基于多层卷积神经网络的串联电弧故障检测方法A Series Arc Fault Detection Method Based on Multi-layer Convolutional Neural Network
褚若波;张认成;杨凯;肖金超;
摘要(Abstract):
低压配电网的电弧故障是诱发电气火灾的重要原因之一。配电网发生串联电弧故障时的电流一般较小,其有效值达不到过电流保护装置的整定值,而且在某些负载工况下,正常工作状态电流与串联电弧故障电流波形特征非常相似,导致串联电弧难以识别。针对串联电弧故障的识别难点,提出了一种基于多层卷积神经网络的时域可视化识别方法。使用高频耦合滤波电路和高速数据采集系统来采集串联电弧故障的高频信号。通过构建多层卷积神经网络,提取电弧图像高维特征。以时域灰度值图像的形式直观展示了卷积神经网络算法对故障电弧数据的抽象特征提取情况。通过与前沿机器学习预测算法进行对比分析,所提出的算法具有对典型负载的串联电弧进行特征学习和识别的良好特性,并且在其他故障诊断领域也具有重要的借鉴意义。
关键词(KeyWords): 卷积神经网络;串联电弧故障;高频耦合滤波电路
基金项目(Foundation): 福建省自然科学基金项目(2018J05082);; 福建省产学合作重大科技项目(2016H6014);; 广州市珠江科技新星专项资助(201710010023);; 华侨大学研究生科研创新基金项目~~
作者(Author): 褚若波;张认成;杨凯;肖金超;
Email:
DOI: 10.13335/j.1000-3673.pst.2019.2489
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