This report provides an introduction to the emerging field of Statistical Performance Monitor-ing for photovoltaic (PV) systems and a survey of the development of these fault detection systems and their applications.
This survey found four primary methods used for identifying faults: (i) identifying faulty elec-trical signatures, (ii) comparing historical performance to actual performance, (iii) comparing predicted performance to actual performance and (iv) comparing the relationships between different PV systems or subsystems. The four approaches used for identifying faults include applying machine learning algorithms, statistical tests, specifying computational rules and generating simulations using models.