Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading
This study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. The GA-based FPPT algorithm exhibits superior performance in power output, tracking accuracy, and convergence speed compa...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00058.pdf |
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| author | Ouatman Hamid Boutammachte Nour-Eddine |
| author_facet | Ouatman Hamid Boutammachte Nour-Eddine |
| author_sort | Ouatman Hamid |
| collection | DOAJ |
| description | This study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. The GA-based FPPT algorithm exhibits superior performance in power output, tracking accuracy, and convergence speed compared to conventional methods. In contrast, the PSO-based FPPT algorithm is designed to mitigate oscillations around steady-state operating points under partial shading conditions (PSC) by incorporating power limitation control. This allows the FPPT-PSO algorithm to effectively track the global maximum power point (GMPP) without fluctuating around steady-state points. The findings of this comparative analysis highlight the significance of adaptive FPPT algorithms in enhancing system reliability and maximizing power extraction under dynamic environmental conditions. The GA-based approach excels in optimizing power generation metrics, while the PSO-based approach specializes in maintaining stability and precision under challenging operational scenarios such as partial shading. By exploring the strengths and limitations of each algorithm, this study provides valuable in-sights into the selection and implementation of FPPT strategies in PV systems. |
| format | Article |
| id | doaj-art-2533d903b9aa42718b07f68ba92cc9c2 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-2533d903b9aa42718b07f68ba92cc9c22025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010005810.1051/e3sconf/202560100058e3sconf_icegc2024_00058Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial ShadingOuatman Hamid0Boutammachte Nour-Eddine1Department of Energy, ENSAM, Moulay Ismail UniversityDepartment of Energy, ENSAM, Moulay Ismail UniversityThis study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. The GA-based FPPT algorithm exhibits superior performance in power output, tracking accuracy, and convergence speed compared to conventional methods. In contrast, the PSO-based FPPT algorithm is designed to mitigate oscillations around steady-state operating points under partial shading conditions (PSC) by incorporating power limitation control. This allows the FPPT-PSO algorithm to effectively track the global maximum power point (GMPP) without fluctuating around steady-state points. The findings of this comparative analysis highlight the significance of adaptive FPPT algorithms in enhancing system reliability and maximizing power extraction under dynamic environmental conditions. The GA-based approach excels in optimizing power generation metrics, while the PSO-based approach specializes in maintaining stability and precision under challenging operational scenarios such as partial shading. By exploring the strengths and limitations of each algorithm, this study provides valuable in-sights into the selection and implementation of FPPT strategies in PV systems.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00058.pdf |
| spellingShingle | Ouatman Hamid Boutammachte Nour-Eddine Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading E3S Web of Conferences |
| title | Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading |
| title_full | Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading |
| title_fullStr | Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading |
| title_full_unstemmed | Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading |
| title_short | Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading |
| title_sort | comparative study of genetic algorithms and particle swarm optimization for flexible power point tracking in photovoltaic systems under partial shading |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00058.pdf |
| work_keys_str_mv | AT ouatmanhamid comparativestudyofgeneticalgorithmsandparticleswarmoptimizationforflexiblepowerpointtrackinginphotovoltaicsystemsunderpartialshading AT boutammachtenoureddine comparativestudyofgeneticalgorithmsandparticleswarmoptimizationforflexiblepowerpointtrackinginphotovoltaicsystemsunderpartialshading |