基于EMD和ELM的低压电弧故障识别方法的研究

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。

Research on low voltage arc fault recognition method based on EMD and ELM

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.