The great technological development, the increase in the number of factories, and the large population growth led to an increase in the demand for the consumption of electric energy that we get from traditional methods (fossil fuels). Moreover, the global shortage in fossil fuel sources and their high costs, the global financial and economic crisis, and the harmful emissions it causes for the environment have made researchers look for electrical energy from alternative and environmentally friendly sources. As a renewable energy, solar energy is considered one of the most important sources of electrical energy today because it is easy to obtain at a low cost. However, this type of energy suffers from low efficiency and is greatly affected by changing weather conditions. To address this problem, several techniques have been proposed by research groups, and MPPT is one of those techniques that has been frequently used in recent years to extract maximum power from solar panels despite the instability in weather conditions. This technique can also generate pulses to control the DC-DC boost converter to provide a certain level of voltage. In this paper, three algorithms, namely Perturbation and Observation (P&O), Fuzzy Logic Controller (FLC), and Particle Swarm Optimization (PSO) are modified and applied in the MPPT technology to control the duty cycle of a DC-DC converter. The photovoltaic system consisting of MPPT technology, solar panels, and a DC-DC boost converter was simulated using MATLAB/Simulink. The performances of the three algorithms were compared to determine the best one that guarantees the highest efficiency under multiple test conditions. The simulation results show that PSO was a better performer than others with (99.32%, 97.02%, and 98.33%, respectively).
2. Peng, J., L. Lu, and H. Yang, "Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems," Renew. Sustain. Energy Rev., Vol. 19, 255-274, 2013.
3. Sampaio, P. G. V. and M. O. A. Gonzalez, "Photovoltaic solar energy: Conceptual framework," Renew. Sustain. Energy Rev., Vol. 74, 590-601, 2017.
4. Abdulaziz, S., G. Attlam, G. Zaki, and E. Nabil, "Cuckoo search algorithm and particle swarm optimization based maximum power point tracking techniques," Indones. J. Electr. Eng. Comput. Sci., Vol. 26, No. 2, 605-616, 2022.
5. Alzubaidi, A. A., L. A. Khaliq, H. S. Hamad, W. K. Al-Azzawi, M. S. Jabbar, and T. A. Shihab, "MPPT implementation and simulation using developed P&O algorithm for photovoltaic system concerning efficiency," Bull. Electr. Eng. Informatics, Vol. 11, No. 5, 2460-2470, 2022.
6. Gouda, E. A., M. F. Kotb, and D. A. Elalfy, "Modelling and performance analysis for a PV system based MPPT using advanced techniques," Eur. J. Electr. Eng. Comput. Sci., Vol. 3, No. 1, 1-7, 2019.
7. Swain, B., D. Patnaik, J. Halder, P. P. Nayak, D. P. Kar, and S. Bhuyan, "Photovoltaic driven resonant wireless energy transfer system for implantable electronic sensor," Progress In Electromagnetics Research M, Vol. 85, 175-184, 2019.
8. Alhussain, H. M. A. and N. Yasin, "Modeling and simulation of solar PV module for comparison of two MPPT algorithms (P&O & INC) in MATLAB/Simulink," Indones. J. Electr. Eng. Comput. Sci., Vol. 18, No. 2, 666-677, 2020.
9. Chekenbah, H., A. El Abderrahmani, A. Aghanim, Y. Maataoui, and R. Lasri, "Solving problem of partial shading condition in a photovoltaic system through a self-adaptive fuzzy logic controller," Int. J. Tech. Phys. Probl. Eng., Vol. 13, No. 2, 130-137, 2021.
10. Toumi, D., et al., "Maximum power point tracking of photovoltaic array using fuzzy logic control," Int. J. Power Electron. Drive Syst., Vol. 13, No. 4, 2440-2449, 2022.
11. Material, C., "Print t for reprint," Differences, Vol. 104, 85-92, 2006.
12. Awan, M. M. A., M. Y. Javed, A. B. Asghar, and K. Ejsmont, "Performance optimization of a ten check MPPT algorithm for an off-grid solar photovoltaic system," Energies, Vol. 15, No. 6, 2022.
13. Al-Adhami, Y. and E. Ercelebi, "A plasmonic monopole Antenna Array on exible photovoltaic panels for further use of the green energy harvesting," Progress In Electromagnetics Research M, Vol. 68, 143-152, 2018.
14. Kingston, S. R., et al., "Spread Spectrum Time Domain Re ectometry (SSTDR) digital twin simulation of photovoltaic systems for fault detection and location," Progress In Electromagnetics Research B, Vol. 94, 105-126, 2021.
15. Majaw, T., R. Deka, S. Roy, and B. Goswami, "Solar charge controllers using MPPT and PWM: A review," ADBU J. Electr. Electron. Eng., Vol. 2, No. 1, 1-4, 2018, [Online]. Available: https://media.neliti.com/media/publications/287658-solar-charge-controllers-using-mppt-and-66d6c4aa.pdf.
16. Bollipo, R. B., S. Mikkili, and P. K. Bonthagorla, "Critical review on PV MPPT techniques: Classical, intelligent and optimisation," IET Renew. Power Gener., Vol. 14, No. 9, 1433-1452, 2020, doi: 10.1049/iet-rpg.2019.1163.
