A Detection Method for a Series Arc Fault Based on the Inevitable DC Component Due to the Arcing Process’s Asymmetry

Abstract

Due to the great diversity of loads in low-voltage systems, the detection based on characteristic parameters of the current often confuses series arc faults (SAFs) with complex loads. To address this issue, an SAF detection method is proposed based on the inevitable dc component. First, comprehensive analyses, as well as observations, are made on the electrode-arcing-current asymmetry (EACA) to demonstrate that an inevitable dc component is inevitably induced during an SAF. Then, a dc-related dominated index and several asymmetry-related supplemental indices are gathered to form a feature set with strong generality. Afterward, a specific scheme is developed based on the uni-period state evaluation and the multiperiod fault judgment to reduce the false detection, where the eXtreme gradient boosting (XGBoost) algorithm is employed as a classifier. After that, experiments are made to verify the proposed method’s validity. Finally, with monitored samples used to construct an ultrageneral testing set, simulations are conducted to prove its superiority in generality.