An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.
Saad, H., ElKsas, M., Saraya, S., Abdelsalam, M. (2021). An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.. EKB Journal Management System, 46(1), 10-20. doi: 10.21608/bfemu.2021.146311
Hend Samir Saad; Mohamed Shreif ElKsas; Sabry Fouad Saraya; Mohamed Moawad Abdelsalam. "An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.". EKB Journal Management System, 46, 1, 2021, 10-20. doi: 10.21608/bfemu.2021.146311
Saad, H., ElKsas, M., Saraya, S., Abdelsalam, M. (2021). 'An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.', EKB Journal Management System, 46(1), pp. 10-20. doi: 10.21608/bfemu.2021.146311
Saad, H., ElKsas, M., Saraya, S., Abdelsalam, M. An Improved Particle Swarm Optimization Algorithm for Maximum Power Point Tracking of Photovoltaic Cells in Normal and under Partial Shading Conditions.. EKB Journal Management System, 2021; 46(1): 10-20. doi: 10.21608/bfemu.2021.146311

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

MEJ- Mansoura Engineering Journal
Article 24, Volume 46, Issue 1, March 2021, Page 10-20  XML PDF (1.51 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.146311
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Authors
Hend Samir Saad email 1; Mohamed Shreif ElKsas2; Sabry Fouad Saraya3; Mohamed Moawad Abdelsalam2
1Computer Engineering and Control Systems Department., Faculty of Engineering., eL-Mansoura University., Mansoura., Egypt.
2Computers Engineering and Control Systems Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3Computers 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.
Keywords
Maximum Power Point Tracking; Photovoltaic cell; Particle Swarm Optimization; Partial shading
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