A novel heat transfer search algorithm and its application in solar cells
Source abstract
Abstract
A swarm intelligent optimization strategy, namely, the elite opposition-based learning (EOBL) strategy, is proposed for the heat transfer search algorithm (HTSA), aiming to achieve global optimization solutions for non-linear optimization problems. An improved heat transfer search algorithm (IHTSA), the EOBL strategy, is proposed to enhance the correlation between the upper and lower generations. The performance of the IHTSA has been verified with nine test functions, and the results of the IHTSA are compared with the corresponding results of the instinctive reaction strategy based on Harris hawks optimization and the HTSA. The experimental results show that the IHTSA achieves the first rank in overall performance among the algorithms. Then, the IHTSA is applied to determine the parameters of photovoltaic models, i.e., the single diode model and double diode model. By comparing with the results in the literature, IHTSA results show that it is an effective optimization algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Photovoltaic System Optimization Techniques · Solar Radiation and Photovoltaics · Solar Thermal and Photovoltaic Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechatronics Engineering, Zhengzhou University of Industrial Technology 1 , Zhengzhou 451100, China
School of Mechanical and Power Engineering, Henan Polytechnic University 2 , Jiaozuo 454003, China
Funding
Science and Technology Plan Project of Henan Province · Wuhu and Xidian University Special Fund for Industry-University-Research Cooperation · Natural Science Foundation of Henan Polytechnic University
References (34)
Show source-provided references
1. Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data10.1016/j.enconman.2016.01.071 ↗
2. Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm10.1016/j.enconman.2015.08.023 ↗
3. Parameter extraction of solar photovoltaic modules using penalty-based differential evolution10.1016/j.apenergy.2012.05.017 ↗
4. A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data10.1016/j.apenergy.2012.06.036 ↗
5. Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis10.1016/j.apenergy.2009.07.022 ↗
6. An improved five-parameter model for photovoltaic modules10.1016/j.solmat.2010.04.003 ↗
7. Parameters identification of solar cell models using generalized oppositional teaching learning based optimization10.1016/j.energy.2016.01.052 ↗
8. Novel seven-parameter model for photovoltaic modules10.1016/j.solmat.2014.07.016 ↗
9. Generation control circuit for photovoltaic modules[J]10.1109/63.923760 ↗
10. A new estimation approach for determining the I–V characteristics of solar cells10.1016/j.solener.2011.04.013 ↗
11. Simulated annealing algorithm for photovoltaic parameters identification10.1016/j.solener.2011.09.032 ↗
12. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics10.1016/j.solmat.2005.04.023 ↗
13. A comparative study of extraction methods for solar cell model parameters10.1016/0038-1101(86)90212-1 ↗
14. Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers10.1080/01425918608909835 ↗
15. Multi-objective optimization of bucket wheel reclaimer based on improved dragonfly algorithm10.3901/JME.2021.06.211 ↗
16. An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems10.1108/EC-08-2019-0362 ↗
17. An adaptive instinctive reaction strategy based on Harris hawks optimization algorithm for numerical optimization problems10.1063/5.0035635 ↗
18. Optimization of a frame structure using the Coulomb force search strategy-based dragonfly algorithm10.1080/0305215x.2019.1618290 ↗
19. Optimal extraction of solar cell parameters using pattern search10.1016/j.renene.2012.01.082 ↗
20. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems10.1007/s00521-015-1920-1 ↗
21. Search group algorithm: A new metaheuristic method for the optimization of truss structures10.1016/j.compstruc.2015.03.003 ↗
22. Heat transfer search (HTS): A novel optimization algorithm10.1016/j.ins.2015.06.044 ↗
23. Multi-objective heat transfer search algorithm for truss optimization
24. Hybrid heat transfer search and passing vehicle search optimizer for multi-objective structural optimization10.1016/j.knosys.2020.106556 ↗
25. Improved metaheuristics through migration-based search and an acceptance probability for truss optimization10.1007/s42107-020-00271-x ↗
26. An improved heat transfer search algorithm for unconstrained optimization problems10.1016/j.jcde.2018.04.003 ↗
27. Multi objecitve structural optimization using improved heat transfer search10.1016/j.knosys.2021.106811 ↗
28. Heat transfer search algorithm for sizing optimization of truss structures10.1590/1679-78253297 ↗
29. Simultaneous heat transfer search for computationally expensive numerical optimization
30. Modified teaching–learning-based optimization algorithm for global numerical optimization—A comparative study10.1016/j.swevo.2013.12.005 ↗
31. Solar cell parameter extraction using genetic algorithms10.1088/0957-0233/12/11/322 ↗
32. Artificial bee swarm optimization algorithm for parameters identification of solar cell models10.1016/j.apenergy.2012.09.052 ↗
33. Parameter extraction of solar cell models using repaired adaptive differential evolution10.1016/j.solener.2013.05.007 ↗
34. Parameter identification for solar cell models using harmony search-based algorithms10.1016/j.solener.2012.08.018 ↗