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

作　　者：褚若波 张认成[1] 杨凯[1] 肖金超[2] CHU Ruobo;ZHANG Rencheng;YANG Kai;XIAO Jinchao(Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment(Huaqiao University),Xiamen 361021,Fujian Province,China;Shenyang Institute of Automation Guangzhou,Chinese Academy of Sciences,Guangzhou 511458,Guangdong Province,China)

机构地区：[1]机电装备过程监测及系统优化福建省高校重点实验室(华侨大学),福建省厦门市361021 [2]中国科学院沈阳自动化研究所广州分所,广东省广州市511458

出　　处：《电网技术》2020年第12期4792-4798,共7页Power System Technology

基　　金：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。

摘　　要：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。Arc faults in low-voltage distribution networks are one of the important causes of fires. The current value of the series arc is generally too small to reach the setting value of the overcurrent breaker. Under certain load conditions, it is usually difficult to identify the series arc fault because its current has the similar waveforms as the normal working current. In order to solve this problem, a time domain visualization convolutional neural network(TDV-CNN) is proposed based on a multi-layer convolutional neural network. First, the high frequency signals of series arc faults are acquired with a high frequency coupled filter circuit and a high speed data acquisition system. Second, the high-dimensional features of the arc image are extracted by constructing a multi-layer convolutional neural network. Third, the abstract feature extraction of the arc fault data by the convolutional neural network algorithm is visualized in the form of the time-domain grayscale image. Finally, by comparing with and analyzing the other machine learning prediction algorithm, the proposed methodology is believed to be reliable for the series arc detection with relatively higher accuracy and also has important potential application in other fault diagnosis.

关 键 词：卷积神经网络 串联电弧故障 高频耦合滤波电路 

分 类 号：TM721[电气工程—电力系统及自动化]