摘要
为更好地保障输电线路设备的电子汇集能力,并实现对高压电网络结构的合理性规划,基于图像识别技术设计了一种输电线路设备缺陷识别应用系统。联合输电循环回路,妥善连接任务管理模块与电量基站控制设备,完成对系统硬件执行环境的搭建。在此基础上,设计卷积神经网络,借助栈式自动编码器结构体,对RBM识别节点进行训练处理,完成系统软件执行环境的搭建。将软硬件结构结合,实现基于图像识别输电线路设备缺陷识别系统的顺利应用。对比实验结果表明,与传统的深度学习型缺陷识别系统相比,该文系统可同时检测的传输电子量更多,且既定时间节点处的识别精准度也更高,能够较好地实现对输电线路设备电子汇集能力的保护。
In order to better guarantee the electronic gathering ability of transmission line equipment and realize the rational planning of the high-voltage network structure,an application system of transmission line equipment defect recognition based on image recognition technology is designed.The combined transmission cycle loop can properly connect the task management module and the power base station control equipment to complete the construction of the system hardware execution environment.On this basis,a convolutional neural network is designed.With the help of the stack automatic encoder structure,the RBM recognition nodes are trained and processed to complete the construction of the system software execution environment.The software and hardware structures are combined to realize the smooth application of the transmission line equipment defect recognition system based on image recognition. The experimental results show that compared with the traditional deep learning defect recognition system,the system in this paper can simultaneously detect more transmitted electrons,and the recognition accuracy at the fixed time node is also higher,which can better realize the protection of the electronic collection ability of transmission line equipment.
作者
翟瑞聪
林俊省
郑桦
ZHAI Ruicong;LIN Junsheng;ZHENG Hua(Machine Patrol Management Center of Guangdong Power Grid Co.,Ltd.,Guangzhou 510000,China;Digital Grid Research Institute Co.,Ltd.,China Southern Power Grid,Guangzhou 510000,China)
出处
《电子设计工程》
2022年第6期161-164,169,共5页
Electronic Design Engineering
关键词
图像识别
缺陷识别
循环回路
卷积神经网络
自动编码器
RBM节点
image recognition
defect recognition
circulating loop
convolutional neural network
automatic encoder
RBM node
