CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer

Meng Jiang, Kun Ding, Xiang Chen, Liu Cui, Jingwei Zhang, Yi Cang, Hang Yang and Ruiguang Gao

Energy, 2024, vol. 296, issue C

Abstract: The current–voltage (I–V) characteristics are significant for applied research in photovoltaics (PV). The I–V characteristics calculated by double diode model (DDM) are accurate. DDM contains seven parameters, which are hard to solve. The combination method (CGH-GTO) based on improved grey wolf optimizer (IGWO), honey badger algorithm (HBA), and gorilla troops optimizer (GTO) for model parameter identification is proposed. First, the random initialization of GWO is replaced by Circle chaotic mapping (CCM). IGWO is employed to provide good initial values of parameters. Then, the bird-guided method of HBA is applied to optimize GTO for precise identification. Finally, the effectiveness, stability, and practicality of CGH-GTO are verified through experiments. The optimal RMSE (Root Mean Square Error) and R2 (Coefficient of determination) of CGH-GTO are 0.00241A and 0.99999 for TSM-240. Compared to the reference method GTO, the improvement ratio of RMSE for CGH-GTO can reach 7.3%. For Photowatt-PWP 201, the average RMSE of CGH-GTO is 0.00184A in the stability experiment. The RMSE of CGH-GTO is stabilized at [0.00182A, 0.00350A]. For three PV cell models, the results of evaluation metrics for DDM present the optimum. CGH-GTO achieves the highest accuracy in model parameter identification for DDM with RMSE and R2 of 0.00182A and 0.99999.

Keywords: Photovoltaics; Model parameter; Grey wolf optimizer; Honey badger algorithm; Gorilla troops optimizer; Combination method (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224009368
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:296:y:2024:i:c:s0360544224009368

DOI: 10.1016/j.energy.2024.131163

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 ().