Boosting slime mould algorithm for parameter identification of photovoltaic models

Yun Liu, Ali Asghar Heidari, Xiaojia Ye, Guoxi Liang, Huiling Chen and Caitou He

Energy, 2021, vol. 234, issue C

Abstract: Estimating the photovoltaic model's unknown parameters efficiently and accurately can determine the solar cell's efficacy in converting the solar energy into electricity. For this purpose, this work proposes an advanced slime mould algorithm (SMA) integrated Nelder-Mead simplex strategy and chaotic map, called CNMSMA. Chaotic maps replace the random number rand that affects the choice of location updating strategy to improve the exploratory patterns. Also, Nelder-Mead simplex is introduced to reinforce the intensification capacity of the algorithm. The effectiveness of CCNMSMA has been verified in a single diode model, double diode model, and three diode models for RTC France solar cell and PVM 752 GaAs cell. Three commercial PV module models, which are the ST40, SM55, and KC200GT, are also utilized to verify the stability of CNMSMA under various temperatures and irradiances. The simulation results demonstrate that a developed SMA-based method can accurately extract the unknown photovoltaic solar cells' unknown parameters and achieve excellent convergence rapidity and stability performance. Also, no matter under insufficient irradiance or high-temperature conditions, CNMSMA is still without losing its accuracy and shows satisfactory stability. Accordingly, the proposed algorithm could act as a reliable and developed tool for extracting significant unknown parameters of photovoltaic models. This research will be supported by https://aliasgharheidari.com.

Keywords: Slime mould algorithm; Swarm-intelligence; Optimization; Parameter estimation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221014122
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014122

DOI: 10.1016/j.energy.2021.121164

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().