Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms
Solar energy is a significant source of energy due to its accessibility and cleanliness. Photovoltaic (PV) systems are among the most efficient methods for converting solar energy into electrical energy. However, these systems are highly sensitive to environmental factors such as temperature and glo...
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2024-06-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| author | M. Sundar Rajan |
| author_facet | M. Sundar Rajan |
| author_sort | M. Sundar Rajan |
| collection | DOAJ |
| description | Solar energy is a significant source of energy due to its accessibility and cleanliness. Photovoltaic (PV) systems are among the most efficient methods for converting solar energy into electrical energy. However, these systems are highly sensitive to environmental factors such as temperature and global irradiance. Consequently, accurate modeling of PV modules is essential for improving system operation and profitability. Optimizing PV panel performance is critical for enhancing solar energy efficiency, which necessitates precise and reliable parameter evaluation of these systems. Various optimization techniques exist, including intelligent methods that incorporate functions, constraints, contributions, mathematical models, and analysis methods. Both contemporary and traditional generation techniques are analyzed, with hybrid algorithms gaining popularity due to their reduced computational time. This paper addresses the parameter estimation of four distinct PV panel models—PV-RTC, PV-PWP 201, PV-STM6 40/36, and PV-STP6 120/36—using a range of meta-heuristic optimization algorithms. The centerpiece of this study is the introduction of a novel hybrid algorithm, HPSGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) techniques. Our findings indicate that the HPSGWO algorithm achieves the lowest error rates and the highest efficiency, accuracy, and convergence speed across all tested PV models. This method was compared against other well-known algorithms such as Artificial Bee Colony (ABC), Dragonfly Algorithm (DA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), Composite Grey Wolf Optimizer (CGWO), and the standalone PSO and GWO methods. This study establishes the HPSGWO algorithm as a superior method for the parameter estimation of PV panels, highlighting its potential to improve the accuracy and reliability of solar energy systems. The enhanced performance of the HPSGWO algorithm suggests its broader applicability for optimizing other complex systems where robust and precise parameter evaluation is critical. |
| format | Article |
| id | doaj-art-225d60f3b7ca44b2ac4f2389b366dd24 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Bilijipub publisher |
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| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-225d60f3b7ca44b2ac4f2389b366dd242025-02-12T08:47:56ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-06-01003028310510.22034/aeis.2024.460309.1201199136Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic AlgorithmsM. Sundar Rajan0Faculty of Electrical and Computer Engineering, Arba Minch Institute of Technology, Arba Minch University, EthiopiaSolar energy is a significant source of energy due to its accessibility and cleanliness. Photovoltaic (PV) systems are among the most efficient methods for converting solar energy into electrical energy. However, these systems are highly sensitive to environmental factors such as temperature and global irradiance. Consequently, accurate modeling of PV modules is essential for improving system operation and profitability. Optimizing PV panel performance is critical for enhancing solar energy efficiency, which necessitates precise and reliable parameter evaluation of these systems. Various optimization techniques exist, including intelligent methods that incorporate functions, constraints, contributions, mathematical models, and analysis methods. Both contemporary and traditional generation techniques are analyzed, with hybrid algorithms gaining popularity due to their reduced computational time. This paper addresses the parameter estimation of four distinct PV panel models—PV-RTC, PV-PWP 201, PV-STM6 40/36, and PV-STP6 120/36—using a range of meta-heuristic optimization algorithms. The centerpiece of this study is the introduction of a novel hybrid algorithm, HPSGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) techniques. Our findings indicate that the HPSGWO algorithm achieves the lowest error rates and the highest efficiency, accuracy, and convergence speed across all tested PV models. This method was compared against other well-known algorithms such as Artificial Bee Colony (ABC), Dragonfly Algorithm (DA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), Composite Grey Wolf Optimizer (CGWO), and the standalone PSO and GWO methods. This study establishes the HPSGWO algorithm as a superior method for the parameter estimation of PV panels, highlighting its potential to improve the accuracy and reliability of solar energy systems. The enhanced performance of the HPSGWO algorithm suggests its broader applicability for optimizing other complex systems where robust and precise parameter evaluation is critical.https://aeis.bilijipub.com/article_199136_37d432c00fa418cac777daa6437f8c7d.pdfsolar cellphotovoltaichybrid hpsgwo algorithmoptimization |
| spellingShingle | M. Sundar Rajan Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms Advances in Engineering and Intelligence Systems solar cell photovoltaic hybrid hpsgwo algorithm optimization |
| title | Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms |
| title_full | Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms |
| title_fullStr | Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms |
| title_full_unstemmed | Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms |
| title_short | Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms |
| title_sort | optimizing photovoltaic panel performance a comparative study of meta heuristic algorithms |
| topic | solar cell photovoltaic hybrid hpsgwo algorithm optimization |
| url | https://aeis.bilijipub.com/article_199136_37d432c00fa418cac777daa6437f8c7d.pdf |
| work_keys_str_mv | AT msundarrajan optimizingphotovoltaicpanelperformanceacomparativestudyofmetaheuristicalgorithms |