|本期目录/Table of Contents|

[1]张丽萍,缪希仁,石敦义.基于EMD 和ELM 的低压电弧故障识别方法的研究[J].电机与控制学报,2016,20(09):54-60.[doi:10. 15938 / j. emc. 2016. 09. 008]
 ZHANG Li-ping,MIAO Xi-ren,SHI Dun-yi.Research on low voltage arc fault recognition method based on EMD and ELM[J].,2016,20(09):54-60.[doi:10. 15938 / j. emc. 2016. 09. 008]
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基于EMD 和ELM 的低压电弧故障识别方法的研究(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
20
期数:
2016年09
页码:
54-60
栏目:
出版日期:
2016-09-01

文章信息/Info

Title:
Research on low voltage arc fault recognition method based on EMD and ELM
作者:
张丽萍1 缪希仁1 石敦义2
(1.福州大学 电气工程与自动化学院,福建 福州 350116; 2.华能罗源发电有限责任公司,福建 福州 350600 )
Author(s):
ZHANG Li-ping1 MIAO Xi-ren1 SHI Dun-yi2
 ( 1. College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350116,China; 2. HuaNeng Luoyuan Power Generation Co.,Ltd,Fuzhou 350600,China)
关键词:
故障电弧 经验模态分解 本征模态函数 极端学习机
Keywords:
fault empirical mode decomposition intrinsic mode function extreme learning machine
分类号:
TM 501
DOI:
10. 15938 / j. emc. 2016. 09. 008
文献标志码:
A
摘要:
针对低压配电线路负载端电弧故障电压具有较强的信号奇异性波形特征,利用低压串联电 弧故障实验平台,采集若干典型的低压配电线路负载端故障电弧电压信号进行分析。采用经验模 态分解( empirical mode decomposition,EMD) 有效地提取反映电弧故障信号局部特性的本征模态函 数( intrinsic mode function,IMF) 分量,经分析 IMF 分量的方差贡献率确定前 5 阶 IMF 用于表征各类 负载电弧故障主要特征信息,提取前 5 阶 IMF 分量能量比为特征向量作为极端学习机( extreme learning machine,ELM) 的输入向量,建立不同负载电弧故障识别模型。实验与仿真结果表明,基于 EMD 分解和 ELM 相结合的故障电弧诊断方法,在有效提取不同负载电弧故障特征的基础上,实现 了不同负载电弧故障的识别。
Abstract:
Arc fault voltage signal of load terminal in low-voltage is not affected by the singularity signal of power line to bring about fault misjudgment. An arc fault experimental platform was built with references to the United States standard-UL1699. The experiment was conducted to collect a large number of typical load arc fault signal. Firstly,the characteristics of arc fault signal intrinsic mode function ( IMF) compo-nents were extracted effectively by using empirical mode decomposition ( EMD) . Secondly,with analysis of the contribution rate of IMF variance,the front five orders IMF was taken to reflect various load arc fault characteristic information. Finally,an arc fault identification model for different loads based on ex-treme learning machine ( ELM) was put forward,whose input vectors is the IMF component ratio of ener-gy for front five orders. The experiment and simulation results show that the arc fault diagnostic method with the combination of EMD and ELM identifies arc fault for various loads effectively.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014 - 07 - 04 
基金项目: 国家自然科学基金( 51377023) ; 福建省教育厅教育科研项目( JA12050)
作者简介: 张丽萍( 1977—) ,女,博士研究生,讲师,研究方向为电气设备在线监测与故障诊断技术;
缪希仁( 1965—) ,男,教授,博士生导师,研究方向为电器及其系统智能化技术;
石敦义( 1989—) ,男,工程师,硕士,研究方向为电气设备在线监测工作
更新日期/Last Update: 2016-12-08