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Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems

Author

Listed:
  • Xianglun Nie

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Jing Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yu He

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Wenjian Luo

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Tingyun Gu

    (Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China)

  • Bowen Li

    (Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China)

  • Xiangxie Hu

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

Abstract

Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods.

Suggested Citation

  • Xianglun Nie & Jing Zhang & Yu He & Wenjian Luo & Tingyun Gu & Bowen Li & Xiangxie Hu, 2023. "Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems," Energies, MDPI, vol. 16(7), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2937-:d:1104933
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    References listed on IDEAS

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    1. Tuyen Nguyen-Duc & Thinh Le-Viet & Duong Nguyen-Dang & Tung Dao-Quang & Minh Bui-Quang, 2022. "Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network," Energies, MDPI, vol. 15(17), pages 1-21, August.
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