  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. Keywords: Data Mining, Fault Prediction, Inverter Module, Key
Performance Indicator, Lost Production
