Analysis and Selection of the Optimal Performance Control Method for Solar Cell Dc Converters
DOI:
https://doi.org/10.22213/2410-9304-2023-1-125-137Keywords:
Matlab/Simulink, MPPT algorithm, buck-boost converter, buck converter, duty ratio control, maximum power point tracking (MPPT), PV cellAbstract
Solar energy is one of the most promising sources of energy to meet the ever-increasing demand for electricity in the world. But due to the low efficiency of solar photovoltaic (PV) cells and the dependence of their performance on environmental conditions, it is necessary to monitor the maximum power point (MPP) of the photovoltaic system. Hence, various maximum power point tracking methods are implemented with photovoltaic systems, but the design of the DC/DC converter is one of the main problems with maximum power point tracking methods. This article compares two different DC/DC converters for solar power conversion. The two converters are a buck converter and a buck-boost converter. The maximum power point tracking algorithm is designed to calculate battery voltage, PV array current, PV array voltage, PV array power, and output power. It has been observed that the buck-boost converter is the best converter for converting solar energy. The two circuits are modeled in MATLAB/Simulink R2021a. By means of the proposed control methods, there is a significant increase in the generation of electrical energy by photovoltaic panels. In the presented work, the information and control system of a low-power solar power plant is investigated. A large amount of converted electrical energy cannot be used by the consumer due to various losses in the photovoltaic system. To improve the efficiency of using the generated electricity, a promising algorithm was developed that is responsible for the operation of the station control unit. As a result, the quality of the solar installation was significantly improved and energy production for the consumer's power supply was increased. The simulation results will give future researchers a clear concept for predicting the behavior of the mentioned converters and designing the most efficient converter specified for solar MPPT.References
El Mentaly, Lahcen, AbdellahAmghar, and Hassan Sahsah. "Comparison between HC, FOCV and TG MPPT algorithms for PV solar systems using buck converter." In 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 1-5. IEEE, 2017.
Mitrofanov S. V., Baykasenov D. K., & Suleev M. A. Simulation model of autonomous solar power plant with dual-axis solar tracker. In 2018 International Ural Conference on Green Energy,2018, (pp. 90-96). IEEE
Layth M. Abd Ali, L M., Ali, Q. A., Klačková, I., Issa, H. A., Yakimovich, B. A. and Kuvshimov, V. (2021) Developing a thermal design for steam power plants by using concentrating solar power technologies for a clean environment. Acta MontanisticaSlovaca, Volume 26 (4), 773-783 DOI:https://doi.org/10.46544/AMS.v26i4.14
Pilakkat, Deepthi, and S. Kanthalakshmi. "An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions." Solar Energy 178, 2019, pp. 37-47.
Banaei, Mohamad Reza, and Hossein AjdarFaeghiBonab. "A high efficiency nonisolated buck-boost converter based on ZETA converter." IEEE Transactionson Industrial Electronics,2019, 67, no. 3, pp.1991-1998.
Gheisarnejad, Meysam, Hamed Farsizadeh, and Mohammad Hassan Khooban. "A novel nonlinear deep reinforcement learning controller for DC-DC power buck converters." IEEE Transactions on Industrial Electronics 68, no. 8 (2020): 6849-6858.
Ram, J. Prasanth, T. Sudhakar Babu, and N. Rajasekar. "A comprehensive review on solar PV maximum power point tracking techniques." Renewable and Sustainable Energy Reviews 67, 2017, pp.826-847.
Khatib, Tamer, and Wilfried Elmenreich. Modeling of photovoltaic systems using Matlab: Simplified green codes. John Wiley & Sons, 2016.
Анализ различных методов отслеживания точки максимальной мощности при работе солнечных фотоэлектрических систем / Л. М. А. Абдали, Х. А. И. Исса, М. Н. К. Аль-Малики, Б. А. Якимович, В. В. Кувшинов // Интеллектуальные системы в производстве. 2022. Т. 20, № 3. С. 104-113. DOI 10.22213/2410-9304-2022-3-104-113.
