A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

In this paper, a modified version of a recent optimization algorithm called gradient-based optimizer (GBO) is proposed with the aim of improving its performance. Both the original gradient-based optimizer and the modified version, MGBO, are utilized for estimating the parameters of Photovoltaic models. The MGBO has the advantages of accelerated convergence rate as well as avoiding the local optima. These features make it compatible for investigating its performance in one of the nonlinear optimization problems like Photovoltaic model parameters estimation. The MGBO is used for the identification of parameters of different Photovoltaic models; single-diode, double-diode, and PV module. To obtain a generic Photovoltaic model, it is required to fit the experimentally obtained data. During the optimization process, the unknown parameters of the PV model are used as a decision variable whereas the root means squared error between the measured and estimated data is used as a cost function. The results verified the fast conversion rate and precision of the MGBO over other recently reported algorithms in solving the studied optimization problem.

Automated summary

The modified gradient-based optimizer (MGBO) is proposed to improve performance in estimating the parameters of Photovoltaic models. MGBO accelerates convergence rate and avoids local optima, making it suitable for nonlinear optimization problems like Photovoltaic model parameters estimation. The results show that MGBO has fast conversion rate and precision compared to other algorithms in solving the optimization problem.