Optimizing solar cell models: a multi-objective parameter estimation algorithm for improved photovoltaic system performance

Accurate models of solar cells are required to improve the performance of solar photovoltaic (PV) systems. Due to a lack of precise parameters in the manufacturer's datasheet, the solar cell model is often inaccurate. Estimating the parameters needed improperly makes it impossible to build up a reliable solar PV cell model. This paper proposes an algorithm for estimating cell parameters by multi-objective optimization to solve this issue. Several optimizers attempted to address the suboptimal results of optimization due to local minima and premature convergence. This work aims to evaluate the effectiveness of the proposed algorithm with those other popular algorithms to understand its reliability. The efficiency of this algorithm is proven using empirical results and statistical figures. It has important features, including simplicity and high accuracy, which imply that the algorithm is better suited to estimating solar PV models when compared with other algorithms. This algorithm is robust as it is computationally efficient and easy to use, which makes this method applicable for solving a wide variety of problems related to solar energy.