Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy
Автор: M.T. Makhloufi, M.S. Khireddine, Y. Abdessemed, A. Boutarfa
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 12 vol.6, 2014 года.
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Photovoltaic generation is the technique which uses photovoltaic cell to convert solar energy to electric energy. Nowadays, PV generation is developing increasingly fast as a renewable energy source. However, the disadvantage is that PV generation is intermittent because it depends considerably on weather conditions. This paper proposes an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and solar irradiation conditions. In this paper, a simulation study of the maximum power point tracking (MPPT) for a photovoltaic system using an artificial neural network is presented. The system simulation is elaborated by combining the models established of solar PV module and a DC/DC Boost converter. Finally performance comparison between artificial neural network controller and Perturb and Observe method has been carried out which has shown the effectiveness of artificial neural networks controller to draw much energy and fast response against change in working conditions.
Solar Energy, Photovoltaic, MPPT, P&O, Boost Converter, Artificial Neural Network
Короткий адрес: https://sciup.org/15010633
IDR: 15010633
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