Bishop model parameter estimation in photovoltaic cells using metaheuristic optimization techniques.

In this paper, the estimation of solar cell parameters in the Bishop model was developed. The parameter estimation was formulated as an optimization problem. Several techniques were selected and compared, such as, Ant Lion Optimizer (ALO), Arithmetic Optimization Algorithm (AOA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Salp Swarm Algorithm (SSA), and Vortex Search Algorithm (VSA). All these techniques were selected because of their solutions' quality when applied to estimating parameters in photovoltaic models. The Bishop model was selected since it can represent the behavior of a photovoltaic cell in direct and reverse modes of operation (when producing and consuming power, respectively). For the validation stage, two technologies were selected (monocrystalline and polycrystalline). All the techniques were executed 100 times and compared based on their estimated objective function and computation time (mean value and standard deviation). Then, the four techniques with the best results were selected and further analyzed employing the I–V curve reconstructions and the results of some critical points, such as short-circuit current and maximum power point. • A procedure to obtain cell I–V curve that includes information in the first and second quadrants of a commercial panel. • A cell parameter estimation strategy based on optimization techniques applied to the Bishop model. • Analysis of estimation results based on response repeatability and execution times. • Analysis of the quality of some important points in the I–V curve. [ABSTRACT FROM AUTHOR]