Research on Series Arc Fault Detection and Phase Selection Feature Extraction Method

Abstract

Series arc fault is one of the important causes of electrical fire in industrial and mining enterprises. It is of great significance to study the series arc fault detection and phase selection feature extraction method to ensure safe and stable operation of electrical equipment and to guide line maintenance. Arc fault experiments under different current and circuit conditions with a three-phase motor and inverter load were carried out. A new arc fault detection and phase selection method based on single-phase current was proposed. First, wavelet threshold noise reduction, piecewise linear fitting, and first-order difference processing were performed on single-phase current signals to filter out noise interference and highlight fault features. Second, fractional Fourier transform (FRFT) was applied to the first-order differential signal to construct the amplitude matrix of the signal from the time domain to the frequency domain. The local features of the amplitude matrix were effectively extracted, and the feature vector of arc fault with lower dimension was established by combining the two-level block singular value decomposition (SVD) method. Finally, an arc fault detection and phase selection model was established using a support vector machine (SVM) optimized by grid search (GS) and particle swarm optimization (PSO) algorithm. The applicability of the model in single-phase multiload was analyzed. The results showed that the proposed method could realize series arc fault detection and phase selection in three-phase motor and inverter circuits, and it can also be used to single-phase multiload circuits.