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A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

Author

Listed:
  • Yin Chen

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

  • Zhenli Tang

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Xiaofeng Weng

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Min He

    (Fujian YILI Information Technology Co., Ltd., Fuzhou 350001, China)

  • Sheng Zhou

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China
    State Grid Fujian Electric Power Company Limited, Fuzhou 350001, China)

  • Ziqiang Liu

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

  • Tao Jin

    (Department of Electrical Engineering, Fuzhou University, Fuzhou 350116, China)

Abstract

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.

Suggested Citation

  • Yin Chen & Zhenli Tang & Xiaofeng Weng & Min He & Sheng Zhou & Ziqiang Liu & Tao Jin, 2024. "A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM," Energies, MDPI, vol. 17(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:404-:d:1318627
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    References listed on IDEAS

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    1. Tito G. Amaral & Vitor Fernão Pires & Armando J. Pires, 2021. "Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA," Energies, MDPI, vol. 14(21), pages 1-18, November.
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