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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. [ABSTRACT FROM AUTHOR]

The metaheuristic algorithms and their hybridization have been utilized successfully in the past to extract the parameters of photovoltaic (PV) cells and panels. The novelty of the paper consists of proposing the black widow optimization algorithm (BWOA) for the first time to identify the parameters of the two photovoltaic cells RTC France, amorphous silicon (aSi), and two photovoltaic panels PWP201, PVM 752 GaAs. The single-diode model (SDM) and double-diode model (DDM) for analyzing the PVs are considered. The performance of the BWOA is verified using four statistical tests: the root mean square error, which is the primary tool, the mean relative error, the mean bias error, and the coefficient of determination. The research results of this study are as follows: BWOA gave the same results, or very slightly better, for RTC and PWP201 for SDM in comparison with the best algorithms from the specialized literature; for all the other cases, BWOA has substantially better results, especially for PVM 752 GaAs, where the improvements in RMSE are: 16.5%, for PWP201: 6.25%, and for aSi: 5.3%, all for the DDM; the computing time is around 2 s, which is one of the lowest durations. A consistent study is made to optimize the accuracy and computational time in function of the number of iterations and population. [ABSTRACT FROM AUTHOR]

Effective and efficient systems that calculate the parameters of PV models to be optimal have attracted great interest from many researchers. In this study, a novel algorithm approach is proposed to improve the search performance of the Harris Hawks optimization (HHO) algorithm based on fractal maps. The random parameter that is effective in besiege of the prey in the exploration and exploitation stages of the HHO algorithm is adjusted using the fractal henon chaotic map. Unimodal, multimodal and fixed-dimension benchmark functions were used to verify the quality and efficiency of the proposed Fractional Henon Chaotic Harris Hawks Optimization (FCHHHO) algorithms and variants in providing optimal solutions. Moreover, RTC France photovoltaic cell and Photowatt-PWP 201 photovoltaic models with single, double and three-diodes have been determined accurately, stably and reliably. Comparative and statistical results obtained from the developed algorithms showed that the algorithms have superior performance with regard to accuracy and robustness in comparison with their variants and compared with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

The first issue in the optimal photovoltaic system design is providing an accurate PV model that emulates the system behaviour under several environmental conditions. The accuracy of the photovoltaic model stands on its identified parameters that are mainly based on the executed optimization technique and the employed objective function. As per the literature, two methodologies have been applied for computing the estimated current in the objective function, detecting the most efficient one is the first step for achieving high qualified and precise solutions. Motivated by that, we investigate the two objective functions with considering several novel optimization algorithms. The implemented algorithms are marine predators algorithm, Slime mould algorithm, atom search optimization, Political Optimizer, Parasitism–Predation algorithm as well as harris hawk optimizer and salp swarm algorithm. The Lambert function forms have been used for validating the results. Several profiles of the experimental datasets are measured under different levels of temperature and irradiation conditions to identify the single, double and three diode models parameters of the RTC France solar cell and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel. The main findings show that, applying Newton–Raphson while computing the estimated current in the objective function enhances the algorithms performance to provide the more precise and accurate parameters in comparison with using the measured current and solve the photovoltaic model equation linearly. Moreover, the marine predators algorithm confirms the quality of its solutions and provides a better representation for the photovoltaic datasets with high stability based on the lambert forms and the statistical analyses. • Investigate two objective functions to determine the more accurate to estimate PV parameters. • Propose a new five techniques, including MPA, SMA, ASO, PO, and PPA, as PV parameter estimation methods. • Evaluate the tested algorithms and two objectives using different diode models. • Apply the Lambert function to report the accuracy of the identified parameters and reliability of the objective function. • Assess the quality of the proposed five algorithms through five experimentally measured datasets. [ABSTRACT FROM AUTHOR]