基于改进区域全卷积网络的高压引线接头红外图像特征分析的在线故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.201136

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Abstract With the continuous development of smart grid construction, massive infrared images have increased dramatically, while traditional infrared fault detection relies on manual inspection or manual extraction of features, low detection efficiency and great dependence on personnel experience. In order to realize the efficient and intelligent detection of infrared images and ensure the safe operation of the grid, this paper constructs an online fault diagnosis system based on infrared feature analysis, and proposes to improve the recognition performance of small targets by improving the feature extraction network of high-voltage lead connectors infrared images. Then, the region-based fully convolutional networks (R-FCN) is used to identify the location and operational status of the faulty area, and the operating state of the faulty area is secondarily diagnosed using OpenCV to further reduce the false alarm rate. Finally, through testing and analysis, the average accuracy of the improved R-FCN network for high-voltage lead connectors infrared image fault diagnosis reached 80.76%, which is 8.43% higher than the original R-FCN network.

Key words： Fault diagnosis high-voltage lead connectors infrared image region-based fully convolutional networks residual network

Received: 30 August 2020

PACS:	TM938.6
	TP391.4

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	Articles by authors
	Xu Qiwei
	Huang Hong
	Zhang Xuefeng
	Zhou Chuan
	Wu Shaopeng

Cite this article:

Xu Qiwei,Huang Hong,Zhang Xuefeng等. Online Fault Diagnosis Method for Infrared Image Feature Analysis of High-Voltage Lead Connectors Based on Improved R-FCN[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1380-1388.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201136 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I7/1380

[1] 徐彪, 尹项根, 张哲, 等. 电网故障诊断的分阶段解析模型[J]. 电工技术学报, 2018, 33(17): 4113-4122.
Xu Biao, Yin Xianggen, Zhang Zhe, et al.A staged analytical model for power system fault diagnosis[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 4113-4122.
[2] 杨志淳, 沈煜, 杨帆, 等. 考虑多元因素态势演变的配电变压器迁移学习故障诊断模型[J]. 电工技术学报, 2019, 34(7): 1505-1515.
Yang Zhichun, Shen Yu, Yang Fan, et al.A transfer learning fault diagnosis model of distribution transformer considering multi-factor situation evolution[J]. Transactions of China Electrotechnical Society, 2019, 34(7): 1505-1515.
[3] 徐春华, 陈克绪, 马建, 等. 基于深度置信网络的电力负荷识别[J]. 电工技术学报, 2019, 34(19): 4135-4142.
Xu Chunhua, Chen Kexu, Ma Jian, et al.Recognition of power loads based on deep belief network[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4135-4142.
[4] 朱煜峰, 许永鹏, 陈孝信, 等. 基于卷积神经网络的直流XLPE电缆局部放电模式识别技术[J]. 电工技术学报, 2020, 35(3): 659-668.
Zhu Yufeng, Xu Yongpeng, Chen Xiaoxin, et al.Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 659-668.
[5] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[6] 吴迪, 汤小兵, 李鹏, 等. 基于深度神经网络的变电站继电保护装置状态监测技术[J]. 电力系统保护与控制, 2020, 48(5): 81-85.
Wu Di, Tang Xiaobing, Li Peng, et al.State monitoring technology of substation relay protection device based on deep neural network[J]. Power System Protection and Control, 2020, 48(5): 81-85.
[7] 张倩, 王建平, 李帷韬. 基于反馈机制的卷积神经网络绝缘子状态检测方法[J]. 电工技术学报, 2019, 34(16): 3311-3321.
Zhang Qian, Wang Jianping, Li Weitao.Insulator sta- te detection of convolutional neural networks based on feedback mechanism[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3311-3321.
[8] Jiang Anfeng, Yan Nannan, Wang Fa, et al.Visible image recognition of power transformer equipment based on mask R-CNN[C]//IEEE Sustainable Power and Energy Conference, Beijing, China, 2019: 657-661.
[9] Ren Shaoping, He Kaiming, Girshick R, et al.Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 2017, 39(6): 1137-1149.
[10] He Kaiming, Gkioxari G, Dollár P, et al.Mask R-CNN[C]//International Conference on Computer Vision, Venice, Italy, 2017: 2961-2969.
[11] Redmon J, Divvala S, Girshick R, et al.You only look once:unified real-time object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779-788.
[12] Redmon J, Farhadi A.YOLO9000: better, faster, stronger[C]//IEEE Conference on Computer Vision & Pattern Recognition, Honolulu, USA, 2017: 6517-6525.
[13] Redmon J, Farhadi A.Yolov3: an incremental improvement[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1-4.
[14] Liu Wei, Anguelov D, Erhan D, et al.SSD: single shot multibox detector[C]//European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21-37.
[15] Wang Bin, Dong Ming, Ren Ming, et al.Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(8): 5345-5355.
[16] 王万国, 田兵, 刘越. 基于RCNN的无人机巡检图像电力小部件识别研究[J]. 地球信息科学学报, 2017, 19(2): 256-263.
Wang Wanguo, Tian Bing, Liu Yue.Research on power component identification of UAV inspection image based on RCNN[J]. Journal of Earth Information Science, 2017, 19(2): 256-263.
[17] Xiao Lijun, Mao Qiang, Lan Penghao, et al.A fault diagnosis method of insulator string based on infrared image feature extraction and probabilistic neural network[C]//International Conference on Intelligent Computation Technology & Automation, Changsha, 2017: 80-85.
[18] Dai Jifeng, Li Yi, He Kaiming, et al.R-FCN: object detection via region-based fully convolutional networks[C]//Proceedings of the 2016 Advances in Neural Information Processing Systems, Barcelona, Spain, 2016: 208-217.
[19] Bodla N, Singh B, Chellappa R, et al.Soft-NMS-improving object detection with one line of code[C]// 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 5562-5570.
[20] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al.Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
[21] Liu Jian, Zhao Mingrui, Guo Xifeng.A fruit detection algorithm based on R-FCN in natural scene[C]//2020 Chinese Control And Decision Conference (CCDC), Hefei, China, 2020: 487-492
[22] Shelhamer E, Long J, Darrell T.Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4): 640-651.