Photovoltaic Module Parameter Estimation by Using Wild Horse Optimizer

The development of highly accurate models is essential for optimizing the performance and efficiency of photovoltaic (PV) modules, which are integral to renewable energy systems. In this context, metaheuristic algorithms have become indispensable tools for tackling complex engineering challenges, particularly in the areas of evaluation, simulation, and parameter estimation. These algorithms excel at finding optimal or near-optimal solutions for problems where traditional methods may struggle due to their complexity or computational intensity. This article presents a novel framework for estimating the parameters of PV modules using the Wild Horse Optimizer (WHO), a recently introduced metaheuristic algorithm known for its robustness and efficiency. Specifically, the framework is applied to the Double Diode Model (DDM) of a solar cell, a widely recognized model for representing the electrical behavior of PV modules. The simulation results clearly illustrate that the proposed framework based on WHO algorithm offers significant improvements in the accuracy and effectiveness of parameter estimation for PV modules, compared to Artificial Bee Swarm Optimization (ABSO), the Flower Pollination Algorithm (FPA), and several others, thereby validating the proposed approach.