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A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms
A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891556/ https://www.ncbi.nlm.nih.gov/pubmed/31744105 http://dx.doi.org/10.3390/s19224998 |
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author | Romero-Ternero, MCarmen Oviedo-Olmedo, David Carrasco, Alejandro Luque, Joaquín |
author_facet | Romero-Ternero, MCarmen Oviedo-Olmedo, David Carrasco, Alejandro Luque, Joaquín |
author_sort | Romero-Ternero, MCarmen |
collection | PubMed |
description | A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning. |
format | Online Article Text |
id | pubmed-6891556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68915562019-12-18 A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms Romero-Ternero, MCarmen Oviedo-Olmedo, David Carrasco, Alejandro Luque, Joaquín Sensors (Basel) Article A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning. MDPI 2019-11-16 /pmc/articles/PMC6891556/ /pubmed/31744105 http://dx.doi.org/10.3390/s19224998 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Romero-Ternero, MCarmen Oviedo-Olmedo, David Carrasco, Alejandro Luque, Joaquín A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title | A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title_full | A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title_fullStr | A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title_full_unstemmed | A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title_short | A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms |
title_sort | distributed approach for estimating battery state-of-charge in solar farms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891556/ https://www.ncbi.nlm.nih.gov/pubmed/31744105 http://dx.doi.org/10.3390/s19224998 |
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