Machine Learning in PV Fault Detection, Diagnostics and Prognostics: A Review

Photovoltaic (PV) system malfunctions cause output efficiency to lower which consequently lowers the return of the investment (ROI) and delays investment payback times. These malfunctions can be limited by implementing Photovoltaic System Monitoring (PVSM) solutions. Recently, Machine Learning Techniques (MLT) have been implemented to improve PVSM results and aid in PV performance and PV fault detection, identification, diagnostics and prognostics. This paper provides a review of the work done in the MLT PVSM research field, provides an organized list of MLT solutions used in PVSM, and provides a list of opportunities and challenges to further research in the PVSM field.