Detection and Fault Diagnosis of Power Transmission Line in Infrared Image

被引:0
|
作者
He, Siyuan [1 ,2 ,3 ]
Yang, Dawei [1 ,2 ]
Li, Wentao [1 ]
Xia, Yong [4 ]
Tang, Yandong [1 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Liaoning, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Shenyang Inst Engn, Sch Automat, Shenyang 110136, Liaoning, Peoples R China
[4] Liaoning Elect Power Co Ltd, Benxi Power Supply Co, Benxi 117000, Liaoning, Peoples R China
来源
2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2015年
关键词
power transmission line; infrared image; object detection; fault diagnosis;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With surface aging and natural disasters, power transmission line often has broken strand fault which presents partial discharge and temperature rising. Infrared thermography is a new method to detect the temperature rising fault. Aiming at the temperature rising fault, a method based on infrared image for power transmission line detection and fault diagnosis is proposed. First, we convert infrared image into gray image, enhance the contrast, and eliminate the interference and noise. Next, cellular automaton method is used to segment the target and background in preprocessed image. Then, power transmission line is recognized and detected by using Hessian matrix. Finally, the fault is diagnosed based on temperature, and fault location is also determined. Experimental results indicate that this method can diagnose the temperature rising fault, and its accuracy is more than 90%. It has better robustness, accuracy and validity.
引用
收藏
页码:431 / 435
页数:5
相关论文
共 50 条
  • [21] Infrared image fault diagnosis based on dual-stream attention convolution network
    Lu, Dong
    Yang, Jing
    Ming, Lyu
    Zhang, Jie
    ENGINEERING RESEARCH EXPRESS, 2024, 6 (02):
  • [22] Study on Fault Detection for Photovoltaic Array Using Infrared Image
    Zhang, Ziyang
    Shi, Junsheng
    Zhang, Jingyu
    Wu, Xuqing
    Li, Ming
    RENEWABLE AND SUSTAINABLE ENERGY II, PTS 1-4, 2012, 512-515 : 280 - +
  • [23] A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing
    Liu, Hechao
    Liu, Wei
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2024, 33 (11)
  • [24] Automatic Fault Diagnosis of Infrared Insulator Images Based on Image Instance Segmentation and Temperature Analysis
    Wang, Bin
    Dong, Ming
    Ren, Ming
    Wu, Zhanyu
    Guo, Chenxi
    Zhuang, Tianxin
    Pischler, Oliver
    Xie, Jiacheng
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (08) : 5345 - 5355
  • [25] Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning
    Fang, Jian
    Yang, Fan
    Tong, Rui
    Yu, Qin
    Dai, Xiaofeng
    GLOBAL ENERGY INTERCONNECTION-CHINA, 2021, 4 (06): : 596 - 607
  • [26] Fault Diagnosis of UHVDC Transmission Line Based on Deep Neural Network
    Wang, Lei
    Zhao, Qingsheng
    Liang, Dingkang
    2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022), 2022, : 445 - 450
  • [27] Infrared Image Combined with CNN Based Fault Diagnosis for Rotating Machinery
    Liu, Ziwang
    Wang, Jinjiang
    Duan, Lixiang
    Shi, Tiefeng
    Fu, Qiang
    2017 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC), 2017, : 137 - 142
  • [28] Intelligent Fault Diagnosis of Transformer Based on Infrared Image and Mask RCNN
    Sun, Lintao
    Wang, Jianjun
    Liu, Jiangming
    Zhang, Xuanzhe
    Li, Wenyan
    Guo, Chuangxin
    2022 4TH INTERNATIONAL CONFERENCE ON SMART POWER & INTERNET ENERGY SYSTEMS, SPIES, 2022, : 1243 - 1247
  • [29] Research on fast fault diagnosis of transmission line based on artificial intelligence
    Yan S.
    Zhang W.
    Li H.
    Wang L.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2019, 47 (19): : 94 - 99
  • [30] EMD and ANN based intelligent fault diagnosis model for transmission line
    Malik, Hasmat
    Sharma, Rajneesh
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2017, 32 (04) : 3043 - 3050