Fig.8_data from An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.低压电路中的电弧故障是引发电气火灾的主要隐性诱因。精准识别电弧故障对于保障用电安全至关重要。本文在低压电路环境下对一种电弧故障检测算法开展了测试验证：通过电容耦合与对数检测器，可提取电弧故障的高频辐射特征；通过快速计算电弧故障电流波形斜率特征的方法，可获取另一类故障特征；基于电弧故障的不对称性，还可提取电流波形的周期性积分特征。基于上述三类特征，本文构建了一种融合多信息、依托支持向量机（support vector machine）学习模型的电弧故障检测算法。测试结果表明，针对单负载与复合负载下的串联电弧故障，以及金属触点间、碳化路径上的并联电弧故障，该识别算法可有效规避电弧故障检测过程中的串扰与信号丢失问题。