An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.

Document Type : Research Studies

Authors

1 Computer Engineering and Control Systems Department., Faculty of Engineering., eL-Mansoura University., Mansoura., Egypt.

2 Computers Engineering and Control Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

3 Computers Engineering and Control Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt

Abstract

Because of the world's acute energy crisis, the need for renewable energy sources is increasing today. In recent years, Standalone photovoltaic systems have been widely used in remote regions, following the growth of the photovoltaic cell industry. The key features of the systems of photovoltaic (PV) used to ingather solar energy while reducing the gas emissions of the greenhouse, maintenance costs are low, reduced site-related restrictions as a mechanical noise reduction due to no moving parts. Nevertheless, Photovoltaic systems are suffering from comparatively poor conversion efficiency. Hence, a PV system needs maximum power point tracking (MPPT) of the solar array. Several factors affect the maximum resulted power as nonlinear behavior of PV systems, temperature and the level of solar radiation that complicate monitoring of the maximum power point (MPP). This paper represents an evolutionary optimization algorithm using improved particle swarm optimization (PSO) technique for MPPT. The proposed technique has achieved high power in the conditions of partial shading rather than the conventional (PSO). The results of the simulation showed that the strategy could be reliable in monitoring the global MPPT in PV systems.

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