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A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application
Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modif...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126226/ https://www.ncbi.nlm.nih.gov/pubmed/34068743 http://dx.doi.org/10.3390/s21093293 |
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author | de Melo, Gustavo Costa Gomes Torres, Igor Cavalcante de Araújo, Ícaro Bezzera Queiroz Brito, Davi Bibiano Barboza, Erick de Andrade |
author_facet | de Melo, Gustavo Costa Gomes Torres, Igor Cavalcante de Araújo, Ícaro Bezzera Queiroz Brito, Davi Bibiano Barboza, Erick de Andrade |
author_sort | de Melo, Gustavo Costa Gomes |
collection | PubMed |
description | Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems. |
format | Online Article Text |
id | pubmed-8126226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81262262021-05-17 A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application de Melo, Gustavo Costa Gomes Torres, Igor Cavalcante de Araújo, Ícaro Bezzera Queiroz Brito, Davi Bibiano Barboza, Erick de Andrade Sensors (Basel) Article Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems. MDPI 2021-05-10 /pmc/articles/PMC8126226/ /pubmed/34068743 http://dx.doi.org/10.3390/s21093293 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article de Melo, Gustavo Costa Gomes Torres, Igor Cavalcante de Araújo, Ícaro Bezzera Queiroz Brito, Davi Bibiano Barboza, Erick de Andrade A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title | A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title_full | A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title_fullStr | A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title_full_unstemmed | A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title_short | A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application |
title_sort | low-cost iot system for real-time monitoring of climatic variables and photovoltaic generation for smart grid application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8126226/ https://www.ncbi.nlm.nih.gov/pubmed/34068743 http://dx.doi.org/10.3390/s21093293 |
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