Method of monitoring and detection of failures in PV system based on machine learning
| Main Authors: | Dario Javier Benavides, Paul Arévalo Cordero, Luis G. González, Luis Hernández Callejo, Francisco Jurado, José A. Aguado |
|---|---|
| Other Authors: | Revista Facultad de Ingeniería, Universidad de Antioquia |
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5256291 |
Daftar Isi:
- 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.