Fault Detection In Photovoltaic Systems Using Machine Learning Algorithms: A Review

The power produced by photovoltaic systems have great importance in the current global market. From small-scale applications to self-sufficient industries PV systems are planted for the generation and distribution of power. The main factors that need to be considered when setting up a PV plant are safety, cost efficiency and early fault detection techniques. This review work displays the various types of faults seen in PV systems and briefs about the intelligent machine learning algorithms that are built to detect and classify these faults. Furthermore, with the extensive literature, this paper reviews various types of faults detected using different machine learning algorithms in photovoltaic systems which are shown to be reliable and effective to be implemented. The research work is not only bounded to reviewing the fault types with regard to the machine learning algorithms but also talks about 1)Types of PV system, 2)Health state estimation, 3)Fault types, 4)AC/DC side fault, 5)Integration Complexity, 6)Accuracy, 7)Cost of realization. In conclusion, this study is disclosed to share all the valuable information to the scholars working in the field of photovoltaic systems.