FUZZY LOGIC CONTROL TO PROCESS CHANGE IRRADIATION AND TEMPERATURE IN THE SOLAR CELL BY CONTROLLING FOR MAXIMUM POWER POINT
DOI:
https://doi.org/10.31272/jeasd.27.1.3Keywords:
Fuzzy logic, direct current to direct current (DC-DC) converter, photovoltaic systems, Matlab/Simulink, efficiencyAbstract
This paper presents intelligent control methods to get the maximum power point (MPPT) to be a photovoltaic system that operates with high efficiency when weather conditions change as well as fluctuations in temperatures resulting from sunlight. The proposed method of controlling by fuzzy control techniques is applied with a Direct current to Direct current (DC-DC) converter device. The important steps of the control unit for integrated design. The photovoltaic system, which was designed by Matlab / Simulink, was implemented with simulations of autonomous water pumping techniques. Comparison of results with simulations without MPPT control. We have noticed that the system in the case of using the MPPT that was used in the fuzzy logic unit gave high efficiency for the energy production from the solar cell the crucial control unit steps for integrated design. Simulated autonomous water pumping methods were used to implement the Matlab/Simulink-designed photovoltaic system. Results comparison with simulations lacking MPPT control. We have observed that the system provided great efficiency for the energy generation from the solar cell in the event of using the MPPT that was used in the fuzzy logic unit.
References
Trishan and P. L. ChapmEsraman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. energy Convers., vol. 22, no. 2, pp. 439–449, 2007. DOI: 10.1109/TEC.2006.874230
P. Singh, D. K. Palwalia, A. Gupta, and P. Kumar, “Comparison of photovoltaic array maximum power point tracking techniques,” Int. Adv. Res. J. Sci. Eng. Technol, vol. 2, no. 1, pp. 401–404, 2015.DOI:10.17148/IARJSET.
N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Trans. power Electron., vol. 20, no. 4, pp. 963–973, 2005. DOI: 10.1109/TPEL.2005.850975
D. Canny and F. Yusivar, “Maximum power point tracking (MPPT) algorithm simulation based on fuzzy logic controller on solar cell with boost converter,” in 2018 2nd International Conference on Smart Grid and Smart Cities (ICSGSC), 2018, pp. 117–121. DOI: 10.1109/TPEL.2005.850975
V. K. PK and K. Manjunath, “A comparitive analysis of MPPT algorithms for solar photovoltaic systems to improve the tracking accuracy,” in 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), 2018, pp. 540–547. DOI: 10.1109/ICCPCCT.2018.8574336
R. H. Ahmed, A. A. Abdulrazzaq, and M. T. Yunis, “The implementation (hybrid unit power supply) of a portable plant power by employ (solar cell and fuel cell) using deep learning technology for neural networks,” in AIP Conference Proceedings, 2021, vol. 2404, no. 1, p. 20001. https://doi.org/10.1063/5.0071022
S. Latorrata, P. G. Stampino, C. Cristiani, and G. Dotelli, “Development of an optimal gas diffusion medium for polymer electrolyte membrane fuel cells and assessment of its degradation mechanisms,” Int. J. Hydrogen Energy, vol. 40, no. 42, pp. 14596–14608, 2015. https://doi.org/10.1016/j.ijhydene.2015.05.100
N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimizing sampling rate of P&O MPPT technique,” in 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No. 04CH37551), 2004, vol. 3, pp. 1945–1949. DOI: 10.1109/PESC.2004.1355415
N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Increasing the efficiency of P&O MPPT by converter dynamic matching,” in 2004 IEEE international symposium on industrial electronics, 2004, vol. 2, pp. 1017–1021. DOI: 10.1109/ISIE.2004.1571953
Y. Xie, Q. Wu, Y. Wang, J. Qu, and H. Qian, “A variable step MPPT optimized algorithm based on power prediction,” in Proceedings of the 32nd Chinese Control Conference, 2013, pp. 7543–7546. https://ieeexplore.ieee.org/abstract/document/6640766
Suryoatmojo et al., “Design of MPPT based fuzzy logic for solar-powered unmanned aerial vehicle application,” in 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 2018, pp. 1–4. DOI: 10.1109/ICEAST.2018.8434430
A. Abbadi, F. Hamidia, A. Morsli, O. Benbouabdellah, and Y. Chiba, “MPPT based fuzzy-logic controller for grid connected residential photovoltaic power system,” in International Conference in Artificial Intelligence in Renewable Energetic Systems, 2019, pp. 124–131. DOI https://doi.org/10.1007/978-3-030-37207-1_13
B. A. Naidu, S. A. Kumar, and D. Narayana, “Fuzzy intelligent controller for the MPPT of a photovoltaic module in comparison with perturb and observe algorithm,” Int. J. Appl. Eng. Res., vol. 13, no. 11, pp. 10058–10062, 2018. http://www.ripublication.com/ijaer18/ijaerv13n11_181.pdf
W. Obaid, A. K. Hamid, and C. Ghenai, “Hybrid PEM fuel-cell-solar power system Design for Electric Boat with MPPT system and fuzzy energy management,” in 2019 International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2019, pp. 1–7. DOI: 10.1109/ICCSPA.2019.8713646
W. Obaid, A.-K. Hamid, and C. Ghenai, “Hybrid PEM fuel-cell-diesel-solar power system design with fuzzy battery management system and weather forecasting for electric boats,” in 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), 2018, pp. 1–7. DOI: 10.1109/IRSEC.2018.8702862
F. F. Ahmad, C. Ghenai, and M. Bettayeb, “Maximum power point tracking and photovoltaic energy harvesting for Internet of Things: A comprehensive review,” Sustain. Energy Technol. Assessments, vol. 47, p. 101430, 2021. https://doi.org/10.1016/j.seta.2021.101430
R. H. Ahmed and A. A. Abdulrazzaq, “Reduced pole to pole voltage deviations through stopping MMC blockading loss of terminals below DC faults,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 881, no. 1, p. 12130. doi:10.1088/1757-899X/881/1/012130
I. M. Mehedi et al., “Intelligent machine learning with evolutionary algorithm based short term load forecasting in power systems,” IEEE Access, vol. 9, pp. 100113–100124, 2021. DOI: 10.1109/ACCESS.2021.3096918
M. Zaja, “Enhancing DC grid reliability by preventing loss of terminals under DC faults.” University of Aberdeen, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774052
A. Mehedi and M. A. Abdullah, “Design and dynamic modeling of a solar electric boat power system.” Memorial University of Newfoundland, 2021. https://doi.org/10.48336/t26k-t303
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