Open AccessMethod of monitoring and detection of failures in PV system based on machine learning
Author(s) -
Darío Benavides,
Paul Arévalo-Cordero,
L. G. González,
Luís Hernández-Callejo,
Francisco Jurado,
José A. Aguado
Publication year - 2021
Publication title -
revista facultad de ingeniería universidad de antioquia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 12
eISSN - 2422-2844
pISSN - 0120-6230
DOI - 10.17533/udea.redin.20200694
Subject(s) - scada , photovoltaic system , computer science , electricity , smart grid , grid , machine learning , reliability engineering , artificial intelligence , control engineering , industrial engineering , engineering , electrical engineering , geometry , mathematics
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.