17. Talbi, M., N. Mensia, and H. Ezzaouia, "Modeling of a PV panel and application of maximum power point tracking command based on ANN," Int. Arab J. Inf. Technol., Vol. 18, No. 4, 568-577, 2021.
18. Jain, K., M. Gupta, and A. Kumar Bohre, "Implementation and comparative analysis of PO and INC MPPT method for PV system," India Int. Conf. Power Electron. IICPE, 1-6, 2018.
19. Idadoub, H., M. Kourchi, M. Ajaamoum, D. Yous, and A. Rachdy, "Comparison and experimental validation of three photovoltaic models of four technology types," Int. J. Tech. Phys. Probl. Eng., Vol. 11, No. 4, 1-10, 2019.
20. Hu, Z., Y. Zhang, L. Liu, L. Yang, and S. He, "A nanostructure-based high-temperature selective absorber-emitter pair for a solar thermophotovoltaic system with narrowband thermal emission," Progress In Electromagnetics Research, Vol. 162, 95-108, 2018.
21. Eltamaly, A. M. and A. Y. Abdelaziz, Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, 2020.
22. Ishaque, K., Z. Salam, M. Amjad, and S. Mekhilef, "An improved Particle Swarm Optimization (PSO)-based MPPT for PV with reduced steady-state oscillation," IEEE Trans. Power Electron., Vol. 27, No. 8, 3627-3638, 2012.
23. Mars, N., F. Grouz, N. Essounbouli, and L. Sbita, "Synergetic MPPT controller for photovoltaic system," J. Electr. Electron. Syst., Vol. 6, No. 2, 2017.
24. Saleem, A., N. Liu, H. Junjie, A. Iqbal, and A. Waqar, "Comprehensive equation-based design of photovoltaic module to investigate its physical parameters and operating conditions used for small application," Meas. Control (United Kingdom), Vol. 53, No. 5-6, 850-858, 2020.
25. Isen, E. and A. Sengul, "Comparison of maximum power point tracking techniques on photo-voltaic panels," Canakkale Onsekiz Mart Universitesi Fen Bilim. Enstitusu Derg., 14-29, 2020.
26. Ali, A., et al., "Investigation of MPPT techniques under uniform and non-uniform solar irradiation condition --- A retrospection," IEEE Access, Vol. 8, 127368-127392, 2020.
27. Belhadj Djilali, A., B. Hemici, and A. Yahdou, "Modied perturb and observe MPPT control for avoid deviation in photovoltaic systems," J. Electr. Eng., Vol. 17, No. 1, 28-37, 2017.
28. Khan, M. J., L. Mathew, M. A. Alotaibi, H. Malik, and M. E. Nassar, "Fuzzy-logic-based comparative analysis of different maximum power point tracking controllers for hybrid renewal energy systems," Mathematics, Vol. 10, No. 3, 2022, doi: 10.3390/math10030529.
29. Guiza, D., D. Ounnas, Y. Sou, A. Bouden, and M. Maamri, "Implementation of modied perturb and observe based MPPT algorithm for photovoltaic system," Proc. --- 2019 1st Int. Conf. Sustain. Renew. Energy Syst. Appl. ICSRESA 2019, 2019.
30. Baramadeh, M. Y., M. A. A. Abouelela, and S. M. Alghuwainem, "A fuzzy logic controller based MPPT technique for photovoltaic generation system," Smart Grid Renew. Energy, Vol. 12, No. 10, 163-181, 2021.
31. Hassan, T. U., et al., "A novel algorithm for MPPT of an isolated PV system using push pull converter with fuzzy logic controller," Energies, Vol. 13, No. 15, 4007, 2020.
32. Li, X., H. Wen, Y. Hu, and L. Jiang, "A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application," Renew. Energy, Vol. 130, 416-427, 2019.
33. Robles, C., "Fuzzy logic based MPPT controller for a PV system," Energies, Vol. 10, No. 12, 1-18, 2017.
34. Li, H., D. Yang, W. Su, J. Lu, and X. Yu, "An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading," IEEE Trans. Ind. Electron., Vol. 66, No. 1, 265-275, 2019.
35. Eltamaly, A. M., "A novel particle swarm optimization optimal control parameter determination strategy for maximum power point trackers of partially shaded photovoltaic systems," Eng. Optim., Vol. 54, No. 4, 634-650, 2022.
36. Nisa, M., M. Andleeb, and B. Farhad Ilahi, "Effect of partial shading on a PV array and its maximum power point tracking using particle swarm optimization," J. Phys. Conf. Ser., Vol. 1817, No. 1, 2021.
37. Irwanto, M., et al., "Photovoltaic powered DC-DC boost converter based on PID controller for battery charging system," J. Phys. Conf. Ser., Vol. 1432, No. 1, 2020.
38. Prabhu, H. U. and M. R. Babu, "Performance study of mppt algorithms of dc-dc boost converters for PV cell applications," Proc. 7th Int. Conf. Electr. Energy Syst. ICEES 2021, 201-205, 2021.