Alessandro Betti, Maria Luisa Lo Trovato, Fabio Salvatore Leonardi, Giuseppe Leotta, Fabrizio Ruffini and Ciro Lanzetta.
This paper presents a novel and flexible solution for fault prediction based on data collected from
SCADA system. Fault prediction is offered at two different levels based on a data-driven approach: (a) generic
fault/status prediction and (b) specific fault class prediction, implemented by means of two different machine learning
based modules built on an unsupervised clustering algorithm and a Pattern Recognition Neural Network, respectively.
Model has been assessed on a park of six photovoltaic (PV) plants up to 10 MW and on more than one hundred inverter
modules of three different technology brands. The results indicate that the proposed method is effective in (a) predicting
incipient generic faults up to 7 days in advance with sensitivity up to 95% and (b) anticipating damage of specific fault
classes with times ranging from few hours up to 7 days. The model is easily deployable for on-line monitoring of
anomalies on new PV plants and technologies, requiring only the availability of historical SCADA and fault data, fault
taxonomy and inverter electrical datasheet.