Parameter Estimation of Three-Diode Photovoltaic Model Using Reinforced Learning-Based Parrot Optimizer with an Adaptive Secant Method

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.