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Betti A, Tucci M, Crisostomi E, et al. Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps. Sensors (Basel). 2021;21(5)doi: 10.3390/s21051687.
Betti, A., Tucci, M., Crisostomi, E., Piazzi, A., Barmada, S., & Thomopulos, D. (2021). Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps. Sensors (Basel, Switzerland), 21(5), . https://doi.org/10.3390/s21051687
Betti, Alessandro, et al. "Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps." Sensors (Basel, Switzerland) vol. 21,5 (2021). doi: https://doi.org/10.3390/s21051687
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 (Basel). 2021 Mar 01;21(5). doi: 10.3390/s21051687. PMID: 33804448; PMCID: PMC7957680.
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Sensors (Basel). 2021 Mar 01;21(5). doi: 10.3390/s21051687.

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

Sensors (Basel, Switzerland)

Alessandro Betti, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, Dimitri Thomopulos

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.

PMID: 33804448 PMCID: PMC7957680 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.

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

References

  1. IEEE Trans Neural Netw. 2010 Jun;21(6):948-60 - PubMed
  2. Sensors (Basel). 2016 May 26;16(6): - PubMed
  3. Sensors (Basel). 2020 Aug 20;20(17): - PubMed

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