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

Autor: Florencia Almonacid Cruz, J. Canada-Bago, M. A. Gadeo-Martos, Antonio Jesus Yuste-Delgado, J. A. Fernandez-Prieto
Rok vydání: 2019
Předmět:
Control and Optimization
Maximum power principle
Computer science
020209 energy
Energy Engineering and Power Technology
02 engineering and technology
lcsh:Technology
0202 electrical engineering
electronic engineering
information engineering
Fuzzy rule based systems
Concentrator photovoltaic
Electrical and Electronic Engineering
ad hoc data-driven generation
Engineering (miscellaneous)
Adaptive neuro fuzzy inference system
Fuzzy rule
Artificial neural network
lcsh:T
Renewable Energy
Sustainability and the Environment
business.industry
adaptive neuro-fuzzy inference system
Control engineering
Fuzzy control system
fuzzy rule-based systems
021001 nanoscience & nanotechnology
Knowledge base
high concentrator photovoltaic modules
0210 nano-technology
business
artificial neural network
maximum power prediction
Energy (miscellaneous)
Zdroj: Energies, Vol 12, Iss 3, p 567 (2019)
Energies
Volume 12
Issue 3
ISSN: 1996-1073
DOI: 10.3390/en12030567
Popis: 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 &ldquo
Adaptive neuro-fuzzy inference system&rdquo
and the &ldquo
Ad Hoc data-driven generation&rdquo
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.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje