基于红外图像识别的输电线路故障诊断方法

Transmission Line Fault Diagnosis Method Based on Infrared Image Recognition

基于红外图像研究了输电线路的故障诊断方法,首先采用LSD线段检测法提取红外图像中的导线,采用深度卷积神经网络提取红外图像中绝缘子,从而得到线路元件区域。在线路元件区域内进行温度和灰度分析提取发热点,并利用漫水填充算法对过热区域进行分割,提取骨架扫描点数、有效凸缺陷、引流线对缺陷类型进行识别。实验证明该算法有较高发热点定位准确率和缺陷类型识别准确率。

A fault diagnosis method of transmission line based on infrared image recognition is proposed in this paper. Firstly, LSD（Line Segment Detector） is used to extract the conductor and CNN （convolutional neural network） is used to extract insulator in the infrared image, both conductor areas and insulator areas are thought as line component areas. Heating pixels in component areas are obtained according to their temperature and gray value. Heating regions are segmented applying Flood Fill algorithm. Target accounting, skeleton scanning points, effective convex defects and lead wire are extracted to identify the type of the defects. Experiments show the effectiveness of defect points locating and defect types classifying.