Fault localization of fully parallel AT traction network based on VMD‑SSDEO
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(1.School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China; 2.School of Railway Power Supply and Electrical Engineering, Hunan Vocational College of Railway Technology, Zhuzhou 412006, China; 3.Periodical Press,Changsha University of Science & Technology, Changsha 410114,China))

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TM77

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    Abstract:

    To address the issue of weakly discernible wave heads in the reflection waves of faults in the fully parallel auto transformer (AT) traction network, compounded by difficult localization caused by refraction and reflection complexity of fault traveling waves introduced by the parallel structure of the lines, a single-ended localization method for fault traveling waves in the traction network is proposed based on variational mode decomposition (VMD) and enhanced energy operator. Firstly, the transmission characteristics of fault traveling waves in the traction network are analyzed, with a focus on the impact of the fully parallel structure on the refraction and reflection of fault traveling waves. Features of different fault types and power flows are determined, facilitating the extraction of fault features. The process of wave head identification is transformed into the extraction of abrupt energy changes. Then, VMD is used for denoising and extracting the genuine voltage traveling wave component. In view of the challenging calibration of weak wave heads in the second reflection wave, a sliding time window (STW) combined with the symmetrical differencing energy operator (SDEO) is employed to construct the second instantaneous energy spectrum of the fault signal, yielding satisfactory results. Simulation outcomes demonstrate the method’s robust resistance to transient resistances, its ability to reflect variations in electromagnetic energy under different operating conditions in the fault traction network, and its high precision in fault localization.

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李泽文,冯译萱,夏翊翔,张一鸣,刘国胜,张灵芝,罗姗姗.基于VMD‑SSDEO的全并联AT牵引网故障定位[J].电力科学与技术学报英文版,2025,40(1):55-66. LI Zewen, FENG Yixuan, XIA Yixiang, ZHANG Yiming, LIU Guosheng, ZHANG Lingzhi, LUO Shsnshan. Fault localization of fully parallel AT traction network based on VMD‑SSDEO[J]. Journal of Electric Power Science and Technology,2025,40(1):55-66.

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  • Online: March 18,2025
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