Series Arc Fault Detection in Photovoltaic Systems Based on Signal-to-Noise Ratio Characteristics Using Cross-Correlation Function

Abstract

Series arc fault (SAF) is one of the most important causes for failure of photovoltaic (PV) systems. Conventional protections could not detect SAF at initial moments. In this paper, a new method is presented for detection of SAF. The method is based on extracting SAF signatures from high frequency contents of the normalized dc terminal voltage. Inverter switching signatures in the signal have a periodic nature, which are rejected by subtracting the resized data-windows based on the lag relevant to maximum cross-correlation value. The utilized criterion is defined as ratio of power of the low frequency components to power of the arc signal (or signal-to-noise ratio). A set of possible scenarios, whether in practice or simulation, is considered to evaluate the performance of the method. They include different arc lengths, different fault locations in the PV system, various partial shadings, different switching frequencies for the inverter, and different types of environmental noise.