Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps

Alessandro Betti, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, Dimitri Thomopulos
2021 Sensors  
In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of
more » ... nverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
doi:10.3390/s21051687 pmid:33804448 fatcat:g6jrpniyxzfn5geb5loogzdjde