【摘要】 碳纤维复合芯导线能够大幅度提高输电线路输送容量,但由于不耐弯折等原因导致多发断线,严重危害线路运行安全。为实现对长距离输电线路进行在线缺陷检测,本文提出了碳纤维复合芯导线缺陷自动检测方案。该方案首先对碳纤维复合芯导线的X射线图像进行图像成像标准化,进行了导线弯曲补偿和亮度一致化矫正,提高了数据的一致性,为导线自动化分析提供条件;然后利用深度卷积神经网络技术进行缺陷检测。以双层铝股线类型的碳纤维复合芯导线X射线图像作为研究对象进行实验,结果证明该方案可快速自动识别导线缺陷。更多还原

【Abstract】 Carbon fiber composite core wires can greatly increase transmission capacity of transmission lines. However,many breaks are caused due to the bending resistance and other reasons,which seriously endangers the safety of line operation. In order to realize on-line defect detection for long-distance transmission lines,this paper proposes an automatic defect detection scheme for carbon fiber composite core conductors. Firstly,the X-ray image of carbon fiber composite core conductors is standardized. Then,data consistency is improved to provide conditions for automatic analysis of conductors after bending compensation and brightness normalization. Finally,the deep convolution neural network technology is used for defect detection. Experiments on aluminum conductor composite core(ACCC) show that the scheme can quickly and automatically identify the defects of carbon fiber composite core conductors.更多还原