Feature Enhancement and Extraction Method of Parallel Arc Fault in Three-Phase Motor and Frequency Converter Load Circuit

The parallel arc fault is one of the most important causes of electrical fire. However, there is no effective detection method for parallel arc fault. To solve this problem, a fault feature enhancement method based on energy transformation and reconstruction of modal components and a fault feature extraction method based on non-negative matrix factorization were proposed. First, the parallel arc fault experiments under different load current and current-limiting resistor conditions were carried out in the three-phase motor and frequency converter load circuit. Second, the current signal was decomposed into k modal components by using adaptive empirical wavelet transform, and the energy transformation and reconstruction of each modal component were performed in turn to enhance the fault features. Third, the parallel arc fault features were extracted from the enhancement signals by using two-stage non-negative matrix factorization dimension reduction. Finally, the performance of the fault feature enhancement and extraction method was tested by using the identification model established with a least squares support vector machine. The results indicated that the proposed method can effectively enhance and extract the parallel arc fault features from the current signals, and the detection accuracy of the parallel arc fault can reach more than 95%.