Journal Article

Iterative Parameter Estimation for Photovoltaic Cell Models by Using the Hierarchical Principle

Ji, Yan, Meng, Xiangxiang, Wang, Junwei

2022

Overview

This paper considers the parameter estimation problems of photovoltaic cell models. In order to overcome the complexity of the model structure, through applying the hierarchical identification principle and decomposing the photovoltaic cell model into two sub-models with a smaller number of parameters. The nonlinear identification model becomes a combination of a linear sub-model and a nonlinear sub-model. A two-stage gradient-based iterative and a two-stage Newton iterative algorithms are proposed to estimate the parameters of photovoltaic cell models by using the negative gradient search and the Newton method. The performance of the proposed algorithms is assessed by using the simulation from the experimental data, and the evaluation results test the effectiveness of the proposed algorithms. In particular, the model built by using the obtained parameter estimates can fit the I-V curve, the P-V curve and the maximum power point well.

• Publisher: Springer Nature B.V
• Subject: Iterative algorithms / Maximum power / Parameter estimation / Parameter identification / Photovoltaic cells