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

doi:10.3390/s21051687

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

Sensors (Basel). 2021 Mar 1;21(5):1687. doi: 10.3390/s21051687.

Authors

Alessandro Betti¹, Mauro Tucci², Emanuele Crisostomi², Antonio Piazzi¹, Sami Barmada², Dimitri Thomopulos²

Affiliations

¹ i-EM S.r.l. (Intelligence in Energy Management), 57121 Livorno, Italy.
² Department of Energy, Systems, Territory and Construction Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy.

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.

Keywords: PV plants; fault prediction; inverter module; key performance indicator; lost production; self-organizing maps.