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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...

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Autores principales: Romero-Ternero, MCarmen, Oviedo-Olmedo, David, Carrasco, Alejandro, Luque, Joaquín
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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.
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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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