Intelligent DC Arc-Fault Detection of Solar PV Power Generation System via Optimized VMD-Based Signal Processing and PSO–SVM Classifier

Abstract

In a solar photovoltaic (PV) power generation system, arc faults including series arc fault (SAF) and parallel arc fault (PAF) may occur due to aging of joints or other reasons. It may lead to a major safety accident, such as fire, if the high temperature caused by the continuous arc fault is not identified and solved in time. Because the SAF without drastic current change is difficult to detect, an intelligent detection algorithm based on the optimized variational mode decomposition and the support vector machine (SVM) is investigated in this article. The proposed algorithm uses the variational mode decomposition to extract the fault information from current signals, and then screens the statistical information of the signals in each frequency band by the proposed adaptive feature screening. The features to be strongly correlated with classification are taken as inputs into the SVM optimized by the particle swarm optimization for classification finally. The proposed intelligent framework not only can accurately identify the SAF occurring at different locations, but also identify the PAF. Moreover, it can also maintain good diagnosing results under the occurrence of dynamic shading, inverter startup, and SAF under wind blowing. In addition, single-series PV string and solar PV power generation systems in different countries are also used to examine the universal ability of the proposed algorithm. As for experimental results, the detection accuracy is more than 98.21% under all examined conditions.