Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions

Accurate photovoltaic (PV) models are essential to optimize grid operations and dynamic energy management. This article proposes parameter extraction of solar PV models using the Artificial Humming bird Optimization (AHO) algorithm. The AHO algorithm is inspired by hummingbird flight dynamics and mimics hummingbird foraging behavior. Three objective functions are developed to minimize the root mean square difference between the experimental and estimated currents. The first objective function is based on the conventional RMSE, while the second is developed using the Lambert W function, and the third is developed using the iterate Newton–Raphson approach. The AHO algorithm has been utilized to determine the parameters for a basic single-diode model (SDM), a double-diode model (DDM), and a photovoltaic module. The AHO algorithm exhibits an average RMSE value of 7.2985 × 10 –04 for the SDM model and 7.4080 × 10 –04 for the DDM model. The proposed AHO algorithm's performance is compared to the findings of other algorithms reported in the literature.