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

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 o...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (5), p.1687
Main Authors: Betti, Alessandro, Tucci, Mauro, Crisostomi, Emanuele, Piazzi, Antonio, Barmada, Sami, Thomopulos, Dimitri
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Approximation
Failure
Fault diagnosis
fault prediction
Internet of Things
inverter module
Inverters
key performance indicator
lost production
Machine learning
Performance evaluation
Power plants
Preventive maintenance
PV plants
Self organizing maps
Supervisory control and data acquisition
Taxonomy
Thin films
Working conditions
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21051687