An Effective Method for Parameter Estimation of a Solar Cell

Electronics

Moshe Averbukh¹  Abhinav Sharma²  Abhishek Sharma³  Vibhu Jately⁴  Brian Azzopardi⁴ 

[1] Department of Electrical and Electronics Engineering, Ariel University, Ariel 40700, Israel;Department of Electrical and Electronics Engineering, School of Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India;Department of Research and Development, University of Petroleum and Energy Studies, Dehradun 248007, India;Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Paola PLA9032, Malta;

关键词: photovoltaic;    parameter extraction;    single-diode model;    double-diode model;    swarm intelligence;   

DOI  :  10.3390/electronics10030312

来源: DOAJ

【 摘 要 】

Parameter extraction of the photovoltaic cell is a highly nonlinear complex optimization problem. This article proposes a new hybrid version of whale optimization and particle swarm optimization algorithm to optimize the photovoltaic cell parameters. The exploitation ability of particle swarm optimization with adaptive weight function is implemented in the pipeline mode with a whale optimization algorithm to improve its exploitation capability and convergence speed. The performance of the proposed hybrid algorithm is compared with six different optimization algorithms in terms of root mean square error and rate of convergence. The simulation result shows that the proposed hybrid algorithm produces not only optimized parameters at different irradiation levels (i.e., 1000 W/m², 870 W/m², 720 W/m², and 630 W/m²) but also estimates minimum root mean square error even at a low level of irradiations. Furthermore, the statistical analysis validates that the average accuracy and robustness of the proposed algorithm are better than other algorithms. The best values of root mean square error generated by the proposed algorithm are 7.1700 × 10^-4 and 9.8412 × 10^-4 for single-diode and double-diode models. It is observed that the estimated parameters based on the optimization process are highly consistent with the experimental data.

【 授权许可】

Unknown