基于邻波电流差与随机性的交流串联电弧故障识别

低压电力线路的交流串联电弧故障易引发电气火灾，造成人身财产损失。根据故障的电流突变量幅值与电流变化量的电弧随机性特征，提出了基于电流邻波绝对差与随机性的电弧识别方法。该方法基于故障前后的电流突变量变化规律，以突变幅度作为故障启动判据。然后根据故障周期间电流变化量在不同负载种类、气隙间距下的电弧随机特征时域分布，构建了电弧故障存在性判据。最后通过一维卷积神经网络综合识别电弧故障。当故障支路负载功率占比20%时，所提方法使用未训练干路数据的平均检测准确率为90.97%，可有效检测串联电弧故障，具有较好的适应性。

Series arc fault detection in low-voltage AC power lines based on absolute difference of the neighboring waveform of the current and randomness

The AC series arc fault of low-voltage power lines is prone to electrical fires, causing personal and property loss. Given the sudden change of fault current amplitude and the arc randomness waveform characteristics of the current change, this paper proposes a recognition method based on the absolute difference of neighboring waves and randomness of the fault current. The method is based on the change law of the current abrupt change amount before and after the fault, and the abrupt change amplitude is used as the initiation criterion. Then an arc fault existence criterion is constructed based on the arc random characteristic time domain distribution of the current change amount during the fault cycle under different load types and air gap spacing. Finally, arc fault identification is achieved through a one-dimensional convolutional neural network (1DCNN) integrated. The average detection accuracy of the method using untrained trunk circuit data is 90.97% when the load power of the faulty branch circuit accounts for 20%. Thus this can effectively detect series arc faults with good adaptability.