基于电流模式分解的非入户式故障电弧识别

Non-invasive arc fault recognition based on current mode decomposition

近年来电气火灾频发,故障电弧是重要诱因之一。考虑实际低压用户场景特点,开展非入户式故障电弧检测与识别方法研究。首先,采集用户供电入口处的负荷总电流波形数据,通过谐波分析得到总电流基波和各次谐波的幅值和相位信息;然后,将总电流和预训练得到的电流特征参数矩阵一起构建目标函数,形成多负荷电流分解模型;最后,采用智能寻优算法进行最优化求解,得到各个电器设备的运行状态(包括故障状态),判别电弧故障并分析其成因。在实验室条件下,针对低压用户常见电器进行故障电弧模拟实验,结果证明所提出的基于电流模式分解的非入户式故障电弧检测方法的有效性。

In recent years,electrical fires occur frequently.Arc fault is a important causes of electrical fires.In this paper,considering the characteristics of the low-voltage customer scenarios,the research of non-invasive arc fault detection is carried out.First,the aggregated load current waveform data is acquired at the entrance of the customer's power supply.Then,the amplitude and phase information of the fundamental and each harmonic wave of the total current is obtained by harmonic analysis.Next,the total current and the current characteristic parameter matrix obtained from training are used together to construct the objective function and form a multi-load current decomposition model.Finally,the intelligent optimization algorithm is adopted to optimize the solution to obtain the operating state of each appliance(including the fault states),and identify the arc fault and analyze its causes.In addition,this paper carries out the simulation experiment of arc fault for common appliances of actual low-voltage users in the laboratory,and the experimental results show that the proposed non-invasive arc fault recognition method is effective.