Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models

Shan Jiao, Guoshuang Chong, Changcheng Huang, Hanqing Hu, Mingjing Wang, Ali Asghar Heidari, Huiling Chen and Xuehua Zhao

Energy, 2020, vol. 203, issue C

Abstract: Extracting parameters and constructing high-precision models of photovoltaic modules through actual current-voltage data is required for simulation, control, and optimization of a photovoltaic system. Because of the application of such problems, the identification of unknown parameters accurately and reliably remains a challenging task. In this paper, we propose an enhanced Harris Hawks Optimization (EHHO), which combines orthogonal learning (OL) and general opposition-based learning (GOBL), to estimate the parameters of solar cells and photovoltaic modules effectively and accurately. In EHHO, OL helps to improve the speed of the HHO method and the accuracy of the solution. At the same time, the GOBL mechanism can increase both diversity of the population and the HHO’s exploitation performance. In addition, these two mechanisms defend the equilibrium between the exploitation and exploration rates. The results show that accuracy, reliability, and other aspects of this method are better than most existing methods. Thus, we observed that EHHO can be used as an effective method for parameter estimation of solar cells and photovoltaic modules.

Keywords: Parameters estimation; Photovoltaic models; Harris hawks optimization; Orthogonal learning; General opposition-based learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220309117
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:203:y:2020:i:c:s0360544220309117

DOI: 10.1016/j.energy.2020.117804

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 ().