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

•An improved Harris Hawks Optimizer (CCNMHHO) is proposed for photovoltaic systems.•The performance of CCNMHHO is compared with some well-known competitive algorithms.•Three PV models are simulated to verify the effectiveness of CCNMHHO.•This method has enhanced the convergence speed and accuracy in...

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Bibliographic Details
Published in:Energy conversion and management Vol. 223; p. 113211
Main Authors: Liu, Yun, Chong, Guoshuang, Heidari, Ali Asghar, Chen, Huiling, Liang, Guoxi, Ye, Xiaojia, Cai, Zhennao, Wang, Mingjing
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.11.2020
Elsevier Science Ltd
Subjects:
administrative management
Algorithms
Cell culture
Convergence
Crisscross optimization algorithm
Crossovers
Data exchange
energy conversion
estimation
exhibitions
extraction
Harris Hawk optimization
information exchange
materials
Mathematical models
measurement
Nelder-Mead simplex algorithm
Optimization
Parabuteo unicinctus
Parameter estimation
Photovoltaic cells
Photovoltaic models
Photovoltaics
population
Radiance
Solar cells
solar collectors
temperature
Harris Hawk optimization
Parameter estimation
Nelder-Mead simplex algorithm
Photovoltaic models
Crisscross optimization algorithm
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:•An improved Harris Hawks Optimizer (CCNMHHO) is proposed for photovoltaic systems.•The performance of CCNMHHO is compared with some well-known competitive algorithms.•Three PV models are simulated to verify the effectiveness of CCNMHHO.•This method has enhanced the convergence speed and accuracy in various conditions. 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://aliasgharheidari.com.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2020.113211