Publications

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.

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 for depending 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 radiation conditions. This method uses a fuzzy logic controller applied to a DC-DC boost converter device. A photovoltaic system including a solar panel, a DC-DC converter, a Fuzzy MPP tracker and a resistive load is modeled and simulated. Finally performance comparison between fuzzy logic controller and Perturb and Observe method has been carried out which has shown the effectiveness of fuzzy logic controller to draw much energy and fast response against change in working conditions.

This paper presents a set of artificial neural network models (ANN) to estimate daily global solar radiation (GSR) on a horizontal surface using meteorological variables: (mean daily extraterrestrial solar radiation intensity G₀, the maximum possible sunshine hours S₀, mean daily relative humidity H, mean daily maximum air temperature T, mean daily atmospheric pressure P and wind speed Vx) for Djelfa city in Algeria. In order to consider the effect of the different meteorological parameters on daily global solar radiation prediction, four following combinations of input features are considered: 1) Day of the year, G₀, S₀, T and Vx. 2) Day of the year, G₀, S₀, T, P and Vx. 3) Day of the year, G₀, S₀, T, H, P and Vx. 4) Day of the year, G₀, S₀, T, H and Vx. These models were compared using three evaluation criteria: Mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The results show that the two parameters: atmospheric pressure and relative humidity affect the prediction output of global solar radiation. In addition, the results show that the relative humidity is the most important features influencing the prediction performance. It can be concluded that fourth model can be used for forecasting daily global solar radiation in other locations in Algeria.