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Warianty tytułu
Języki publikacji
Abstrakty
This study presents an intelligent Maximum Power Point Tracking (MPPT) control strategy for variable-speed wind turbine generators, based on the Crow Search Algorithm (CSA) to maximize power generation under wind fluctuations. The proposed CSA-based MPPT method is designed to improve the dynamic response and efficiency of wind energy conversion systems by effectively tracking the optimal operating point. The performance of the CSA-based approach is compared with a conventional torque regulation method, evaluating key metrics such as convergence speed and robustness under turbulent wind conditions. Simulation results demonstrate that the CSA-based MPPT controller outperforms the conventional method, achieving faster convergence to the maximum power point, reduced power oscillations, and improved energy capture efficiency. The results highlight the potential of bio-inspired algorithms like CSA in advancing MPPT control for renewable energy systems, offering a promising alternative to traditional methods for enhancing the performance and reliability of wind turbine generators.
Wydawca
Rocznik
Tom
Strony
189--202
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
autor
- Laboratory of Automatic, Electrical Systems and Environment, the National Engineering School of Monastir, University of Monastir, Tunisia
autor
- Laboratory of Automatic, Electrical Systems and Environment, the National Engineering School of Monastir, University of Monastir, Tunisia
Bibliografia
- 1. Da Silva, M.M. Power and Gas Asset Management Regulation, Planning and Operation of Digital Energy Systems. Springer Nature Switzerland AG 2020.
- 2. Alremali, F.A.M., Yaylacı, E.K., Uluer, İ. Optimization of proportional-integral controllers of grid-connected wind energy conversion system using grey wolf optimizer based on artificial neural network for power quality improvement. Advances in Science and Technology. Research Journal, 2022; 16(3).
- 3. Teklehaimanot, Y.K., Akingbade, F.K., Ubochi, B.C., Ale, T.O. A review and comparative analysis of maximum power point tracking control algorithms for wind energy conversion systems. International Journal of Dynamics and Control, 2024; 1–23.
- 4. Kumar, D., Chatterjee, K. A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and sustainable energy reviews, 2016; 55: 957–970.
- 5. Pande, J., Nasikkar, P., Kotecha, K., Varadarajan, V. A review of maximum power point tracking algorithms for wind energy conversion systems. Journal of Marine Science and Engineering, 2021; 9(11): 1187.
- 6. Hannachi, M., Elbeji, O., Benhamed, M., Sbita, L. Comparative study of four MPPT for a wind power system. Wind Engineering, 2021; 45(6): 1613–1622.
- 7. Nouriani, A., Moradi, H. Variable speed wind turbine power control: A comparison between multiple MPPT based methods. International Journal of Dynamics and Control, 2022; 10(2): 654–667.
- 8. Tiwari, R., Babu, N.R. Fuzzy logic based MPPT for permanent magnet synchronous generator in wind energy conversion system. IFAC-PapersOnLine, 2016; 49(1): 462–467.
- 9. Yessef, M., Bossoufi, B., Taoussi, M., Lagrioui, A., Chojaa, H. Overview of control strategies for wind turbines: ANNC, FLC, SMC, BSC, and PI controllers. Wind Engineering, 2022; 46(6): 1820–1837.
- 10. Govinda Chowdary, V., Udhay Sankar, V., Mathew, D., Hussaian Basha, C.H., Rani, C. Hybrid fuzzy logic-based MPPT for wind energy conversion system. In Soft Computing for Problem Solving: SocProS 2018, Springer Singapore, 2020; 2: 951–968.
- 11. Minai, A.F., Malik, H. Metaheuristics paradigms for renewable energy systems: advances in optimization algorithms. Metaheuristic and Evolutionary Computation: Algorithms and Applications, 2021; 35–61.
- 12. Bouchakour, A., Zarour, L., Bessous, N., Bechouat, M., Borni, A., Zaghba, L., Ghoneim, S.S. MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance. Scientific Reports, 2024; 14(1): 17891.
- 13. Azzouz, S., Messalti, S., Harrag, A. Innovative PID-GA MPPT controller for extraction of maximum power from variable wind turbine. Przegląd Elektrotechniczny, 2019; 95.
