Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

作     者：Liu, Yun Chong, Guoshuang Heidari, Ali Asghar Chen, Huiling Liang, Guoxi Ye, Xiaojia Cai, Zhennao Wangg, Mingjing 

作者机构：Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing 100040 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam 

出 版 物：《ENERGY CONVERSION AND MANAGEMENT》 (能量转换与管理)

年 卷 期：2020年第223卷

页      面：113211-113211页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 0801[工学-力学（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [U1809209] Key scientific research project of Wenzhou Polytechnic [WZY2019004] Service & technology innovation project of Wenzhou science and technology association [2019KXCX-RH07] General research project of Zhejiang Provincial Education Department [Y201942618] 

主　　题：Photovoltaic models Harris Hawk optimization Crisscross optimization algorithm Nelder-Mead simplex algorithm Parameter estimation 

摘      要：An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://***.