Improving the Efficiency of Solar Systems by Tracking the MPP under Different Test Conditions

The great technological development, the increase in the number of factories, and the large population growth led to an increase in the demand for the consumption of electric energy that we get from traditional methods (fossil fuels). Moreover, the global shortage in fossil fuel sources and their high costs, the global financial and economic crisis, and the harmful emissions it causes for the environment have made researchers look for electrical energy from alternative and environmentally friendly sources. As a renewable energy, solar energy is considered one of the most important sources of electrical energy today because it is easy to obtain at a low cost. However, this type of energy suffers from low efficiency and is greatly affected by changing weather conditions. To address this problem, several techniques have been proposed by research groups, and MPPT is one of those techniques that has been frequently used in recent years to extract maximum power from solar panels despite the instability in weather conditions. This technique can also generate pulses to control the DC-DC boost converter to provide a certain level of voltage. In this paper, three algorithms, namely Perturbation and Observation (P&O), Fuzzy Logic Controller (FLC), and Particle Swarm Optimization (PSO) are modified and applied in the MPPT technology to control the duty cycle of a DC-DC converter. The photovoltaic system consisting of MPPT technology, solar panels, and a DC-DC boost converter was simulated using MATLAB/Simulink. The performances of the three algorithms were compared to determine the best one that guarantees the highest efficiency under multiple test conditions. The simulation results show that PSO was a better performer than others with (99.32%, 97.02%, and 98.33%, respectively).