Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic... Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms; genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy and Environmental Engineering Springer Journals

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

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Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Renewable and Green Energy
ISSN
2008-9163
eISSN
2251-6832
D.O.I.
10.1007/s40095-017-0252-6
Publisher site
See Article on Publisher Site

Abstract

Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms; genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

Journal

International Journal of Energy and Environmental EngineeringSpringer Journals

Published: Oct 28, 2017

References

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