Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.
Keywords
Renewable Energy Sources, Solar Energy, Machine Learning, Photovoltaic System, Maximum Power Point Tracking, Hybrid Grey Wolf Optimizer, Accuracy
F. Chien, H. W. Kamran, G. Albashar, and W. Iqbal, “Dynamic planning, conversion, and management strategy of different renewable energy sources: A Sustainable Solution for Severe Energy Crises in Emerging Economies,” International Journal of Hydrogen Energy, vol. 46, no. 11, pp. 7745–7758, Feb. 2021, doi: 10.1016/j.ijhydene.2020.12.004.
S. Dixit, “Solar technologies and their implementations: A review,” Materials Today: Proceedings, vol. 28, pp. 2137–2148, 2020, doi: 10.1016/j.matpr.2020.04.134.
C. Sun, Y. Zou, C. Qin, B. Zhang, and X. Wu, “Temperature effect of photovoltaic cells: a review,” Advanced Composites and Hybrid Materials, vol. 5, no. 4, pp. 2675–2699, Aug. 2022, doi: 10.1007/s42114-022-00533-z.
D. Díaz Martínez, R. Trujillo Codorniu, R. Giral, and L. Vázquez Seisdedos, “Evaluation of particle swarm optimization techniques applied to maximum power point tracking in photovoltaic systems,” International Journal of Circuit Theory and Applications, vol. 49, no. 7, pp. 1849–1867, Mar. 2021, doi: 10.1002/cta.2978.
R. B. Bollipo, S. Mikkili, and P. K. Bonthagorla, “Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation,” IET Renewable Power Generation, vol. 14, no. 9, pp. 1433–1452, Jun. 2020, doi: 10.1049/iet-rpg.2019.1163.
S. A. Mohamed, M. A. Tolba, A. A. Eisa, and A. M. El-Rifaie, “Comprehensive Modeling and Control of Grid-Connected Hybrid Energy Sources Using MPPT Controller,” Energies, vol. 14, no. 16, p. 5142, Aug. 2021, doi: 10.3390/en14165142.
“Hybrid, optimization, intelligent and classical PV MPPT techniques: Review,” CSEE Journal of Power and Energy Systems, 2020, doi: 10.17775/cseejpes.2019.02720.
M. Takruri et al., “Maximum Power Point Tracking of PV System Based on Machine Learning,” Energies, vol. 13, no. 3, p. 692, Feb. 2020, doi: 10.3390/en13030692.
“Maximum Power Point Tracking with Regression Machine Learning Algorithms for Solar PV systems,” International Journal of Renewable Energy Research, no. Vol12i3, 2022, doi: 10.20508/ijrer. v12i3.13249.g8517.
P. V. Mahesh, S. Meyyappan, and R. R. Alla, “Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems,” Clean Energy, vol. 6, no. 5, pp. 762–775, Oct. 2022, doi: 10.1093/ce/zkac057.
F. Mirza, M. Mansoor, K. Zhan, and Q. Ling, “High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance,” Energy, vol. 228, p. 120602, Aug. 2021, doi: 10.1016/j.energy.2021.120602.
Ibrahim et al., “PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions,” Chinese Journal of Electrical Engineering, vol. 6, no. 4, pp. 106–121, Dec. 2020, doi: 10.23919/cjee.2020.000035.
M. Vyas and G. S. Kushwah, “Sand Battery: An Innovative Solution for Renewable Energy Storage (A Review),” 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference (RESEM), May 2023, doi: 10.1109/resem57584.2023.10236319.
V. S. Simankov, P. Yu. Buchatskiy, S. V. Teploukhov, A. N. Cherkasov, and S. V. Onishchenko, “Designing an Intelligent Information and Analytical System for Evaluating Solutions in Renewable Energy Based on Digital Twins,” 2023 Seminar on Information Systems Theory and Practice (ISTP), Nov. 2023, doi: 10.1109/istp60767.2023.10426987.
S. Rafiqi and A. H. Bhat, "Role of UPQC In Addressing Power Quality Issues in the Power Grid Connected with Renewable Energy Sources," 2022 1st International Conference on Sustainable Technology for Power and Energy Systems (STPES), SRINAGAR, India, 2022, pp. 1-4, doi: 10.1109/STPES54845.2022.10006596.
F. Ayadi, I. Colak, I. Garip and H. I. Bulbul, "Targets of Countries in Renewable Energy," 2020 9th International Conference on Renewable Energy Research and Application (ICRERA), Glasgow, UK, 2020, pp. 394-398, doi: 10.1109/ICRERA49962.2020.9242765.
Yuan and X. Xiao, "A unified access conditioner for microgrid with renewable energy sources," 2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA, 2014, pp. 1919-1923, doi: 10.1109/ECCE.2014.6953653.
S. Paul, T. Dey, P. Saha, S. Dey, and R. Sen, “Review on the development scenario of renewable energy in different country,” 2021 Innovations in Energy Management and Renewable Resources (52042), Feb. 2021, doi: 10.1109/iemre52042.2021.9386748.
M. A. Abdulgalil, M. Ali, F. S. Al-Ismail and M. Khalid, "One-Step Solution for Sizing and Allocation of Battery Energy Storage System Using DC Optimal Power Flow," 2022 11th International Conference on Renewable Energy Research and Application (ICRERA), Istanbul, Turkey, 2022, pp. 379-384, doi: 10.1109/ICRERA55966.2022.9922869.
M. Mohammad, O. A. Alghamdi, A. A. Alhusssainy, M. Rawa, A. M. Abusorrah and Y. A. Al-Turki, "Optimal Economic Dispatch of Power System Networks with Renewable Energy Sources," 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East), Abu Dhabi, United Arab Emirates, 2023, pp. 1-7, doi: 10.1109/ISGTMiddleEast56437.2023.10078642.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Bojja Vani
Bojja Vani
Department of Computer Science and Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana, 506015, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Nabeel S. Alsharafa, Selvanayaki Kolandapalayam Shanmugam, Bojja Vani, Balaji P, Gokulraj S, Srinivas P.V.V.S, “Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency”, Journal of Machine and Computing, doi: 10.53759/7669/jmc202404055.
