10.1007/s40095-017-0252-6

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

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  • Mohcene Bechouat*1,
  • Abdelaziz Younsi1,
  • Moussa Sedraoui1,
  • Youcef Soufi2,
  • Laatra Yousfi3,
  • Ismail Tabet4,
  • Khaled Touafek4,
  1. Laboratoire des télécommunications, Université 8 Mai 1945 Guelma, Guelma, DZ
  2. Labget Laboratory, Department of Electrical Engineering, University of Tebessa, Tebessa, DZ
  3. Laboratory Inverses Problems: Modeling, Information and Systems (PI: MIS), University of Tebessa, Tebessa, DZ
  4. Unité de Recherche Appliquée en Énergies Renouvelables, URAER, Centre de Développement des Énergies Renouvelables, CDER, Ghardaïa, 47133, DZ
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Published in Issue 2017-10-28

How to Cite

Bechouat, M., Younsi, A., Sedraoui, M., Soufi, Y., Yousfi, L., Tabet, I., & Touafek, K. (2017). Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods. International Journal of Energy and Environmental Engineering, 8(4 (December 2017). https://doi.org/10.1007/s40095-017-0252-6
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Abstract

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.

Keywords

  • Photovoltaic system,
  • Photovoltaic thermal system,
  • Modelization,
  • Identification,
  • Genetic algorithm,
  • Particle swarm optimization algorithm
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