Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm

Abstract

Building highly accurate model for solar cells and photovoltaic (PV) modules based on experimental data is vital for the simulation, evaluation, control, and optimization of PV systems. Powerful optimization algorithms are necessary to accomplish this task. In this study, a new optimization algorithm is proposed for efficiently and accurately estimating the parameters of solar cells and PV modules. The proposed algorithm is developed based on the flower pollination algorithm by incorporating it with the Nelder-Mead simplex method and the generalized opposition-based learning mechanism. The proposed algorithm has a simple structure thus is easy to implement. The experimental results tested on three different solar cell models including the single diode model, the double diode model, and a PV module clearly demonstrate the effectiveness of this algorithm. The comparisons with some other published methods demonstrate that the proposed algorithm is superior than most reported algorithms in terms of the accuracy of final solutions, convergence speed, and stability. Furthermore, the tests on three PV modules of different types (Multi-crystalline, Thin-film, and Mono-crystalline) suggest that the proposed algorithm can give superior results at different irradiance and temperature. The proposed algorithm can serve as a new alternative for parameter estimation of solar cells/PV modules.