Low-Voltage Alternating Current Series Arc Fault Detection Using Periodic Background Subtraction and Linear Dividing Lines

To address the challenges in fully expressing the fault features of low-voltage series arcs and the limitations of existing detection algorithms, this paper proposes a novel method combining periodic background subtraction and linear dividing lines for detecting arc faults. During feature extraction, a periodic background subtraction method is introduced, which calculates the difference between the periodic current signal and the average of the first four current periods nearest to the signal. This approach effectively suppresses interference from normal current variations and environmental noise, enabling a more robust expression of arc fault characteristics. For fault detection, a method based on linear dividing lines is developed to determine the optimal dividing line between fault and non-fault states for both linear and nonlinear load types via adaptive learning of the logistic regression model, achieving effective arc fault detection.