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Energies 2019, 12(3), 567; https://doi.org/10.3390/en12030567

Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems

1
Telematic Engineering System Research Group, CEATIC Center of Advanced Studies in Information and Communication Technologies, University of Jaén, Campus Científico-Tecnológico de Linares, C.P. 23700 Linares, Spain
2
IDEA Solar Energy Research Group, Center of Advanced Studies in Energy and Environment, University of Jaén, Campus las Lagunillas, C.P. 23071 Jaén, Spain
*
Author to whom correspondence should be addressed.
Received: 28 November 2018 / Revised: 31 January 2019 / Accepted: 31 January 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Alternative Sources of Energy Modeling and Automation)
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Abstract

Currently, there is growing interest in the modeling of high concentrator photovoltaic modules, due to the importance of achieving an accurate model, to improve the knowledge and understanding of this technology and to promote its expansion. In recent years, some techniques of artificial intelligence, such as the Artificial Neural Network, have been used with the goal of obtaining an electrical model of these modules. However, little attention has been paid to applying Fuzzy Rule-Based Systems for this purpose. This work presents two new models of high concentrator photovoltaics that use two types of Fuzzy Systems: the Takagi-Sugeno-Kang, characterized by the achievement of high accuracy in the model, and the Mamdani, characterized by high accuracy and the ease of interpreting the linguistic rules that control the behavior of the fuzzy system. To obtain a good knowledge base, two learning methods have been proposed: the “Adaptive neuro-fuzzy inference system” and the “Ad Hoc data-driven generation”. These combinations of fuzzy systems and learning methods have allowed us to obtain two models of high concentrator photovoltaic modules, which include two improvements over previous models: an increase in the model accuracy and the possibility of deducing the relationship between the main meteorological parameters and the maximum power output of a module. View Full-Text
Keywords: artificial neural network; fuzzy rule-based systems; adaptive neuro-fuzzy inference system; ad hoc data-driven generation; high concentrator photovoltaic modules; maximum power prediction artificial neural network; fuzzy rule-based systems; adaptive neuro-fuzzy inference system; ad hoc data-driven generation; high concentrator photovoltaic modules; maximum power prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gadeo-Martos, M.A.; Yuste-Delgado, A.J.; Almonacid Cruz, F.; Fernandez-Prieto, J.-A.; Canada-Bago, J. Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems. Energies 2019, 12, 567.

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