基于改进深度残差收缩网络的电缆早期故障识别
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TM247

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四川省科技厅项目(2021YFG0313,2022YFS0518,2022ZHCG0035);人工智能四川省重点实验室项目(2019RYY01);四川轻化工大学人才引进项目(2021RC12);自贡市科技局项目(2019YYJC02,2020YGJC16);四川轻化工大学研究生创新基金(Y2023278)


Research on Early Fault Identification of Cable Based on Improved Deep Residual Shrinkage Network
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    摘要:

    电缆早期故障的多次发生易造成电缆出现永久性故障,给电网的稳定运行带来严重的影响。为了在永久性故障发生前准确识别出电缆早期故障,本文提出一种基于改进深度残差收缩网络的电缆早期故障识别方法。首先通过改进的完全自适应噪声经验模态分解方法进行故障信号处理,并利用相关系数筛选IMF分量;然后对IMF分量求其复合多尺度排列熵作为进一步的特征提取,以构建特征数据集;最后利用改进的收缩模块,多尺度卷积层、Self-Attention和SimAM注意力机制对深度残差收缩网络进行了改进。使用改进的深度残差收缩网络进行电缆早期故障识别实验,实验结果表明,该算法能准确识别出电缆早期故障,且具有一定的抗干扰能力。

    Abstract:

    The frequent occurrence of early cable faults easily leads to the occurrence of permanent cable faults, causing serious impacts on the stable operation of the power grid. In order to accurately identify early cable faults before permanent failures occur, a method based on an improved deep residual contraction network for early cable fault identification is proposed in this paper. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise method was employed for fault signal processing, and correlation coefficients were utilized for filtering IMF components. Then, the composite multiscale permutation entropy of the IMF component was calculated for further feature extraction, constructing a feature dataset. Finally, the improved deep residual shrinkage network, incorporating an enhanced shrinkage module, multi-scale convolutional layer, Self Attention, and SimAM attention mechanism, was employed for early cable fault identification experiments. The algorithm is demonstrated by experimental results to accurately identify early cable faults and exhibit a certain degree of anti-interference capability.

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唐丹,吴浩,蔡源,等. 基于改进深度残差收缩网络的电缆早期故障识别[J]. 科学技术与工程, 2024, 24(28): 12159-12168.
Tang Dan, Wu Hao, Cai Yuan, et al. Research on Early Fault Identification of Cable Based on Improved Deep Residual Shrinkage Network[J]. Science Technology and Engineering,2024,24(28):12159-12168.

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  • 收稿日期:2023-09-18
  • 最后修改日期:2024-07-28
  • 录用日期:2024-03-21
  • 在线发布日期: 2024-11-05
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