Li H., Yang D., and Su W., “An overall distribution particle swarm optimization mppt algorithm for photovoltaic system under partial shading,” IEEE Transactions on Industrial Electronics, 2018, vol. 1, no. 1, pp. 265-275.
Pathy S.; Subramani C.; Sridhar R.; Thentral T.; Padmanaban S. Nature-inspired MPPT algorithms for partially shaded PV systems: A comparative study. Energies 2019, 12, 1451.Senol M., Abbaso˘ glu S., Kukrer O., Babatunde A. A guide in installing large-scale PV power plant for self-consumption mechanism. Sol. Energy, 2019, 132, pp. 518-537.
Aouchiche N.; Aitcheikh M.S.; Becherif M.; Ebrahim M.A. AI-based global MPPT for partial shaded grid connected PV plant via MFO approach. Sol. Energy 2018, 171, 593-603.
Bhukya M. N., & Kota V. R. A quick and effective MPPT scheme for solar power generation during dynamic weather and partial shaded conditions. Engineering Science and Technology, an International Journal,2019, 22 (3), pp. 869-884.
Abo-Elyousr F.K.; Abdelshafy A.M.; Abdelaziz A.Y. MPPT-Based Particle Swarm and Cuckoo Search Algorithms for PV Systems. In Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems; Springer: Cham, Switzerland, 2020; pp. 379-400.
Belkaid A.; Colak I.; Kayisli K. Implementation of a modified P&O-MPPT algorithm adapted for varying solar radiation conditions. Electr. Eng. 2017, 99, pp. 839-846.
Исследование режимов работы комбинированных солнечно-ветровых установок для обеспечения уличного освещения / Л. М. Абдали, Х. А. Исса, М. Н. Аль-Малики, В.В. Кувшинов, Э.А. Бекиров // Строительство и техногенная безопасность. 2022. № 25 (77). С. 75-85.
Zhang Q., Ning Xu., Ye Z. MMPT control method for photovoltaic power supply based on improved variable-step hill-climbing method. Electric Engineering, vol. 2, pp. 55-57, 2018.
Использование метода отслеживания точки максимальной мощности для увеличения эффективности работы фотоэлектрических установок / Л. М. Абдали, М. Н. Аль-Малики, Х. А. Исса, Б. А. Якимович, В. В. Кувшинов // Интеллектуальные системы в производстве. 2022. Т. 20, № 4. C. 106-116. DOI: 10.22213/2410-9304-2022-4-106-116.
Darwesh M. R., & Ghoname M. S. Experimental studies on the contribution of solar energy as a source for heating biogas digestion units. Energy Reports, 2021, 7, pp. 1657-1671.
Abd Ali L. M., Al-Rufaee F. M., Kuvshinov V. V. et al. Study of Hybrid Wind-Solar Systems for the Iraq Energy Complex. Appl. Sol. Energy, 2020, vol. 56, no. 4, pp. 284-290. https://doi.org/10.3103/S0003701X20040027.
Shaw R. N., Walde P., & Ghosh, A. IOT based MPPT for performance improvement of solar PV arrays operating under partial shade dispersion. In 2020 IEEE 9th Power India International Conference (PIICON),2020, pp. 1-4.
Mehrjerdi H. Peer-to-peer home energy management incorporating hydrogen storage system and solar generating units. Renewable Energy, 2020, 156, 183-192.
Pathak P. K., Padmanaban S., Yadav A. K., Alvi P. A., & Khan B. Modified incremental conductance MPPT algorithm for SPV-based grid-tied and stand-alone systems. IET Generation, Transmission & Distribution, 2022, 16(4), pp. 776-791.
Javed K.; Ashfaq H.; Singh R. A new simple MPPT algorithm to track MPP under partial shading for solar photovoltaic systems.Int. J. Green Energy 2020, 17, 48-61.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Лаит Абдали Абдали, Муатаз Кассим Аль-Малики, Али Ганим Аль Баирмани, Борис Анатольевич Якимович, Виктория Викторовна Сяктерева, Владимир Владиславович Кувшинов
This work is licensed under a Creative Commons Attribution 4.0 International License.
