Method of monitoring and detection of failures in PV system based on machine learning

Authors

DOI:

https://doi.org/10.17533/udea.redin.20200694

Keywords:

Artificial intelligence;, renewable energy sources, monitoring

Abstract


Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate the prediction of the energy production of three different photovoltaic systems and the supervision of measurement sensors, through Machine learning and data mining in response to the behavior of the climatic variables of the place under study. On the other hand, it also includes the implementation of the resulting models in the SCADA system through indicators, which will allow the operator to actively manage the electricity grid. It also offers a strategy in simulation and prediction in real-time of photovoltaic systems and measurement sensors in the concept of smart grids.

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Author Biographies

Dario Javier Benavides, Universidad de Málaga

Msc, Universidad de Málaga

Paul Arévalo-Cordero, Universidad de Jaén

MsC Universidad de Jaén and Universidad de Cuenca

Luis G. González, Universidad de Cuenca

PhD Universidad de Cuenca

Luis Hernández-Callejo, Universidad de Valladolid

PhD Professor Universidad de Valladolid

Francisco Jurado, Universidad de Jaén

PhD Universidad de Jaén

José A. Aguado, Universidad de Málaga

PhD Universidad de Málaga

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Published

2021-08-17

How to Cite

Benavides, D. J., Arévalo-Cordero, P. ., González, L. G., Hernández-Callejo, L., Jurado, F., & Aguado, J. A. (2021). Method of monitoring and detection of failures in PV system based on machine learning. Revista Facultad De Ingeniería Universidad De Antioquia, (102), 26–43. https://doi.org/10.17533/udea.redin.20200694

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