PV Module Optimum Operation ModelingJournal of Power Technologies (2014)
This paper proposes first photovoltaic (PV) module theoretical modeling based on the Schott ASE-300-DGF PV panel as a practical basis for checking and verifying the modeling process. This is done with the aid of an equivalent electric circuit with a diode and an electric model with moderate complexity. It is modeled at nominal conditions at 25◦C, and 1 kW/m2 with I-V curves at (0◦C, 25◦C, 50◦Cg, 75◦C), also power and irradiance. General modeling in more probable situations for variable values of temperature and irradiance is proposed using Artificial Neural Networks (ANN). The inputs of this model are Irradiance and Temperature; the outputs are: Module Voltage, Current, and Power. All characteristics are well depicted in 3-D figures. Then, it proposes the identification of the maximum power point (MPP) function for the (PV) module using a genetic algorithm (GA). This function efficiently picks the peaks of PV power curves as the objective function and two variables as arguments (Vmp and Imp) with nonlinear constraints and variables boundaries. This function generates reference values to drive the tracking system in the PV system at optimum operation and is deduced with the aid of ANN too. This is done with probable situations for various values of temperature and irradiance to obtain corresponding voltage and current at maximum power. The simulation results at MPP are well depicted in 3-D figures to be used as training or learning data for the ANN model. The results obtained are sufficiently accurate to apply the models to control the PV systems for tracking the optimal power points.
- Neural Network,
- Maximum power,
- Genetic Algorithm,
Citation InformationAdel El Shahat. "PV Module Optimum Operation Modeling" Journal of Power Technologies Vol. 94 Iss. 1 (2014) p. 50 - 66 ISSN: 2083-4187
Available at: http://works.bepress.com/adel-el-shahat/9/