A series arc fault diagnosis method based on random forest model

The current of series arc fault is too weak to be detected by the circuit breaker, which is one of the causes of electrical fire. Therefore, an intelligent diagnosis method of series arc fault based on random forest (RF) is proposed in this study. Firstly, the high-frequency current signals of six kinds of loads are collected as experimental data. Then, 13 features are extracted from time domain and frequency domain, and the feature is reduced to four dimensions by principal component analysis (PCA). Finally, a classifier for series arc fault diagnosis is designed using RF. The experimental data in this study are collected by the low-voltage AC series arc fault data acquisition device developed by ourselves. The identification accuracy of series arc fault is 99.95 +/- 0.03%. Compared with the existing series arc fault diagnosis methods, it has higher recognition performance.