A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using Metaheuristic Optimization Algorithms

Abstract

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.