Series Arc Fault Detection Based on Dual Filtering Feature Selection and Improved Hierarchical Clustering Sensitive Component Selection

Affected by the downstream load of the line, the low-voltage ac series arc fault signal presents nonlinear, nonstationary, and random characteristics, which bring great difficulties to the extraction of arc fault features and lead to low arc detection accuracy. To solve this problem, a low-voltage ac series arc fault detection method based on strong discriminative features and sensitive components is proposed. In this work, first, the arc fault current signal is collected based on the self-built experimental platform, and the arc fault current components are obtained by the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) method. Second, 16 feature indexes based on time window are used to describe the component signals, and an effective feature selection method based on the maximum mutual information coefficient and the significance of feature changes is proposed to realize the mining of highly identifiable features. Then, an improved hierarchical clustering algorithm and a sensitive component selection strategy combining the saliency of feature changes are proposed to eliminate the interference of redundant components. Finally, a strong discriminative feature library of sensitive components of the current signal is constructed, and series arc fault detection is realized based on a support vector machine (SVM). Experimental results show that the proposed method is feasible and effective in arc current feature extraction and fault detection and provide a reference for low-voltage ac series arc fault detection.