Series Arc Fault Detection Method Based on Signal-Type Enumeration and Zoom Circular Convolution Algorithm

Ac series arc fault detection under various circuits is always a challenging task because arcing current is affected by different circuit types and fault points. Additionally, normal current is confused as arcing current when nonlinear loads are present in the circuit. To cope with these issues, this article presents a detection method using signal-type enumeration and a zoom circular convolution (CC) (ZCC) algorithm. The generalization ability of the detection method under unknown conditions is improved by means of signal-type enumeration. The impulsive components of current can be divided into stable, periodically impulsive, nonperiodically impulsive, and hybrid signals by enumeration. Then, given the CC limitations, the ZCC is presented to extract distinguishable features and decrease the computation complexity of CC, and the signal function is partially reconstructed to improve the CC performance. Finally, the presented detection method is analyzed in a laptop and evaluated by TMS320F28335. The online detection results show that the proposed method determined by known single-load circuits has good detection accuracy under unknown conditions and can be achieved in practical application.