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Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Author

Listed:
  • Lixiao Mu

    (State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China)

  • Xiaobing Xu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Zhanran Xia

    (State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China)

  • Bin Yang

    (State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China)

  • Haoran Guo

    (State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China)

  • Wenjun Zhou

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Chengke Zhou

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
    School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

Abstract

Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

Suggested Citation

  • Lixiao Mu & Xiaobing Xu & Zhanran Xia & Bin Yang & Haoran Guo & Wenjun Zhou & Chengke Zhou, 2021. "Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories," Energies, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4316-:d:596344
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    References listed on IDEAS

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    1. Irfan Ullah & Rehan Ullah Khan & Fan Yang & Lunchakorn Wuttisittikulkij, 2020. "Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment," Energies, MDPI, vol. 13(2), pages 1-17, January.
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    3. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.
    4. Lan Xiong & Yonghui Chen & Yang Jiao & Jie Wang & Xiao Hu, 2019. "Study on the Effect of Cable Group Laying Mode on Temperature Field Distribution and Cable Ampacity," Energies, MDPI, vol. 12(17), pages 1-15, September.
    5. Irfan Ullah & Fan Yang & Rehanullah Khan & Ling Liu & Haisheng Yang & Bing Gao & Kai Sun, 2017. "Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach," Energies, MDPI, vol. 10(12), pages 1-13, December.
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    Cited by:

    1. Songyuan Li & Pengxian Song & Zhanpeng Wei & Xu Li & Qinghua Tang & Zhengzheng Meng & Ji Li & Songtao Liu & Yuhuai Wang & Jin Li, 2022. "Partial Discharge Detection and Defect Location Method in GIS Cable Terminal," Energies, MDPI, vol. 16(1), pages 1-10, December.
    2. Tanachai Somsak & Thanapong Suwanasri & Cattareeya Suwanasri, 2021. "Lifetime Estimation Based Health Index and Conditional Factor for Underground Cable System," Energies, MDPI, vol. 14(23), pages 1-15, December.

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