Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. (20th January 2020)

• Record Type: Journal Article
• Authors: Chen, Huiling; Jiao, Shan; Wang, Mingjing; Heidari, Ali Asghar; Zhao, Xuehua
• Is Part Of: Journal of cleaner production. Volume 244(2020)
• Publication Date: 2020-01-20
• Subjects: Photovoltaic models -- Parameters estimation -- Harris hawks optimization -- Swarm-intelligence -- Exploration and exploitation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.118778
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Accurate estimation of critical parameters of the photovoltaic models is a highly demanding task in modeling and simulating the photovoltaic systems. In this research, a diversification-enhanced Harris hawks optimization (HHO) is proposed based on the chaotic drifts in the vicinity of the best agent and an opposition-based exploratory strategy to efficiently identify unknown parameters of photovoltaic models modules. This novel technique is employed to estimate the parameters of solar cells models for single diode, double diode, and photovoltaic module. Also, different temperatures and irradiation levels for three practical manufactures datasets are investigated. Competitive and statistical experimental results demonstrate the excellent traits of the proposed HHO-based identifier in estimating the key parameters of photovoltaic models compared to several well-regarded competitors that prominently performs a good performance for solving this case. Also, satisfactory stability level is archived at different temperatures and levels of irradiance due to a better interchange between diversification and intensification drifts of the proposed variant. Therefore, HHO-based identifier provides high-quality solutions for parameter identification cases based on its coherent searching trends. We recommend this approach as a competent and auxiliary technique for parameters estimation of photovoltaic models. Graphical abstract: Image 1