Chaos Game Optimization Algorithm for Parameters Identification of Different Models of Photovoltaic Solar Cell and Module
Main Article Content
Abstract
In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Geng Y, Chen W, Liu Z, Chiu A S F, Han W, Liu Z, Zhong S, Qian Y, You W, Cui X. A bibliometric review: energy consumption and greenhouse gas emissions in the residential sector. Journal of Cleaner Production, 2017; 159: 301–316. doi:10.1016/j.jclepro.2017.05.091
Abbassi R, Abbassi A, Jemli M, Chebbi S. Identification of unknown parameters of solar cell models: a comprehensive overview of available approaches. Renewable and Sustainable Energy Reviews, 2018; 70: 453–474. doi:10.1016/j.rser.2018.03.011
Humada A M, Hojabri M, Mekhilef S, Hamada H M. Solar cell parameters extraction based on single and double-diode models: a review. Renewable and Sustainable Energy Reviews, 2016; 56: 494–509. doi:10.1016/j.rser.2015.11.051
Gomes R C M, Vitorino M A, Corrêa M B R, Fernandes D A, Wang R. Shuffled complex evolution on photovoltaic parameter extraction: a comparative analysis. IEEE Transactions on Sustainable Energy, 2017; 8(2): 805–815. doi:10.1109/TSTE.2016.2620941
Jordehi A R. Parameter estimation of solar photovoltaic (PV) cells: a review. Renewable and Sustainable Energy Reviews, 2016; 61: 3543–3571. doi:10.1016/j.rser.2016.03.049
Ishaque K, Salam Z, Mekhilef S, Shamsudin A. Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Applied Energy, 2012; 99: 297–308. doi:10.1016/j.apenergy.2012.05.017
Yeh W C, Lin P, Huang C L. Simplified swarm optimization for the solar cell models parameter estimation problem. IET Renewable Power Generation, 2017; 11(8): 1166–1173. doi:10.1049/iet-rpg.2016.0473
Soliman M A, Hasanien H M, Alkuhayli A. Marine predators algorithm for parameters identification of triple-diode photovoltaic models. IEEE Access, 2020; 8: 155832–155842. doi:10.1109/access.2020.3019244
Batzelis E. Non-iterative methods for the extraction of the single-diode model parameters of photovoltaic modules: a review and comparative assessment. Energies, 2019; 12(3): 358. doi:10.3390/en12030358
Diab A Z, Sultan H M, Aljendy R, Al-Sumaiti A, Shoyama M, Ali Z M. Tree growth-based optimization algorithm for parameter extraction of different models of photovoltaic cells and modules. IEEE Access, 2020; 5: 119668–119687. doi:10.1109/access.2020.3005236
Yuan X, He Y, Liu L. Parameter extraction of solar cell models using chaotic asexual reproduction optimization. Neural Computing & Applications, 2015; 26: 1227–1239. doi:10.1007/s00521-014-1795-6
Chen X, Yu K, Du K, Zhao W, Liu G. Parameters identification of solar cell models using generalized oppositional teaching learning-based optimization. Energy, 2016; 90: 170–180.
doi:10.1016/j.energy.2016.01.052
Yu K, Liang J J, Qu B Y, Chen X, Wang H. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 2017; 150: 742–753. doi:10.1016/j.enconman.2017.08.063
Lin P, Cheng S, Yeh W, Chen Z, Wu L. Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Solar Energy, 2017; 144, 594–603. doi:10.1016/j.solener.2017.01.064
Kang T, Yao J, Yang S, Duong T L, Zhu X. Novel cuckoo search algorithm with quasi-oppositional population initialization strategy for solar cell parameters identification. 13th World Congress on Intelligent Control and Automation, Changsha, China, 4-8 July 2018.
doi:10.1109/WCICA.2018.8630628
Beigia A M, Maroosi A. Parameter identification for solar cells and module using a hybrid firefly and pattern search algorithms. Solar Energy, 2018; 171: 435–446. doi:10.1016/j.solener.2018.06.092
Chen X, Yu K. Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters. Solar Energy, 2019; 180: 192–206. doi:10.1016/j.solener.2019.01.025
Long W, Cai S, Jiao J, Xu M,Wu T. A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 2010; 203: 112243. doi:10.1016/j.enconman.2019.112243
Oliva D, Aziz M A E, Hassanien A E. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Applied Energy, 2017; 200: 141–154. doi:10.1016/j.apenergy.2017.05.029
Elazab O S, Hasanien H M, Elgendy M A, Abdeen A M. Whale optimization algorithm for photovoltaic model identification. The Journal of Engineering, 2017; 2017(13): 1906–1911. doi:10.1049/joe.2017.0662
Beigia A M, Maroosi A. Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms. Solar Energy, 2018; 171, 435–446. doi:10.1016/j.solener.2018.06.092
Li S, Gong W, Yan X, Hu C, Bai D, Wang L. Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy, 2019; 190: 465–474. doi:10.1016/j.solener.2019.08.022
Yu K, Qu B, Yue C, Ge S, Chen X, Liang J. A performance guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy, 2019; 237: 241–257. doi:10.1016/j.apenergy.2019.01.008
Chen H, Jiao S, Heidari A A, Wang M, Chen X, Zhao X. An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Conversion and Management, 2019; 195: 927-942. doi:10.1016/j.enconman.2019.05.057
Li S, Gong W, Yan X, Hu C, Bai D, Wang L, Gao L. Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization. Energy Conversion and Management, 2019; 186: 293–305. doi:10.1016/j.enconman.2019.02.048
Toledo F J, Blanes J M, Galiano V. Two-step linear least-squares method for photovoltaic single-diode model parameters extraction. IEEE Transactions on Industrial Electronic, 2018; 658: 6301–6308. doi:10.1109/TIE.2018.2793216
Mares O, Paulescu M, Badescu V. A simple but accurate solving the five-parameter model. Energy Conversion and Management, 2015; 105: 139–148. doi:10.1016/j.enconman.2015.07.046
Talatahari S, Azizi M. Optimization of constrained mathematical and engineering design problems using chaos game optimization. Computers & Industrial Engineering, 2020; 145: 106560. doi:10.1016/j.cie.2020.106560
Talatahari S, Azizi M. Chaos game optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 2021; 917: 1004–1054. doi:10.1007/s10462-020-09867-w