Research Article |
An Intelligent Approach for MPPT Extraction in Hybrid Renewable Energy Sources
Author(s): N. Ravi*, R. Arunmozhi and T. Chandra Shekar
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 3
Publisher : FOREX Publication
Published : 25 July 2024
e-ISSN : 2347-470X
Page(s) : 799-805
Abstract
A multi-source power system that integrates sustainable energy sources for power generation. MPPT, or Maximum Power Point Tracking, is a method employed to optimise the power generation of sources, such as solar panels or wind turbines. Since the efficiency of these sources can vary due to environmental conditions (like sunlight intensity or wind speed), MPPT algorithms optimize the electrical operational parameters of the modules to guarantee they are functioning at their highest efficiency. In the context of MPPT, fuzzy logic is used to handle the uncertainties and nonlinearities in the behaviour of these sources. It allows for a more adaptive and resilient control strategy, which can be particularly effective in fluctuating environmental conditions. When fuzzy logic is applied to MPPT in a hybrid power system, the goal is to intelligently manage and optimize the power output from various sources. This process involves continuously monitoring environmental factors and the performance of each power source. This integration of fuzzy logic into MPPT for hybrid power systems represents an advanced step in renewable energy management, making it possible to get the most out of these resources even under varying and unpredictable conditions.
Keywords: Fuzzy Logic Controller
, Maximum Power Point Tracking
, Solar
, Wind
.
N. Ravi*, Research Scholar, Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Tamil Nadu; Email: rv.nenavath@gmail.com
R. Arunmozhi, Assistant Professor, Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, Tamil Nadu; Email: arunau2006@gmail.com
T. Chandra Shekar, Associate Professor Department of Electrical and Electronics Engineering, Maturi Venkata Subba Rao (MVSR) Engineering College, Nadergul, Hyderabad; Email: Chandrashekar_eee@mvsrec.edu.in;
-
[1] S. A. Hamad and M. A. Ghalib, “Fuzzy MPPT operation-based model predictive flux control for linear induction motors,” Int. J. Hydrog. Energy, vol. 50, pp. 1035–1044, 2024.
-
[2] M. A. Abdelkareem, S. I. Alshathri, M. S. Masdar, and A. G. Olabi, “Adaptive Neuro-Fuzzy Inference System Modeling and Optimization of Microbial Fuel Cells for Wastewater Treatment,” Water, vol. 15, no. 20, Art. no. 20, Jan. 2023, doi: 10.3390/w15203564.
-
[3] C. Hussaian Basha, M. Palati, C. Dhanamjayulu, S. M. Muyeen, and P. Venkatareddy, “A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions,” Sci. Rep., vol. 14, no. 1, p. 1609, 2024.
-
[4] A. S. Pawar, N. B. Chopade, M. T. Kolte, and H. Mehta, “Photovoltaic Fuzzy-MPPT Based Smart Battery Charger for Low Power Applications,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 2s, pp. 140–162, 2024.
-
[5] O. Mrhar, K. Kandoussi, M. Eljouad, and M. Louzazni, “A Novel Mixed Mppt Algorithm Based on P&O and Fuzzy-Logic Approaches,” Available SSRN 4684291, Accessed: Jan. 27, 2024. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4684291.
-
[6] M. Kumar, S. Sen, S. Kumar, and J. Samantaray, “An Adaptive Fuzzy Controller-Based Distributed Voltage Control Strategy for a Remote Microgrid System with Solar Energy and Battery Support,” IEEE Trans. Ind. Appl., 2024, Accessed: Jan. 27, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10382643.
-
[7] J. Pande, P. Nasikkar, K. Kotecha, and A. Abraham, “An Ingenious Technique to Track the Maximum Power Point for a Wind Energy System,” IEEE Access, 2024, Accessed: Jan. 27, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10400470/
-
[8] M. Sundaram, K. R. Sugavavam, V. S. Chandrika, and T. J. Catherine, “Controller based SEPIC converter fuzzy for wind renewable energy system,” in AIP Conference Proceedings, AIP Publishing, 2024. Accessed: Jan. 27, 2024. https://pubs.aip.org/aip/acp/article/2512/1/020100/3052380.
-
[9] M. M. Elymany, M. A. Enany, and N. A. Elsonbaty, “Hybrid optimized-ANFIS based MPPT for hybrid microgrid using zebra optimization algorithm and artificial gorilla troops optimizer,” Energy Convers. Manag., vol. 299, p. 117809, 2024.
-
[10] A. Bahri, N. Mezhoud, K. Benamrane, B. Ayachi, T. Abdelkrim, and M. Bechouat, “Fuzzy Logic Mppt Controller for a Standalone PV/Battery Hybrid Energy System”, Accessed: Jan. 27, 2024. [Online]. Available: https://www.asjp.cerist.dz/en/downArticle/736/5/2/238717.
