The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy

Abstract

This paper presents a series arc-fault detection algorithm for photovoltaic (PV) systems, which relies on the quantum probability model theory. The algorithm determines the presence of the arc by calculating the modified Tsallis entropy of the PV panel current. Based on the calculated entropy, the algorithm is able to differentiate an arc state (when the current variations are chaotic) from a no-arc state (when the current variations are ordered). The proposed algorithm enables arc-fault detection on a plug-and-play principle, requiring no prior information about the PV system in which it operates. The operation of the algorithm was first simulated on the prerecorded data from the system with the PV panel simulator and the commercial PV inverter. A laboratory prototype of the detector was then built and tested in a real 1.6-kW grid-connected PV system with different PV inverter, without any additional parameter adjustments. Both the sensitivity and the robustness of the detector were confirmed. The tests have shown that both the sustained series arcs and the small sparking series arcs were successfully detected, and there was no false detection due to the MPP tracker operation, PV current step change, or inverter turn on transient.