Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models

Accurate parameter estimation in photovoltaic (PV) models is essential for optimizing solar energy systems, enhancing their efficiency, and ensuring precise performance predictions. This paper proposes a novel Improved version of Rime Metaheuristic Optimization (RMO) influenced by rime growth and combined with Quadratic Interpolation Learning (QIL) technique for the simulation and design of the triple-Diode Model (DM). This novel combination seeks to provide a more accurate perspective in the field of solar energy optimization by managing the complexities of PV module characterization with greater flexibility and resilience. By meticulously replicating the distinctive features of both processes, the hard-rime puncture and soft-rime searching are disclosed. The QIL technique improves the search process by selecting three different rime particles rather than relying solely on the current best solution. This selection allows for a more diverse set of candidate solutions, fostering better exploration and reducing premature convergence to local optima. By leveraging quadratic interpolation, QIL adjusts the solution updates in a flexible and nonlinear manner, enabling a more precise and adaptive parameter estimation process. QIL's capacity to adjust its quadratic function in a flexible and non-linear way makes it easier to navigate complex terrain. The novel IRMO as well as the original RMO are developed for predicting PV parameters for the triple-diode model (DM) of the three distinct PV modules which are Photowatt PWP201, STM6-40/36, and R.T.C France. In accordance with other published publications, the results of the suggested IRMO are also compared with those of contemporary algorithms. According to the results of the simulation, the upgraded IRMO shows significant average improvements of 49.56%, 62.56%, and 34.15% for the three modules, correspondingly.