Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models

• Published in: Energy (Oxford) 2020-07, Vol.203, p.117804, Article 117804
• Main Authors: Jiao, Shan, Chong, Guoshuang, Huang, Changcheng, Hu, Hanqing, Wang, Mingjing, Heidari, Ali Asghar, Chen, Huiling, Zhao, Xuehua
• Format: Article
• Language: English
• Subjects: Computer simulation; Exploitation; General opposition-based learning; Harris hawks optimization; Learning; Mathematical models; Modules; Optimization; Orthogonal learning; Parameter estimation; Parameter identification; Parameters estimation; Photovoltaic cells; Photovoltaic models; Photovoltaics; Reliability aspects; Solar cells
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2020.117804

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. •Enhanced Harris Hawks Optimization is proposed to identify the parameters of photovoltaic cell models.•Multiple learning strategies balance the exploration and exploitation trends.•The exploitative trend is boosted with the aid of orthogonal learning strategy.•The exploratory behavior is enhanced based on general opposition-based learning.