Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm

Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges simulation design systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control must be adjusted with many existing algorithms, making them difficult to use. In problems, these combined or hybridized, which results more complex time-consuming algorithms. This paper presents new artificial parameter-less algorithm (APLO) parameter estimation PV models. New mutation operators are designed proposed algorithm. APLO’s exploitation phase enhanced by each individual searching best solution this updating operator. Moreover, current best, old individual’s position utilized differential term operator assist exploration control convergence speed. The uses random step length based on normal distribution ensure population diversity. We present comparative study using APLO well-known meta-heuristic as grey wolf optimization, salp swarm algorithm, JAYA, teaching-learning colliding body well three major parameter-based evolution, genetic particle estimate modules. revealed that could provide excellent exploration–exploitation balance consistency during iterations. Furthermore, shows high reliability accuracy identifying