A solar panel can be physically perfect yet mathematically misunderstood. This study finds a way to estimate the hidden numbers inside a panel more accurately, helping models predict how much power it can produce. The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic systems.

But existing models lack precise parameters, and the manufacturer’s datasheet does not provide the information needed for reliable modeling. Consequently, accurate parameter estimation becomes necessary: the missing numbers have to be inferred rather than simply read from the panel’s paperwork.

The proposed method is designed to estimate those cell parameters while overcoming local minima and premature convergence, which often lead to suboptimal results. The study aims to use a new parameter-estimation algorithm for three- and four-diode models of a solar cell.

Here is the everyday picture: finding the best settings is like searching for the lowest point in a landscape. A search can settle in a nearby hollow and miss a deeper one, so combining search strategies is meant to avoid that trap. The hybrid approach combines two algorithms for increased efficiency.

One handles exploration, searching broadly, while the other handles exploitation, refining a promising answer. The swarm-based algorithm has a tendency to get stuck in local minima. As a result, the hybrid method is not trapped in local minimums and is also enhanced in accuracy.

The combination is described as cost-effective because it does not require a large amount of computational power to achieve the desired result. The findings from the benchmark tests show that the suggested approach is more efficient than the others, with lower average results and less variation than the rest.

The benchmark results support the conclusion that the hybrid algorithm offers greater convergence, robustness, precision, and performance compared with the other algorithms. The study therefore says the hybrid algorithm yields better results than the other algorithms and can solve a wide range of complex problems efficiently and reliably.

For a mono-crystalline solar panel, the study estimates the unknown parameters of both three-diode and four-diode models. The tables and graph indicate that the proposed hybrid algorithm is much better than the compared algorithm. It is also reported as superior in convergence time, reliability, and memory requirements.

For a poly-crystalline solar panel, the proposed hybrid algorithm outperforms the compared algorithm by a wide margin. It also outperforms the standalone algorithm in convergence time, reliability, and memory usage, and the statistical tests support that conclusion.

For solar-cell parameter estimates, smaller error means the model is closer to the cell’s actual behaviour. The comparison reports these errors for both three-diode and four-diode descriptions, with most methods near zero while one produces visibly larger errors.

The study concludes that the hybrid method is a promising and effective way to extract solar photovoltaic cell parameters. The same approach could also be applied to other energy-optimization problems, including planning power generation, balancing costs, and scheduling energy in power systems.

The study’s hybrid search method estimates solar-cell behavior more accurately than the compared methods in its tests. Better estimates could make solar-energy designs easier to plan and improve.