Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

SUBSCRIPTION MANAGEMENT

发行征订

首页 >> 发行征订 >> 征订方式

基于Faster R-CNN模型的绝缘子故障检测

来源:电工电气发布时间:2020-04-18 14:18浏览次数:926
基于Faster R-CNN模型的绝缘子故障检测
 
陈俊杰,叶东华,产焰萍,陈凌睿
(国网漳州供电公司,福建 漳州 363000)
 
    摘 要:绝缘子是电力系统中用来支撑电线和电气隔离的重要器件,对输配电线路绝缘状态的在线检测意义重大。针对现阶段人工判别航拍图像的不足,提出基于Faster R-CNN的绝缘子图像故障检测方案,阐述了卷积神经网络特征提取的原理,构建基于Faster R-CNN的绝缘子检测模型,利用无人机航拍的绝缘子图像及故障样本,对检测模型加以训练与测试,分别进行绝缘子分类检测实验和绝缘子故障定位实验。实验结果表明,所提出的绝缘子故障检测方法能够准确对绝缘子进行检测与分类,并定位出故障位置,且达到实时性要求。
    关键词:绝缘子检测;故障定位;卷积神经网络;图像检测;深度学习
    中图分类号:TM216;TM855     文献标识码:A     文章编号:1007-3175(2020)04-0056-05
 
Insulator Fault Detection Based on Faster R-CNN
 
CHEN Jun-jie, YE Dong-hua, CHAN Yan-ping, CHEN Ling-rui
(State Grid Zhangzhou Power Supply Company, Zhangzhou 363000, China)
 
    Abstract: Insulators are important devices used to support electrical wires and electrical isolation in power systems, and are of great significance for online test of the insulation status of transmission and distribution lines. In this paper, in view of the shortcomings of manually discriminating aerial images at this stage, an insulator image fault detection scheme based on Faster R-CNN is proposed, and the principle of feature extraction for convolutional neural networks is described, and an insulator detection model based on Faster R-CNN is constructed. Utilizing aerial insulator images and fault samples of aerial drones, the detection model is trained and tested, and insulator classification detection experiments and insulator fault location experiments are performed respectively. Experimental results show that the proposed insulator fault detection method can accurately detect and classify insulators, locate the fault location, and meet the real-time requirements.
    Key words: insulator detection; fault location; convolutional neural network; image detection; deep learning
 
参考文献
[1] 仝卫国,苑津莎,李宝树. 图像处理技术在直升机巡检输电线路中的应用综述[J]. 电网技术,2010,34(12):204-208.
[2] 朱虎,李卫国,林治. 绝缘子检测方法的现状与发展[J]. 电瓷避雷器,2006(6):13-17.
[3] PARK K C, MOTAI Y, YOON J R. Acoustic Fault Detection Technique for High Power Insulators[J].IEEE Transactions on Industrial Electronics,2017,64(12):9699-9708.
[4] 黄霄宁,张真良. 直升机巡检航拍图像中绝缘子图像的提取算法[J]. 电网技术,2010,34(1):194-197.
[5] 徐耀良,张少成,杨宁,等. 航拍图像中绝缘子的提取算法[J]. 上海电力学院学报,2011,27(5):515-518.
[6] 赵振兵,金思新,刘亚春. 基于NSCT的航拍绝缘子图像边缘提取方法[J]. 仪器仪表学报,2012,33(9):2045-2052.
[7] 李卫国,叶高生,黄锋,等. 基于改进MPEG-7纹理特征的绝缘子图像识别[J]. 高压电器,2010,46(10):65-68.
[8] OBERWEGER M, WENDEL A, BISCHOF H.Visual recognition and fault detection for power line insulators[C]//19th Computer Vision Winter Workshop,2014.
[9] ZHANG Xinye, AN Jubai, CHEN Fangming.A method of insulator fault detection from airborne image[C]//2010 Second WRI Global Congress on Intelligent Systems,2010.
[10] 姜浩然,金立军,闫书佳. 航拍图像中绝缘子的识别与故障诊断[ J ] . 机电工程,2015,32(2):274-278.
[11] KRIZHEVSKY Alex, SUTSKEVER I, HINTON G.Imagenet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems,2012.
[12] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:Towards Real-Time Oobject Detection with Region Proposal Networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence,2016.
[13] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//Computer Vision-ECCV 2014:13th European Conference,2014.
[14] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//3rd International Conferenceon on Learning Representations,2015.