- 14. Ben Smida, M., Azar, A.T., Sakly, A., Hameed, I.A. Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control. Frontiers in Energy Research, 2024; 12: 1381376.
- 15. Güven, A.F., Samy, M.M. Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches. Energy Conversion and Management, 2022; 269: 116058.
- 16. Güven, A.F., Yörükeren, N.U.R.A.N., Tag-Eldin, E., Samy, M.M. Multi-objective optimization of an islanded green energy system utilizing sophisticated hybrid metaheuristic approach. IEEE Access, 2023; 11: 103044–103068.
- 17. Kassa, Y., Zhang, J.H., Zheng, D.H., Wei, D. A GA-BP hybrid algorithm-based ANN model for wind power prediction. In 2016 IEEE Smart Energy Grid Engineering (SEGE), IEEE, 2016; 158–163.
- 18. Bouaouda, A., Sayouti, Y. Hybrid meta-heuristic algorithms for optimal sizing of hybrid renewable energy system: a review of the state-of-the-art. Archives of Computational Methods in Engineering, 2022; 29(6): 4049–4083.
- 19. Ganji, E., Mahdavian, M., Eshaghpour, I., Janghorban, M. Designing and modeling of control strategies based on multi-objective optimization for a PMSG wind turbine: a study based on the grid errors and wind speed. Advances in Science and Technology. Research Journal, 2019; 13(4).
- 20. Smida, M.B., Sakly, A. Different conventional strategies of pitch angle control for variable speed wind turbines. In 2014 15th international conference on sciences and techniques of automatic control and computer engineering (STA), IEEE, 2014; 803–808.
- 21. Smida, M.B., Sakly, A. Fuzzy logic control of a hybrid renewable energy system: A comparative study. Wind Engineering, 2021; 45(4): 793–806.
- 22. Ben Smida, M., Sakly, A. Pitch angle control for grid-connected variable-speed wind turbine system using fuzzy logic: A comparative study. Wind Engineering, 2016; 40(6): 528–539.
- 23. Ben Smida, M., Sakly, A. Smoothing wind power fluctuations by particle swarm optimization-based pitch angle controller. Transactions of the Institute of Measurement and Control, 2019; 41(3): 647–656.
- 24. Goyal, S., Deolia, V.K., Agrawal, S. An advanced neuro-fuzzy tuned PID controller for pitch control of horizontal axis wind turbines. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 2022; 20(2): 296–305.
- 25. Abd Elkader, F., Elhady, B., Kalas, A. Sensor and Sensorless Speed Control of Doubly Fed Induction Generator Wind Turbines for Maximum Power Point Tracking. Port-Said Engineering Research Journal, 2014; 18(2): 8–16.
- 26. Meraihi, Y., Gabis, A.B., Ramdane-Cherif, A., Acheli, D. A comprehensive survey of Crow Search Algorithm and its applications. Artificial Intelligence Review, 2021; 54(4): 2669–2716.
- 27. Smida, B.M., Azar, A.T., Sakly, A., Hameed I.A. Analyzing grid connected shaded photovoltaic systems with steady state stability and crow search MPPT control. Frontiers in Energy Research, Section Solar Energy, 2024; 12.
- 28. Houam, Y., Terki, A., Bouarroudj, N. An efficient metaheuristic technique to control the maximum power point of a partially shaded photovoltaic system using crow search algorithm (CSA). Journal of Electrical Engineering & Technology, 2021; 16: 381–402.
- 29. Borni, A., Bechouat, M., Bessous, N., Bouchakour, A., Laid, Z., Zaghba, L. Comparative study of P&O and fuzzy MPPT controllers and their optimization using PSO and GA to improve wind energy system. International Journal for Engineering Modelling, 2021; 34(2): 55–76.
- 30. Chaicharoenaudomrung, K., Areerak, K., Areerak, K., Bozhko, S., Hill, C.I. Maximum power point tracking for stand‐alone wind energy conversion system using FLC‐P&O method. IEEJ Transactions on Electrical and Electronic Engineering, 2020; 15(12): 1723–1733.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9379f52f-82be-4a4a-93e9-f90b2cabe1a9
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