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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
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    References listed on IDEAS

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    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).
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