article · Scientific Reports
Accurately determining unknown parameters in photovoltaic models is vital for understanding solar cell behaviour, but existing computational methods often struggle with slow convergence and trap solutions in local optima. A new technique called the hybrid Kepler optimization algorithm addresses these challenges by enhancing the standard Kepler optimization algorithm. It incorporates a ranking-based update mechanism to boost exploration and avoid local traps, alongside an exploitation improvement mechanism to accelerate convergence towards optimal solutions. Tested on single, double, and triple diode models, the method underwent validation using experimental data from the RTC France solar cell and five commercial photovoltaic modules, including Photowatt-PWP201 and Ultra 85-P. Extensive comparisons with other optimization techniques confirm that this hybrid approach delivers superior accuracy, efficiency, and stability, offering a reliable alternative for solar cell parameter estimation.
Solar power systems rely on accurate digital models to simulate, monitor, and optimise their energy output. Because traditional calculation tools struggle with the complex mathematics of solar cells, improved algorithmic methods help engineers accurately represent how solar panels function under varied operational settings, supporting better performance assessments and design decisions across the solar energy sector.
This algorithm provides an applied computational tool for solar module manufacturers, solar system designers, and performance engineers who need to model panel behaviour accurately. By successfully validating the approach on real test modules such as Photowatt-PWP201 and Ultra 85-P, the technique demonstrates applied readiness for integration into photovoltaic simulation, monitoring, and diagnostic software platforms.
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The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA's exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models.
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DOI: 10.1038/s41598-024-52416-6
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