基于SSA ELM的直流串联故障电弧检测方法研究  

DC Series Fault Arc Detection Method Based on SSA-ELM

作　　者：刘树鑫 刘学识 李静[1] 曹云东[1] 刘洋[1] LIU Shuxin;LIU Xueshi;LI Jing;CAO Yundong;LIU Yang(Key Lab of Special Electric Machine and High Voltage Apparatus in the Ministry ofEducation,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]教育部特种电机与高压电器重点实验室(沈阳工业大学),辽宁沈阳110870

出　　处：《电器与能效管理技术》2022年第10期65-73,共9页Electrical & Energy Management Technology

基　　金：辽宁省科技重大专项(2020JH1/10100012);辽宁省厅自然科学项目(LJGD2020001)

摘　　要：针对故障电弧信号随机性和不稳定性等问题,提出了一种基于SSA优化ELM(SSA-ELM)的直流串联电弧故障检测方法。首先根据UL 1699B标准搭建实验平台,进行数据采集;其次将采集到的直流故障电弧信号,通过添加自适应噪声的完全集合经验模态分解(CEEMDAN),得到固有模态分量(IMF),依据各IMF相关系数与能量分布,提取样本熵作为特征量;最后使用SSA-ELM学习特征量,并将该模型用于直流串联电弧故障检测。实验结果表明CEEMDAN分解方法对于信号干扰不敏感,SSA-ELM学习速度较快,对于直流串联电弧故障的检测识别更为准确灵敏。To solve the problem of the randomness and instability of fault arc signal,a DC series arc fault detection method based on sparrow search algorithm extreme learning machine(SSA-ELM)is proposed.Firstly,the experimental platform is built according to UL 1699B standard for data collection.Secondly,the DC arc fault signal is decomposed into the intrinsic mode components(IMF)by the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN).On the basis of the correlation coefficient and energy distribution of each IMF,the sample entropy as the feature quantity is extracted.Finally,SSA-ELM is used to learn the feature quantity,and the model is used for DC series arc fault detection.The experimental results show that CEEMDAN method is not sensitive to signal interference,SSA-ELM has a fast learning speed and is more accurate and sensitive for DC series arc fault detection and recognition.

关 键 词：直流串联电弧 经验模态分解 电弧故障 SSA ELM 

分 类 号：TM5012[电气工程—电器]