Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

In the developing landscape of photovoltaic (PV) technology, accuracy in simulating PV cell behaviour is dominant for enhancing energy conversion efficiency. This study introduces a new approach for parameter estimation in the three-diode PV model, a basis in the representation of PV cell characteristics. The methodology combines a reinforced learning-based parrot optimizer (RLPO) with an adaptive secant method (ASM) to fine-tune the parameters governing the PV model. The RLPO algorithm is inspired by the mimetic ability of parrots, i.e., foraging, staying, communicating, and fear noticed in trained Pyrrhura Molinae parrots, as it influences reinforced learning mechanisms to adaptively explore and exploit the search space for optimal parameter sets. Simultaneously, the ASM enhances the convergence rate through an iterative adjustment mechanism, responding to the curvature of the objective function, thereby ensuring accuracy in parameter estimation. The combination of the RLPO and ASM addresses the complexities and non-linearities inherent in the PV model, offering a robust framework for parameter estimation. Through extensive simulations, the proposed method demonstrated superior performance in terms of accuracy, convergence speed, and reliability when compared to existing algorithms. The empirical results emphasize the effectiveness of integrating a reinforced learning strategy with an adaptive method in handling the details of PV model parameterization. These outcomes show that the algorithm can handle issues related to optimization in PV systems, opening the door to progress in sustainable energy technologies.