Table of Contents Author Guidelines Submit a Manuscript
International Journal of Photoenergy
Volume 2016 (2016), Article ID 2174573, 16 pages
http://dx.doi.org/10.1155/2016/2174573
Research Article

A Population Classification Evolution Algorithm for the Parameter Extraction of Solar Cell Models

1College of Physics and Information Engineering and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou 350116, China
2Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou 213164, China

Received 11 May 2016; Revised 21 June 2016; Accepted 22 June 2016

Academic Editor: Tamer Khatib

Copyright © 2016 Yiqun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To quickly and precisely extract the parameters for solar cell models, inspired by simplified bird mating optimizer (SBMO), a new optimization technology referred to as population classification evolution (PCE) is proposed. PCE divides the population into two groups, elite and ordinary, to reach a better compromise between exploitation and exploration. For the evolution of elite individuals, we adopt the idea of parthenogenesis in nature to afford a fast exploitation. For the evolution of ordinary individuals, we adopt an effective differential evolution strategy and a random movement of small probability is added to strengthen the ability to jump out of a local optimum, which affords a fast exploration. The proposed PCE is first estimated on 13 classic benchmark functions. The experimental results demonstrate that PCE yields the best results on 11 functions by comparing it with six evolutional algorithms. Then, PCE is applied to extract the parameters for solar cell models, that is, the single diode and the double diode. The experimental analyses demonstrate that the proposed PCE is superior when comparing it with other optimization algorithms for parameter identification. Moreover, PCE is tested using three different sources of data with good accuracy.