PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast

The employment of solar microconverter allows a more detailed monitoring of the photovoltaic (PV) output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants, such as regular shadings. In this way, it is possible to compare in real time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends that might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a microconverter, and on the outcome of the neighbor PV modules. Finally, this paper also proposes the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.