张重远;岳浩天;王博闻;刘云鹏;罗世豪;

• 1:河北省输变电设备安全防御重点实验室(华北电力大学)

摘要(Abstract)：

电气设备的故障类型与局部放电现象密切相关,有效提取和分析局部放电信号中的特征信息对故障类型判断和运维检修具有重要意义。针对局部放电超声信号的特点,提出了基于相似矩阵的盲源分离方法对原始超声信号进行预处理,有效提取局部放电的特征量。采用光纤传输的局部放电超声检测平台对4种类型的局部放电信号进行采集,并应用上述方法对信号数据预处理,将处理后的数据作为训练样本用于深度学习模式识别,选用卷积神经网络,最终识别准确率达到90%以上,提高了局部放电类型识别的准确性,为新一代电力系统的设备故障诊断提供了一种新方法。

关键词(KeyWords)： 局部放电;超声波;盲源分离;相似矩阵;深度学习;卷积神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目(5200201955095A0000)~~

作者(Author): 张重远;岳浩天;王博闻;刘云鹏;罗世豪;

Email:

DOI: 10.13335/j.1000-3673.pst.2018.2365

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