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

Zhaochuang Zhang, Jianhua Xia, Yuchuan Wen, Liting Weng, Zuofu Ma, Hekai Yang, Haobo Yang, Jinyao Dou, Jingang Wang and Pengcheng Zhao ()
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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

Energies, 2024, vol. 17, issue 18, 1-14

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.

Keywords: dynamic snake convolution; YOLOv8; slicing-aided hyper-inference algorithm; infrared image; inter-turn short circuit fault (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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