Open Access
Issue
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
Article Number 00064
Number of page(s) 16
DOI https://doi.org/10.1051/e3sconf/202346900064
Published online 20 December 2023
  1. Bingöl, Okan, and Burçin ÖZKAYA.: A comprehensive overview of soft computing based MPPT techniques for partial shading conditions in PV systems. Mühendislik Bilimleri ve Tasarım Dergisi 7.4, 926-939(2019). [CrossRef] [Google Scholar]
  2. Kim, Sunghyun, et al.: Upper limit to the photovoltaic efficiency of imperfect crystals from first principles. Energy & Environmental Science 13.5, 1481-1491(2020). [CrossRef] [Google Scholar]
  3. Li, Xingshuo, et al.: A comparative study on photovoltaic MPPT algorithms under EN50530 dynamic test procedure. IEEE Transactions on Power Electronics 36.4, 4153-4168(2020). [Google Scholar]
  4. Pant, Shraiya, and R. P. Saini.: Comparative study of MPPT techniques for solar photovoltaic system. 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, (2019). [Google Scholar]
  5. Wasim, Muhammad Shahid, et al.: A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions. Energy Reports 8, 4871-4898(2022). [CrossRef] [Google Scholar]
  6. Al-Majidi, Sadeq D et al.: Design of an intelligent MPPT based on ANN using a real photovoltaic system data. 2019 54th International Universities Power Engineering Conference (UPEC). IEEE, (2019). [Google Scholar]
  7. Yaich, Mohamed, et al.: Metaheuristic Optimization Algorithm of MPPT Controller for PV system application. E3S Web of Conferences. Vol. 336. EDP Sciences, (2022). [Google Scholar]
  8. Bollipo, Ratnakar Babu et al.: Hybrid, optimal, intelligent and classical PV MPPT techniques: A review. CSEE Journal of Power and Energy Systems 7.1, 9-33(2020). [Google Scholar]
  9. Azad, Murari Lal et al.: Comparative study between P&O and incremental conduction MPPT techniques-a review. 2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE, (2020). [Google Scholar]
  10. Eseosa, Omorogiuwa, and Itelema Kingsley. : Comparative study of MPPT techniques for photovoltaic systems. Saudi Journal of Engineering and Technology 5, 12-14(2020). [Google Scholar]
  11. Pal, Rudra Sankar, and V. Mukherjee. : Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition. Energy 212, 118592(2020). [CrossRef] [Google Scholar]
  12. Tepe, Izviye Fatimanur, and Erdal Irmak. : Review and comparative analysis of metaheuristic MPPT algorithms in PV systems under partial shading conditions. 2022 11th International Conference on Renewable Energy Research and Application (ICRERA). IEEE, (2022). [Google Scholar]
  13. Boudaraia, Karima, et al.: MPPT design using artificial neural network and backstepping sliding mode approach for photovoltaic system under various weather conditions. International Journal of Intelligent Engineering and Systems 12.6, 177-186(2019). [CrossRef] [Google Scholar]
  14. Bouri, Sihem, Oussama-Abdelfettah Mekkaoui, and Anes Mohammed Mamem. : Comparative Study of Different MPPT Methods of a Boost Chopper of PV Generator. Acta Electrotechnica et Informatica 22.3, 24-31(2022). [CrossRef] [Google Scholar]
  15. Viswambaran, V. K., A. Bati, and E. Zhou. : Review of AI based maximum power point tracking techniques & performance evaluation of artificial neural network based MPPT controller for photovoltaic systems. International Journal of Advanced Science and Technology 29.10s, 8159-8171(2020). [Google Scholar]
  16. Belhachat, Faiza, and Cherif Larbes. : PV array reconfiguration techniques for maximum power optimization under partial shading conditions: A review. Solar Energy 230, 558-582(2021). [CrossRef] [Google Scholar]
  17. Chtita, Smail, et al.: A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions. Scientific Reports 12.1, 10637(2022). [CrossRef] [PubMed] [Google Scholar]
  18. Vankadara, Sampath Kumar, et al.: Marine predator algorithm (MPA)-based MPPT technique for solar PV systems under partial shading conditions. Energies 15.17, 6172(2022). [CrossRef] [Google Scholar]
  19. Raj, Akhil, and R. P. Praveen. : Highly efficient DC-DC boost converter implemented with improved MPPT algorithm for utility level photovoltaic applications. Ain Shams Engineering Journal 13.3, 101617(2022). [CrossRef] [Google Scholar]
  20. Manna, Saibal, and Ashok Kumar Akella. : Comparative analysis of various P & O MPPT algorithm for PV system under varying radiation condition. 2021 1st International Conference on Power Electronics and Energy (ICPEE). IEEE, (2021). [Google Scholar]
  21. Ghizlane, Chbirik, et al.: Speed Control of Induction Motor Driving a Pump Supplied by a Photovoltaic Array. International Journal of Renewable Energy Research 10, 237-242(2020). [Google Scholar]
  22. Alshareef, Muhannad, et al.: Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions. Energies 12.4, 623(2019). [CrossRef] [Google Scholar]
  23. Pathy, Somashree, et al.: Nature-inspired MPPT algorithms for partially shaded PV systems: A comparative study. Energies 12.8, 1451(2019). [Google Scholar]
  24. Dagal, Idriss, Burak Akın, and Erdem Akboy. : MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink. Scientific reports 12.1, 2664(2022). [CrossRef] [PubMed] [Google Scholar]
  25. Khan, Asim Iqbal, et al.: Artificial neural network-based maximum power point tracking method with the improved effectiveness of standalone photovoltaic system. AI and machine learning paradigms for health monitoring system: intelligent data analytics, 459-470(2021). [Google Scholar]
  26. Mughal, Shafqat Nabi, Yog Raj Sood, and R. K. Jarial. : A neural network-based time-series model for predicting global solar radiations. IETE Journal of Research 69.6, 3418-3430(2023). [CrossRef] [Google Scholar]
  27. Ghedhab, Nabila, et al.: Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network. E3S Web of Conferences. Vol. 152. EDP Sciences, (2020). [Google Scholar]

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