An interval branch and bound global optimization algorithm for parameter estimation of three photovoltaic models.

• This paper is concerned with solving the parameter estimation problem of PV cells. • An interval branch and bound global optimization algorithm is applied for estimating the unknown PV parameters. • Accuracy of the proposed algorithm is proved against other optimization algorithms. • The performance of two fitness functions, Maximal Absolute Error and Root Mean Square Error, is assessed. In this paper, an interval branch and bound algorithm is proposed to estimate the parameters for three models of photovoltaic (PV) cells. These models are a single diode model (SDM), a double diode model (DDM) and a three diode model (TDM). The minimization of Maximal Absolute Error (MAE) based on the infinite norm is evaluated and compared with the common used objective function called the Root Mean Square Error (RMSE). The MAE aims to minimize the average error on a set of experimental data. In this regard, the parameter estimation is reformatted. The performance of the proposed optimization algorithm is analyzed and tested on all three models. It has good convergence and robust statistical analysis at different operating conditions as well as for various models. The estimated performance characteristics for current-voltage (I - V) and power-voltage (P-V) of the tested cells are very close to the experimental data and compete with existing works in the literature. [ABSTRACT FROM AUTHOR]