基于DRSN的高噪声环境下XLPE电缆故障识别

Fault Identification of XLPE Cable in High Noise Environment Based on DRSN

为对高噪声环境下交联聚乙烯(XLPE)电缆故障进行智能化识别,提出了一种基于深度残差收缩网络的XLPE电缆故障识别方法,该方法将软阈值作为非线性变换层嵌入到网络深层结构中,并引入软注意力机制对软阈值进行加权优化,从而加强深度神经网络从高噪声局部放电信号中特征学习的能力,进而提高电缆故障诊断精度。首先,根据运维、检修经验制作了4种典型的终端故障,并搭建局部放电测试系统,测试得到不同电压等级下局部放电数据,并对其进行加噪处理;然后,通过深度残差收缩网络完成不同噪声环境下故障数据特征提取及分类;最后,与其他故障诊断方法进行对比。结果表明:该方法能够有效地对噪声信号进行抑制,极大地提高了高噪声环境下电缆故障诊断精度,为后续的工程应用提供了切实可行的方法。

In order to intelligently identify crosslinked polyethylene(XLPE)cable faults in high noise environment,a method of XLPE cable fault identification based on deep residual shrinkage network was proposed.In this method,the soft threshold was embedded into the deep structure of the network as a nonlinear transformation layer,and the soft attention mechanism was introduced to optimize the soft threshold,so as to enhance the ability of deep neural network to learn features from high noise partial discharge signals,and improve the accuracy of cable fault diagnosis.Firstly,according to the experience of operation and maintenance,four kinds of typical terminal faults were made,and a partial discharge test system was built to test the partial discharge data under different voltage levels and add noise to them.Then,fault data feature extraction and classification under different noise environments were completed through deep residual shrinkage network.Finally,compared with other fault diagnosis methods.The results show that the method can effectively suppress the noise signal,greatly improve the accuracy of cable fault diagnosis in high noise environment,and provide a practical method for the subsequent engineering application.