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• After introducing the Laplacian strategy and Nelder-Mead simplex mechanism to the basic hunger games search, a novel LNMHGS algorithm is developed. • The proposed LNMHGS compares against the original HGS and other algorithms to optimize unknown parameters of static PV systems. • The LNMHGS method is used to extract SDM and DDM models' parameters on ST40, KC200GT and SM55 datasheet under different environmental conditions. • The results show the fast convergence and stable convergence of the proposed LNMHGS algorithm to identify parameters for PV models. Photovoltaic (PV) technology can convert solar energy to electric power, which is an essential tool for future years. Subsequently, several static solar PV models have been designed to simulate the current in a PV cell. However, the modeling process of PV systems requires extracting the unknown parameters of these cells, which can be modeled as an optimization problem. However, this is a very challenging task as it is multimodal and nonlinear. In this case, a Laplacian Nelder-Mead hunger games search (HGS) method, denoted as LNMHGS, is proposed for parameter extraction of static solar PV models. It realizes the equilibrium between exploitation and exploration by introducing the Laplacian strategy and Nelder-Mead simplex mechanism to hunger name search. LNMHGS compares against many recent methods and stands out by efficiently estimating static PV models' parameters. The simulation outcomes show that the root mean square error (RMSE) values and standard deviation offered by LNMHGS are smaller than other algorithms. Meanwhile, LNMHGS obtains the best performance both in different light conditions and at different temperature conditions. Therefore, LNMHGS is a promising, reliable, and feasible alternative for optimizing the parameters for PV systems. [ABSTRACT FROM AUTHOR]

• An enhanced political optimizer (QNMPO) is proposed to realize the extraction of photovoltaic models parameters. • The performance of the proposed QNMPO algorithm is compared with some well-known competing algorithms. • The stability performance of QNMPO was tested on three commercial PV models at different temperature levels and irradiation levels. • QNMPO exhibits better convergence speed and accuracy. Evaluating the PV system parameters is crucial for the more efficient utilization of sustainable solar energy. Due to PV systems' nonlinear and multimodal nature, efficient evaluation of PV system parameters is still a very challenging task. This paper proposes an improved political optimization algorithm, namely the QNMPO algorithm, which combines quantum rotation gate and Nelder-Mead simplex (NMs). It can be used to evaluate unknown parameters of PV systems and solar cells efficiently. In QNMPO, the quantum rotation gate method can rotate the population of agents to a more favorable position for finding the optimal solution, making the individuals in the middle of the population communicate more frequently and enhancing the population's diversity. In addition, the NMs method can improve the solution quality by better searching the neighborhoods near the optimal solution and then strengthen the advantage of local development of the algorithm so that it can converge to the global optimal solution faster. The developed QNMPO algorithm extracts the unknown parameters for single diode, double diode, triple diode and photovoltaic modules in this study. It is compared with some other representative algorithms. The experimental results show that QNMPO outperforms similar algorithms in convergence speed, solution accuracy, and better solution performance. QNMPO was then used to successfully extract the best parameter values for three real PV models (three commercial PV models (thin-film ST40, monocrystalline SM55, and multi crystalline KC200GT)) at different temperatures and different irradiance levels. The results also show that QNMPO performs consistently and accurately enough under varying temperatures and irradiation. Therefore, the QNMPO algorithm proposed in this paper can be a promising parameter extraction method for solar PV systems with good performance. [ABSTRACT FROM AUTHOR]