IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5221-d424550.html
   My bibliography  Save this article

Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information

Author

Listed:
  • Da Zhang

    (College of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)

  • Shuailin Chen

    (College of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)

Abstract

In order to achieve the noncontact detection of the contamination grade of insulators and to provide guidance for preventing the contamination flashover of insulators based on the pollution state, we propose a contamination grade recognition method based on the deep learning of ultraviolet discharge images using a sparse autoencoder (SAE) and a deep belief network (DBN). Under different humidity conditions, we filmed and preprocessed the ultraviolet discharge images of insulators at different contamination grades and we obtained the ultraviolet spot area sequence as original data for contamination grade recognition. A double-layer sparse autoencoder was used to extract sparse features that could characterize different contamination grades from the ultraviolet spot area sequence. Using the extracted features, a DBN composed of three layers of restricted Boltzmann machine was trained to provide contamination grade recognition. To verify the effectiveness of the method proposed in this paper, high-voltage experiments were performed on contaminated insulators at relative humidity levels of 80%, 85%, and 90%, and ultraviolet images were recorded. The proposed SAE–DBN method was used to identify the ultraviolet images of the insulators with different contamination grades. The recognition accuracy rates at the three humidity levels were 91.25%, 93.125%, and 92.5%. The experimental results showed that this method could accurately recognize the contamination grade of the insulator and provide guidance for the prevention of contamination flashover based on the pollution severity.

Suggested Citation

  • Da Zhang & Shuailin Chen, 2020. "Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information," Energies, MDPI, vol. 13(19), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5221-:d:424550
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5221/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/19/5221/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maurizio Albano & A. Manu Haddad & Nathan Bungay, 2019. "Is the Dry-Band Characteristic a Function of Pollution and Insulator Design?," Energies, MDPI, vol. 12(19), pages 1-15, September.
    2. Xinhan Qiao & Zhijin Zhang & Xingliang Jiang & Tian Liang, 2019. "Influence of DC Electric Fields on Pollution of HVDC Composite Insulator Short Samples with Different Environmental Parameters," Energies, MDPI, vol. 12(12), pages 1-12, June.
    3. Da Zhang & Fancui Meng, 2019. "Research on the Interrelation between Temperature Distribution and Dry Band on Wet Contaminated Insulators," Energies, MDPI, vol. 12(22), pages 1-14, November.
    4. Zhijin Zhang & Shenghuan Yang & Xingliang Jiang & Xinhan Qiao & Yingzhu Xiang & Dongdong Zhang, 2019. "DC Flashover Dynamic Model of Post Insulator under Non-Uniform Pollution between Windward and Leeward Sides," Energies, MDPI, vol. 12(12), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiazheng Lu & Jianping Hu & Zhen Fang & Xinhan Qiao & Zhijin Zhang, 2021. "Electric Field Distribution and AC Breakdown Characteristics of Polluted Novel Lightning Protection Insulator under Icing Conditions," Energies, MDPI, vol. 14(22), pages 1-11, November.
    2. Luqman Maraaba & Khaled Al-Soufi & Twaha Ssennoga & Azhar M. Memon & Muhammed Y. Worku & Luai M. Alhems, 2022. "Contamination Level Monitoring Techniques for High-Voltage Insulators: A Review," Energies, MDPI, vol. 15(20), pages 1-32, October.
    3. Dongdong Zhang & Hong Xu & Jin Liu & Chengshun Yang & Xiaoning Huang & Zhijin Zhang & Xingliang Jiang, 2021. "Research on the Non-Contact Pollution Monitoring Method of Composite Insulator Based on Space Electric Field," Energies, MDPI, vol. 14(8), pages 1-15, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Da Zhang & Fancui Meng, 2019. "Research on the Interrelation between Temperature Distribution and Dry Band on Wet Contaminated Insulators," Energies, MDPI, vol. 12(22), pages 1-14, November.
    2. Da Zhang & Shuailin Chen, 2021. "Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images," Energies, MDPI, vol. 14(20), pages 1-15, October.
    3. Ioannis F. Gonos & Issouf Fofana, 2020. "Special Issue “Selected Papers from the 2018 IEEE International Conference on High Voltage Engineering (ICHVE 2018)”," Energies, MDPI, vol. 13(18), pages 1-5, September.
    4. Guolin Yang & Yi Liao & Xingliang Jiang & Xiangshuai Han & Jiangyi Ding & Yu Chen & Xingbo Han & Zhijin Zhang, 2022. "Research on Value-Seeking Calculation Method of Icing Environmental Parameters Based on Four Rotating Cylinders Array," Energies, MDPI, vol. 15(19), pages 1-17, October.
    5. Marc-Alain Andoh & Christophe Volat, 2024. "Experimental Investigation of Parameters Influencing the Formation of Dry Bands and Related Electric Field," Energies, MDPI, vol. 17(10), pages 1-23, May.
    6. Jiahong He & Kang He & Bingtuan Gao, 2019. "Modeling of Dry Band Formation and Arcing Processes on the Polluted Composite Insulator Surface," Energies, MDPI, vol. 12(20), pages 1-20, October.
    7. Hao Yang & Haotian Zhang & Wen Cao & Xuanxiang Zhao & Ran Wen & Junping Zhao & Shengwu Tan & Pengchao Wang, 2021. "Optical Diagnostic Characterization of the Local Arc on Contaminated Insulation Surface at Low Pressure," Energies, MDPI, vol. 14(19), pages 1-11, September.
    8. Dongdong Zhang & Hong Xu & Jin Liu & Chengshun Yang & Xiaoning Huang & Zhijin Zhang & Xingliang Jiang, 2021. "Research on the Non-Contact Pollution Monitoring Method of Composite Insulator Based on Space Electric Field," Energies, MDPI, vol. 14(8), pages 1-15, April.
    9. Yanpeng Hao & Yifan Liao & Zhiqiang Kuang & Yijie Sun & Gaofeng Shang & Weixun Zhang & Guiyun Mao & Lin Yang & Fuzeng Zhang & Licheng Li, 2020. "Experimental Investigation on Influence of Shed Parameters on Surface Rainwater Characteristics of Large-Diameter Composite Post Insulators under Rain Conditions," Energies, MDPI, vol. 13(19), pages 1-16, September.
    10. Jinpeng Hao & Jinzhu Huang & Ziyi Fang & Xiao He & Qiang Wu & Xiaolong Gu & Yu Wang & Hong Wu, 2023. "Suppression Measures of Partial Discharge at Rod–Plate Connection in Composite Tower," Energies, MDPI, vol. 16(9), pages 1-17, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5221-:d:424550. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.