A New Method for Detecting Series Arc Fault in Photovoltaic Systems Based on the Blind-Source Separation

Abstract

Photovoltaic networks are outdoor infrastructures, faced to different harsh conditions, which may experience various failures such as parallel arc fault (PAF) and series arc fault (SAF). Although PAF is more severe than SAF, the detection of SAF is more problematic, and its hazards including fire and the risk of staff electrocution are more serious. This paper proposes a new method for timely and reliable detection of SAF in photovoltaic systems. In this method, one of the blind-source separation algorithms called the principal component analysis (PCA) is employed. This method separates the nondependent components of some measurable quantities such as voltage and current using eigenvectors of their covariance matrix. In a normal condition, these signals include the dc component, switching components, and the network disturbances. When a SAF occurs, some new components by the electrical arc also add to the system. Using PCA, a suitable index is derived to discriminate signatures of SAF from the other components. Performance of the method is evaluated using plenty of experiments in different conditions.