摘要
针对串联故障电弧发生时隐蔽性和随机性强、电流幅值相对较小易被负载电流湮没、与负载性质关联性大而导致的难以准确检测问题,提出一种基于相关理论及零休特征融合的串联故障电弧检测方法。以参照UL1699标准搭建的低压单相交流串联故障电弧实验平台为基础,通过采集用电回路的两个周期电流并计算其零休时间比例系数、滤除低频成份后的归一化绝对值最大互相关系数,然后用模糊逻辑器将两系数进行融合处理得到串联故障电弧综合特征识别系数,进一步结合零休时间比例系数并分别与经验阈值比较,从而判别是否有串联故障电弧发生。结果表明,该法对低压单相交流用电回路中使用GB14287.4推荐负载时产生的串联故障电弧辨识率高达100%,无误判漏判现象发生。
Due to the concealment and randomness of the series fault arc,it is difficult to detect these faults accurately.The relatively small current amplitude is easy to be annihilated by the load current,and the load is highly correlated with the nature of the load.To solve these problems,a method based on the low-voltage single-phase AC series fault arc experiment platform is proposed,which refers to the UL1699 standard.Two periodic currents of the electrical circuit are collected.The proportion coefficient of zero current time and the maximum correlation coefficient of the normalized absolute value after filtering the low-frequency components are calculated.Then,two coefficients are fused by a fuzzy logic processor to obtain the comprehensive characteristic identification coefficient of the series fault arc.It is possible to identify whether there is occurrence of series fault arc by comparing the deep combination of the achieved coefficient and the proportion coefficient of zero current time with the empirical threshold value.Experimental results show that this method can recognize up to 100%of the series fault arc when the recommended load in GB14287.4 is used in the low-voltage single-phase AC power circuit.There is no phenomenon of misjudgment and leakage.
作者
赵怀军
秦海燕
刘凯
朱凌建
Zhao Huaijun;Qin Haiyan;Liu Kai;Zhu Lingjian(School of Mechanical and Precision Instrument Engineerings Xi'an University of Technology,Xi'an 710048,China;Shenyang Fire Research Institute of MEM,Shenyang 110034,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第4期218-228,共11页
Chinese Journal of Scientific Instrument
基金
国家重点研发计划重大科学仪器设备开发重点专项(2017YFF0104403)资助。
关键词
串联故障电弧检测
零休时间比例系数
相关理论
特征融合
series fault arc detection
proportion coefficient of zero current time
correlation theory
feature fusion
