IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i18p4559-d1476238.html
   My bibliography  Save this article

Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm

Author

Listed:
  • Zhaochuang Zhang

    (Xiluodu Hydropower Plant, Zhaotong 657300, China)

  • Jianhua Xia

    (Xiluodu Hydropower Plant, Zhaotong 657300, China)

  • Yuchuan Wen

    (Three Gorges Ecological Environment Co., Ltd., Zhaotong 657300, China)

  • Liting Weng

    (Xiluodu Hydropower Plant, Zhaotong 657300, China)

  • Zuofu Ma

    (Xiluodu Hydropower Plant, Zhaotong 657300, China)

  • Hekai Yang

    (State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Haobo Yang

    (State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Jinyao Dou

    (State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Jingang Wang

    (State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Pengcheng Zhao

    (State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

Inter-Turn Short Circuit (ITSC) faults do not necessarily produce high temperatures but exhibit distinct heat distribution and characteristics. This paper proposes a novel fault diagnosis and identification scheme utilizing an improved You Look Only Once Vision 8 (YOLOv8) algorithm, enhanced with an infrared image slicing-aided hyper-inference (SAHI) technique, to automatically detect ITSC fault trajectories in dry-type transformers. The infrared image acquisition system gathers data on ITSC fault trajectories and captures images with varying contrast to enhance the robustness of the recognition model. Given that the fault trajectory constitutes a small portion of the overall infrared image and is subject to significant background interference, traditional recognition algorithms often misjudge or omit faults. To address this, a YOLOv8-based visual detection method incorporating Dynamic Snake Convolution (DSConv) and the Slicing-Aided Hyper-Inference algorithm is proposed. This method aims to improve recognition precision and accuracy for small targets in complex backgrounds, facilitating accurate detection of ITSC faults in dry-type transformers. Comparative tests with the YOLOv8 model, Fast Region-based Convolutional Neural Networks (Fast-RCNNs), and Residual Neural Networks (Retina-Nets) demonstrate that the enhancements significantly improve model convergence speed and fault trajectory detection accuracy. The approach offers valuable insights for advancing infrared image diagnostic technology in electrical power equipment.

Suggested Citation

  • Zhaochuang Zhang & Jianhua Xia & Yuchuan Wen & Liting Weng & Zuofu Ma & Hekai Yang & Haobo Yang & Jinyao Dou & Jingang Wang & Pengcheng Zhao, 2024. "Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm," Energies, MDPI, vol. 17(18), pages 1-14, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4559-:d:1476238
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/18/4559/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/18/4559/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Pengcheng & Wang, Jingang & Xia, Haiting & He, Wei, 2024. "A novel industrial magnetically enhanced hydrogen production electrolyzer and effect of magnetic field configuration," Applied Energy, Elsevier, vol. 367(C).
    Full references (including those not matched with items on IDEAS)

    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. Vincent Henkel & Lukas Peter Wagner & Felix Gehlhoff & Alexander Fay, 2024. "Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers," Energies, MDPI, vol. 17(17), pages 1-17, September.
    2. Chukwuma Ogbonnaya & Grace Hegarthy, 2024. "Manufacturing Strategies for a Family of Integrated Photovoltaic-Fuel Cell Systems," Energies, MDPI, vol. 17(19), pages 1-16, September.
    3. Liuzhou Zhou & Zhen Yao & Ke Sun & Zhongliang Tian & Jie Li & Qifan Zhong, 2024. "Methodological Review of Methods and Technology for Utilization of Spent Carbon Cathode in Aluminum Electrolysis," Energies, MDPI, vol. 17(19), pages 1-26, September.
    4. Luciano T. Barbosa & Samuel D. Vasconcelos & Pedro A. C. Rosas & José F. C. Castro & Douglas C. P. Barbosa, 2024. "Assessment of Green Hydrogen as Energy Supply Alternative for Isolated Power Systems and Microgrids," Energies, MDPI, vol. 17(19), pages 1-28, September.

    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:17:y:2024:i:18:p:4559-:d:1476238. 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.