Parameter Estimation of Solar PV Models with an Artificial Humming Bird Optimization Algorithm using Various Objective Functions

Abstract

Accurate photovoltaic (PV) models are essential to optimize grid operations and dynamic energy management. This article proposes parameter estimation of solar PV models using Artificial Humming bird Optimization (AHO) algorithm. The AHO algorithm is inspired by hummingbird flight dynamics and mimics hummingbird foraging behaviour. 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 iterative Newton Raphson approach. The proposed AHO algorithm is applied to estimate the parameters for a single diode model (SDM), a double diode model (DDM), and a PV module. The AHO algorithm achieves an RMSE of 7.2985E-04 for SDM and 7.4080E-04 for DDM. The proposed AHO algorithm's performance is compared to the findings of other algorithms reported in the literature.