-
[11] D. J. K. Kishore, M. R. Mohamed, K. Sudhakar, and K. Peddakapu, “A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions,” Neural Comput. Appl., Jan. 2024, doi: 10.1007/s00521-023-09407-x.
-
[12] Z. Ishrat, A. K. Gupta, and S. Nayak, “A comprehensive review of MPPT techniques based on ML applicable for maximum power in solar power systems,” J. Renew. Energy Environ., vol. 11, no. 1, pp. 28–37, 2024.
-
[13] R. K. Pachauri, V. Sharma, A. Kumar, Shashikant, A. A. Khan, and P. Sharma, “Conventional and AI‐Based MPPT Techniques for Solar Photovoltaic System‐Based Power Generation: Constraints and Future Perception,” in Clean and Renewable Energy Production, 1st ed., S. Mondal, A. Kumar, R. K. Pachauri, A. K. Mondal, V. K. Singh, and A. K. Sharma, Eds., Wiley, 2024, pp. 355–374. doi: 10.1002/9781394174805.ch15.
-
[14] A. Refaat et al., “Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions,” Renew. Energy, vol. 220, p. 119718, 2024.
-
[15] H. Alnuman, A. Armghan, A. Kumar, E. T. Alanazi, and A. Sahbani, “Solar PV system fed improved power quality converter with digital proportional resonant controller,” Comput. Electr. Eng., vol. 114, p. 109086, 2024.
-
[16] N. Vázquez and J. Vázquez, “Photovoltaic system conversion,” in Power Electronics Handbook, Elsevier, 2024, pp. 781–795. Accessed: Jan. 30, 2024. https://www.sciencedirect.com/science/article/pii/B9780323992169000238.
-
[17] T. Al Smadi, A. Handam, K. S. Gaeid, A. Al-Smadi, and Y. Al-Husban, “Artificial intelligent control of energy management PV system,” Results Control Optim., vol. 14, p. 100343, 2024.
-
[18] H. P. Corrêa and F. H. T. Vieira, “Analytical estimation of three-phase inverter input impedance applied to MPPT in off-grid variable-voltage photovoltaic systems,” Int. J. Electr. Power Energy Syst., vol. 155, p. 109482, 2024.
-
[19] E. Assareh, S. S. M. Asl, M. Ahmadinejad, M. Parvaz, and M. Ghodrat, “Optimization of a solar energy system integrating cooling, hot water, and power units in Australian cities: A climate-based analysis and cost-efficiency investigation,” Int. J. Hydrog. Energy, vol. 49, pp. 353–375, 2024.
-
[20] N. Ennemiri, A. Berrada, A. Emrani, J. Abdelmajid, and R. El Mrabet, “Optimization of an off-grid PV/biogas/battery hybrid energy system for electrification: A case study in a commercial platform in Morocco,” Energy Convers. Manag. X, vol. 21, p. 100508, 2024.
-
[21] A. A. Hassan and K. El-Rayes, “Optimal use of renewable energy technologies during building schematic design phase,” Appl. Energy, vol. 353, p. 122006, 2024.
-
[22] Namburi Nireekshana, M. Anil Goud, R. Bhavani Shankar, and G. Nitin Sai Chandra, “Solar Powered Multipurpose Agriculture Robot,” May 2023, doi: 10.5281/ZENODO.7940166.
-
[23] Namburi Nireekshana, Tanvi H Nerlekar, P. N. Kumar, and M. M. Bajaber, “An Innovative Solar Based Robotic Floor Cleaner,” May 2023, doi: 10.5281/ZENODO.7918621.
-
[24] N. Nireekshana, R. Ramachandran, and G. V. Narayana, “A Peer Survey on Load Frequency Control in Isolated Power System with Novel Topologies,” Int. J. Eng. Adv. Technol. IJEAT, vol. 11, no. 1, pp. 82–88, Oct. 2021, doi: 10.35940/ijeat. A3124.1011121.
-
[25] N. Nireekshana, R. Ramachandran, and G. V. Narayana, “A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems,” Int.J. Electr. Electron. Res., vol. 11, no. 2, pp. 371–377, Jun. 2023, doi: 10.37391/ijeer.110218.
-
[26] N. Nireekshana, R. Ramachandran, and G. V. Narayana, “Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation,” E3S Web Conf., vol. 472, p. 02005, 2024, doi: 10.1051/e3sconf/202447202005.
-
[27] N. Nireekshana, R. Ramachandran, and G. V. Narayana, “A New Soft Computing Fuzzy Logic Frequency Regulation Scheme for Two Area Hybrid Power Systems,” Int. J. Electr. Electron. Res., vol. 11, no. 3, pp. 705–710, Aug. 2023, doi: 10.37391/IJEER.110310.