高技术通讯2023，Vol.33Issue(11)：1213-1222,10.DOI:10.3772/j.issn.1002-0470.2023.11.009
基于OS-EM-ELM的边缘侧串联电弧故障检测方法
Edge-side series arc fault detection method based on OS-EM-ELM algorithm
摘要
Abstract
The high randomness and complexity of arc fault make it difficult to be accurately identified.Aiming at the problem that the traditional arc recognition algorithm has low real-time performance and high hardware computing power,an error minimization extreme learning machine(EM-ELM)arc fault detection method suitable for edge computing,multi-load types and multi-feature combination is proposed.Through fast Fourier transform(FFT)and db4 wavelet decomposition,the period mean difference,pulse width percentage,inter-harmonic factor and wavelet high-frequency energy are extracted as the input characteristics of the arc fault detection algorithm on the edge side.On this basis,OS-EM-ELM combined with online sequence(OS)method is proposed,and the algo-rithm is improved by using field operation data to improve adaptability.The experimental results show that the pro-posed edge side arc fault detection method can effectively distinguish the normal and arc fault waveform,and it is suitable for the complex situation of working with a variety of loads at the same time.The calculation amount is small,the real-time performance is high,the adaptability is strong,and the application cost is low,which is more in line with the requirements of edge calculation of arc detection device.关键词
交流(AC)串联电弧/边缘侧/快速傅里叶变换(FFT)/故障识别/极限学习机(ELM)Key words
alternating current(AC)series arc/edge side/fast Fourier transform(FFT)/fault identifica-tion/extreme learning machine(ELM)引用本文复制引用
薛鹏,潘国兵,欧阳静,陈星星..基于OS-EM-ELM的边缘侧串联电弧故障检测方法[J].高技术通讯,2023,33(11):1213-1222,10.基金项目
国家重点研发计划(2017YFA0700301),浙江省重点研发计划(2021C01112)和浙江省基础公益技术研究计划(No.LGF21E070001)资助项目. （2017YFA0700301）
