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An Improved Commutation Prediction Algorithm to Mitigate Commutation Failure in High Voltage Direct Current

Author

Listed:
  • Xinnian Li

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Fengqi Li

    (State Grid Operation Company, Beijing 100052, China)

  • Shuyong Chen

    (State Key Laboratory of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China)

  • Yanan Li

    (State Power Economic Research Institute, Beijing 102209, China)

  • Qiang Zou

    (NR Electric Co., Ltd., Nanjing 211102, China)

  • Ziping Wu

    (Information Trust Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA)

  • Shaobo Lin

    (State Key Laboratory of Power Grid Safety and Energy Conservation (China Electric Power Research Institute), Beijing 100192, China)

Abstract

Commutation failure is a common fault for line-commutated converters in the inverter. To reduce the possibility of commutation failure, many prediction algorithms based on alternating current (AC) voltage detection have already been implemented in high voltage direct current (HVDC) control and protection systems. Nevertheless, there are currently no effective methods to prevent commutation failure due to transformer excitation surge current. In this paper, an improved commutation failure prediction algorithm based on the harmonic characteristics of the converter bus voltage during transformer charging is proposed. Meanwhile, a sliding-window iterative algorithm of discrete Fourier transformation (DFT) is developed for detecting the voltage harmonic in real time. This method is proved to be an effective solution, which prevents commutation failure in cases of excitation surge current, through experimental analysis. This method is already implemented into TianShan-ZhongZhou (TianZhong) ± 800 kV ultra high voltage direct current (UHVDC) system.

Suggested Citation

  • Xinnian Li & Fengqi Li & Shuyong Chen & Yanan Li & Qiang Zou & Ziping Wu & Shaobo Lin, 2017. "An Improved Commutation Prediction Algorithm to Mitigate Commutation Failure in High Voltage Direct Current," Energies, MDPI, vol. 10(10), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1481-:d:113121
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    Citations

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    Cited by:

    1. Chengjun Xia & Xia Hua & Zhen Wang & Zhenlin Huang, 2018. "Analytical Calculation for Multi-Infeed Interaction Factors Considering Control Modes of High Voltage Direct Current Links," Energies, MDPI, vol. 11(6), pages 1-19, June.
    2. Chao Xiao & Xiaofu Xiong & Jinxin Ouyang & Getu Ma & Di Zheng & Ting Tang, 2018. "A Commutation Failure Suppression Control Method Based on the Controllable Operation Region of Hybrid Dual-Infeed HVDC System," Energies, MDPI, vol. 11(3), pages 1-13, March.
    3. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    4. Li Sun & Hongbo Liu & Chenglian Ma, 2020. "AC Tie-Line Power Oscillation Mechanism and Peak Value Calculation for a Two-Area AC/DC Parallel Interconnected Power System Caused by LCC-HVDC Commutation Failures," Energies, MDPI, vol. 13(5), pages 1-14, March.
    5. Wen Si & Simeng Li & Huaishuo Xiao & Qingquan Li & Yalin Shi & Tongqiao Zhang, 2018. "Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer," Energies, MDPI, vol. 11(3), pages 1-19, March.

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