一次设备红外监测图像分析与处理智能化较低的问题,提出了一种基于Faster R-CNN的变电站异常设备红外检测图谱分析方法,并实现了变电站故障设备的智能识别和原因分析。将远程终端单元所采集到的红外图像进行预处理,并对图中的变电站设备进行识别;采用大津算法结合图像灰度值的特殊性对图像进行分割与图像配准;Faster R-CNN则用于对比判断设备是否处于故障状态并分析原因。实验测试结果表明,所提方法对于多种故障设备的识别准确率均在90%以上,具有较优的鲁棒性。

Aiming at the problem of low intelligence in the analysis and processing of infrared monitoring images for primary equipment within intelligent substations,a Faster R-CNN-based infrared detection image analysis method for abnormal equipment within substations was proposed to realize the intelligent identification and cause analysis of faulty equipment in substations.The infrared images collected by the remote terminal unit(RTU)were preprocessed,and the substation equipment in images was identified.The OSTU algorithm in combination with the image gray value was used to rectify and segment image.Faster R-CNN was used to distinguish whether the equipment was in fault state and analyze the cause.Experimental results show that the as-proposed method has a recognition accuracy of more than 90% for a variety of faulty equipment,and has a comparatively excellent robustness.