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New i-EM paper on fault prediction and early-detection in large PV plants

We are really happy to announce that MDPI has recently published a new paper on “Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps” on its website. The Head of data Science at i-EM, Alessandro Betti, together with our our Data Scientist, Antonio Piazzi, are among the authors of this paper, made in collaboration with Mauro Tucci, Emanuele Crisostomi, Sami Barmada and Dimitri Thomopulos (Department of Energy, Systems, Territory and Construction Engineering – DESTEC, University of Pisa).

Here’s the paper’s abstract:

“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 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”.