Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems

The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem-based optimization (LAEO), which improves upon the original artificial ecosystem-based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader-based mutation-selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three-diode PV models, as well as integral-order and fractional-order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three-diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. This paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem-based optimization (LAEO), which improves upon the original artificial ecosystem-based optimization (AEO). image