SET2018_paper_52.pdf (328.24 kB)
0/0

Parameters extraction of single diode model of photovoltaic cell using improved firefly algorithm

Download (328.24 kB)
conference contribution
posted on 14.09.2018 by Yong Wu, Qian Xia, Richard Blanchard
Models of photovoltaic (PV) are significant in the design, study and control of renewable energy system. In these models, equivalent diode circuit model is widely researched and used because of its high precision. In diode circuit model, usually there are several parameters to be determined. To improve the performance of PV models, it is important to extract their unknown parameters exactly and quickly. However, because of the nonlinear of the V-I characteristic output of PV, it is difficult to obtain the parameters accurately. In this paper, a self-adaptive firefly algorithm is proposed to extract the parameters of single diode circuit model. Through introducing an adaptive mutation factor into the evolution process of a firefly algorithm, it improves the precision and stability of the solution. The proposed adaptive firefly algorithm is used to extract the parameters of single diode model which have 5 unknown parameters. By using measure output voltage and current data of PV, the proposed algorithm can extract parameters exactly and quickly. The proposed algorithm is compared with classic firefly algorithms and other algorithms in the paper. The results show that the effectiveness of the proposed algorithm.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

SET2018 - 17th International Conference on Sustainable Energy Technologies

Citation

WU, Y., XIA, Q. and BLANCHARD, R.E., 2018. Parameters extraction of single diode model of photovoltaic cell using improved firefly algorithm. Presented at the 17th International Conference on Sustainable Energy Technologies (SET 2018), Wuhan, China, 21st-23rd August 2018.

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

05/03/2018

Publication date

2018

Notes

This is a conference paper.

Publisher version

Language

en

Location

Wuhan, China

Exports

Logo branding

Exports