Abstract
The modeling accuracy of photovoltaic (PV) modules is essential nowadays due to the spread of PV power plant installation. Thus, precise PV modeling is vital as it ensures the optimal design, reliable performance prediction, and efficient energy management in solar power systems. The three-diode PV modeling is thus a suitable solution due to its precision and accuracy. However, it is complicated and includes nine unidentified parameters. The Puma optimization algorithm is presented in this paper for utilization in extraction and the optimization of nine unknown PV module parameters. The suggested methodology is applied to two commercial PV modules: the Kyocera KC200GT multi-crystalline and the Canadian PV monocrystalline modules CS6K280M. To ensure the superiority of the Puma algorithm, its results are compared with others resulting from more than four optimization algorithms, which include Artificial Electric Field Algorithm, Northern Goshawk Optimization, Grey Wolf Optimization, Coati Optimization Algorithm, and Particle Swarm Optimization algorithms. The Puma's parameter extraction precision is further approved by the close agreement between the measured and estimated characteristics curves, extending its validation to variable temperature and irradiance level variations scenarios. The study establishes the Puma algorithm as a robust tool for parameter determination in the three-diode PV model. It opens new avenues for application in other complex optimization problems in renewable energy systems.
| Original language | English |
|---|---|
| Article number | 103170 |
| Journal | Ain Shams Engineering Journal |
| Volume | 15 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2024 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Optimization techniques, Photovoltaic, renewable energy
- Parameter extraction
- Sustainable energy
- puma optimizer, three-diode PV
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