Analysis of aviation DC series arcing characteristics based on LMD

Direct current (DC) series arcs are common electrical faults in aviation systems. These faults are challenging to extinguish and detect. To address this issue, a DC series arc fault characteristic analysis method utilizing local mean decomposition (LMD) is proposed. First, a experimental platform is established, and current signals are collected. Next, the LMD method is used for modal decomposition to obtain the arc fault current components. These components are then described using six characteristic indexes. A characteristic index selection method, based on the significance of characteristic changes, is employed to identify and analyze high-recognition characteristic. Experimental results indicate that the mean value, mean square value, and kurtosis index of PF3 significantly increase after LMD decomposition of the DC series arc fault current. Notably, there are significant differences in these indexes before and after the occurrence of an arc fault. These findings remain unaffected by variations in load, offering essential theoretical support for the detection and identification of DC series arc faults in aviation systems.