This article is focused on low-frequency, spectral and correlation analysis to serial arc fault electrical system in order to detect arc fault. This analysis can be used to improve the security of power supplies in the automotive, aerospace and photovoltaic systems. More precisely, we study the influence of arc ignition on the signal characteristics. The experimental test bench is composed by an arc generator, a robotic cylinder and different loads. Three types of arcs ignitions are considered: carbonized path, contact opening and over-voltage. 0.5 kHz - 500 kHz spectrum is measured before and during the start phase of the arc and stabilization. The results show the possibility to determine the cause of the arc fault ignition. The shape of low frequency arc voltage is estimated from the current measurement with a very high fidelity where the load is known. Finally we study the possibility to locate series arcs by analyzing the correlation function of the Radio Frequency (RF) signals from two Rogowski coils inserted at two distinct points of the circuit. The calibration procedure performed reveals a mean velocity in the system of about 24 cm/ns.

This article describes arc faults detection methods based on the temporal analysis of electric lines current signatures. We first give the, specifications of the domestic loads under test. Then, we describe the methods under test: crest factor, average value, variance, adaptive filter and mathematics moments. In the second part, these methods are implemented on MATLAB - Simulink. The analysis is done on the current for each period half cycle. The ability of arc faults detection is then presented for each algorithm.