Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 28 January 2021 / Approved: 29 January 2021 / Online: 29 January 2021 (15:42:39 CET)

A peer-reviewed article of this Preprint also exists.

Betti, A.; Tucci, M.; Crisostomi, E.; Piazzi, A.; Barmada, S.; Thomopulos, D. Fault Prediction and Early-Detection in Large PV Power Plants based on Self-Organizing Maps. Sensors 2021, 21, 1687. Betti, A.; Tucci, M.; Crisostomi, E.; Piazzi, A.; Barmada, S.; Thomopulos, D. Fault Prediction and Early-Detection in Large PV Power Plants based on Self-Organizing Maps. Sensors 2021, 21, 1687.

Journal reference: Sensors 2021, 21, 1687
DOI: 10.3390/s21051687

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.

Subject Areas

PV plants; Self-Organizing Maps; Fault Prediction; Inverter Module; Key Performance Indicator; Lost Production

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