基于时频域分析和随机森林的故障电弧检测

Arc fault detection based on time and frequency analysis and random forest

针对生活用电器品种繁多,不同类型用电器之间的故障电流与正常电流波形可能类似,导致传统的故障电弧识别方法不能有效检测的问题,提出一种时频域分析与随机森林结合且适用于多种典型负载单独或混合工作的串联型低压故障电弧识别方法。根据收集到的多种负载频谱与纯阻性负载频谱的相关系数,将负载分为开关电源型负载和非开关电源型负载,分别训练两个随机森林模型对其进行故障识别。实验一共收集33 723组正常和故障电流样本验证提出的检测方法,证明所提方法能够提高故障电弧识别率。

For a wide variety of domestic appliances, the fault current waveforms among different types of appliances may be similar to normal current waveforms, which leads to the problem that traditional methods of fault arc identification cannot detect effectively, this paper presents a series low voltage fault arc identification method which combines time-frequency domain analysis and random forest which is suitable for a variety of typical loads working independently or mixed. Based on the correlation coefficients between the collected load spectra and the pure resistance load spectra, the loads are divided into switched-supply loads and non-switched-supply loads, then two random forest models are trained to identify the faults. A total of 33 723 sets of normal and fault current samples were collected to verify the proposed detection method, which proves that the proposed method can improve the recognition rate of fault arc.