基于APIT-MEMD的牵引网行波故障定位方法研究
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华东交通大学

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国家自然科学(51467004);


Research on traveling wave fault location method of traction network based on APIT-MEMD
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    摘要:

    为了解决全并联AT牵引网故障点入射波、反射波以及AT所反射波等不同成分的行波难以识别,使得故障定位十分困难的问题。本文提出了一种基于自适应投影多元经验模态分解(APIT-MEMD)的波形相似度行波故障定位方法。采用APIT-MEMD算法对上下行多导线故障信号进行自适应投影多元模态分解,以提取表征不同成分故障行波的暂态高频特征。通过构建不同成分行波模态分量的互相关函数矩阵,以识别不同路径的行波,并计算相应的互相关函数最大时延,从而实现牵引网行波故障定位。实验结果表明,基于时频模态特征提取的方法故障定位的误差在102 m以内,且平均绝对误差为49 m,并与多元经验模态分解算法不同投影参数的结果进行对比,有效优化了故障定位精度。优化后的行波故障定位算法满足高精度的故障定位需求。

    Abstract:

    To address the challenge of identifying traveling waves with different components, including fault point incident waves, reflected waves, and waves reflected by AT, which makes fault location on all parallel AT traction networks difficult, this paper proposes a wave similarity-based fault location method using Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition (APIT-MEMD). The APIT-MEMD algorithm is applied to adaptively decompose the fault signals of multi-conductor lines in both directions to extract transient high-frequency characteristics representing different components of fault traveling waves. By constructing the cross-correlation function matrix of different wave mode components to identify traveling waves along different paths and calculating the corresponding maximum time delay of the cross-correlation function, the fault location of the traction network's traveling waves is achieved. Experimental results demonstrate that the proposed method, based on time-frequency mode feature extraction, achieves fault location accuracy within 102 m with an average absolute error of 49 m.Compared with the results of different projection parameters in the multi-dimensional empirical mode decomposition algorithm, the effectiveness of fault location accuracy is optimized. The optimized fault location algorithm meets the requirements for high-precision fault location.

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  • 收稿日期:2023-09-09
  • 最后修改日期:2024-04-25
  • 录用日期:2024-04-28
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