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Research on Cross-Circuitry Fault Identification Method for AC/DC Transmission System Based on Blind Signal Separation Algorithm

Author

Listed:
  • Yan Tao

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Xiangping Kong

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Chenqing Wang

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Junchao Zheng

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Zijun Bin

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Jinjiao Lin

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

  • Sudi Xu

    (Electric Power Science Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China)

Abstract

The AC/DC transmission system is an important component of the power system, and the cross-circuitry Fault diagnosis of the AC/DC transmission system plays an important role in ensuring the normal operation of power equipment and personal safety. The traditional AC/DC transmission detection methods have the characteristics of complex detection processes and low fault line identification rates. Aiming at such problems, this paper proposes a new method of cross-circuitry Fault diagnosis based on the AC/DC transmission system based on a blind signal separation algorithm. Firstly, the method takes the typical cross-circuitry Fault scenario as an example to construct the topology diagram of the AC/DC power transmission system. Then, the electrical signals of the AC system and the DC system of the AC/DC power transmission system are collected, and the collected signals are extracted by the blind signal separation algorithm. Then, aiming at the cross-circuitry Fault problem of the DC system, the electrical quantities of the positive and negative poles on the rectifier side and the inverter side are collected, and the characteristics of the electrical quantities are analyzed by wavelet to determine the fault. At the same time, aiming at the problem of the cross-circuitry Fault of the AC system, three fault types of cross-circuitry Fault, ground fault, and intact fault are set up, and the electrical quantities of A, B, and C are collected on the same side, and the characteristics of three-phase electrical quantities are analyzed by wavelet. Finally, the cross-circuitry Fault judgment interval of the AC/DC system is set as the basis of fault judgment. After experimental verification, the relative error of the model is 1.4683%. The crossline fault identification method of the AC/DC transmission system based on the blind source separation algorithm proposed in this paper can accurately identify the crossline fault location and identify the fault type. It also provides theoretical and experimental support for power system maintenance personnel to maintain equipment.

Suggested Citation

  • Yan Tao & Xiangping Kong & Chenqing Wang & Junchao Zheng & Zijun Bin & Jinjiao Lin & Sudi Xu, 2025. "Research on Cross-Circuitry Fault Identification Method for AC/DC Transmission System Based on Blind Signal Separation Algorithm," Energies, MDPI, vol. 18(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1395-:d:1610374
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    References listed on IDEAS

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    1. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    2. Paul Arévalo & Francisco Jurado, 2024. "Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids," Energies, MDPI, vol. 17(17), pages 1-22, September.
    3. Li, Shuhan & Li, Zhigang & Shahidehpour, Mohammad & Huang, Wenjing & Zheng, J.H., 2024. "Dispatchable region for distributed renewable energy generation in reconfigurable AC–DC distribution networks with soft open points," Applied Energy, Elsevier, vol. 371(C).
    4. Guo Wang & Yibin Wang & Yongzhi Min & Wu Lei, 2022. "Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis," Energies, MDPI, vol. 15(16), pages 1-15, August.
    5. Yubo Wang & Chao Huo & Fei Xu & Libin Zheng & Ling Hao, 2025. "Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling," Energies, MDPI, vol. 18(1), pages 1-21, January.
    6. Yuanyang Xia & Xiaocong Li & Zhili Liu & Yuan Liu, 2023. "Application of Underdetermined Blind Source Separation Algorithm on the Low-Frequency Oscillation in Power Systems," Energies, MDPI, vol. 16(8), pages 1-18, April.
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