Detection and identification of faulty modules in a large-scale photovoltaic installation

Abstract

The global warming and realisation that fossil fuel is limited, has been a drive over the years to move to renewable energy sources (RES). Consequently, solar energy and other renewable energy sources are currently being exploited. The fast development of photovoltaic technologies makes the systems one of the leading methods to effectively exploit the RES. In recent times, the Photo voltaic (PV) system are used at large to augment the classical energy supply. However, large-scale photovoltaic installations possess challenges of being susceptible to faults. Therefore, it is imperative that faulty modules are detected and isolated to preserve the efficiency of the overall PV system. Fault detection is crucial to ensure operational reliability of a large-scale photo-voltaic installations (LSPVI). However, faults detection and identification are still a key challenge in LSPVI. In a large-scale photovoltaic installation, there are large quantity of photovoltaic arrays. Because of these arrays and their complex configuration, various types of faults are produced frequently which directly affect reliability and safety of PV installation. One of the goals of any PV installation is to retain power generation with the desired operation efficiency. Therefore, it is imperative that faulty modules are detected to preserve an efficiency of PV system. In this study, a fault detection and identification technique which involves excitation of both normal and faulty modules with signals that have various frequencies was implemented for detection of faulty modules in LSPVI. The results obtained show that the proposed techniques can diagnose and identify faulty solar panels in a large-scale photovoltaic installation.