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

Manuel Angel Gadeo-Martos, Antonio Jesús Yuste-Delgado, Florencia Almonacid Cruz, Jose-Angel Fernandez-Prieto and Joaquin Canada-Bago
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Manuel Angel Gadeo-Martos: 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
Antonio Jesús Yuste-Delgado: 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
Florencia Almonacid Cruz: IDEA Solar Energy Research Group, Center of Advanced Studies in Energy and Environment, University of Jaén, Campus las Lagunillas, C.P. 23071 Jaén, Spain
Jose-Angel Fernandez-Prieto: 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
Joaquin Canada-Bago: 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

Energies, 2019, vol. 12, issue 3, 1-22

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.

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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