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Yerkin, Y., Al Mansor, A.H.O., Ibrahim, A.A., Zaboun, A.R.T., Abbas, J.K., Hlail, S.H., Lafta, D.A., Al-Majdi, K.. (1402). Boost DC-DC Converter Design for Improved Performance and Stability of Fuel Cell Using Model Predictive Control and Firefly Optimization Algorithm. سامانه مدیریت نشریات علمی, 11(Special Issue), 55-62. doi: 10.22098/joape.2023.13823.2065
Y. Yerkin; A.H.O. Al Mansor; A.A. Ibrahim; A.R.T. Zaboun; J.K. Abbas; S.H. Hlail; D.A. Lafta; K. Al-Majdi. "Boost DC-DC Converter Design for Improved Performance and Stability of Fuel Cell Using Model Predictive Control and Firefly Optimization Algorithm". سامانه مدیریت نشریات علمی, 11, Special Issue, 1402, 55-62. doi: 10.22098/joape.2023.13823.2065
Yerkin, Y., Al Mansor, A.H.O., Ibrahim, A.A., Zaboun, A.R.T., Abbas, J.K., Hlail, S.H., Lafta, D.A., Al-Majdi, K.. (1402). 'Boost DC-DC Converter Design for Improved Performance and Stability of Fuel Cell Using Model Predictive Control and Firefly Optimization Algorithm', سامانه مدیریت نشریات علمی, 11(Special Issue), pp. 55-62. doi: 10.22098/joape.2023.13823.2065
Yerkin, Y., Al Mansor, A.H.O., Ibrahim, A.A., Zaboun, A.R.T., Abbas, J.K., Hlail, S.H., Lafta, D.A., Al-Majdi, K.. Boost DC-DC Converter Design for Improved Performance and Stability of Fuel Cell Using Model Predictive Control and Firefly Optimization Algorithm. سامانه مدیریت نشریات علمی, 1402; 11(Special Issue): 55-62. doi: 10.22098/joape.2023.13823.2065

Boost DC-DC Converter Design for Improved Performance and Stability of Fuel Cell Using Model Predictive Control and Firefly Optimization Algorithm

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue، اسفند 2023، صفحه 55-62    اصل مقاله (568.96 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.13823.2065
نویسندگان
Y. Yerkin* 1؛ A.H.O. Al Mansor2؛ A.A. Ibrahim3؛ A.R.T. Zaboun4؛ J.K. Abbas5؛ S.H. Hlail6؛ D.A. Lafta7؛ K. Al-Majdi8
1Kazakh National Agrarian Research University, Abai 8 Almaty, Kazakhstan
2Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq
3Department of Biomedical Engineering, Mazaya University College, Iraq
4Department of Biomedical Engineering, Al-Esraa University College, Baghdad, Iraq
5Department of Biomedical Engineering, AL-Nisour University College, Baghdad, Iraq
6College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq
7College of Petroleum Engineering, Al-Ayen University, Thi-Qar , Iraq
8Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq
چکیده
    DC-DC converters play a crucial role in fuel cell power generation systems, serving as an interface between the fuel cell and the load. Boost converters have gained popularity due to their ability to increase input voltage. However, the performance and efficiency of DC-DC converters in fuel cell power systems have posed significant challenges. This study proposes the use of Model Predictive Control (MPC) and the Firefly Optimization Algorithm (FA) for designing and controlling boost DC-DC converters in the most efficient manner. Initially, stability analysis and precise modeling techniques were employed to optimize the characteristics of boost DC-DC converters in fuel cell power generation systems. Subsequently, the predictive control method, utilizing the Firefly optimization algorithm, was applied to enhance converter performance under diverse conditions. The outcomes of the designed control system were compared with conventional methods. Both predictive control and the Firefly optimization algorithm were integrated into the design and control processes of boost DC-DC converters in fuel cell. Based on the simulation results and stability evaluations, the application of the Firefly algorithm and predictive control led to a significant improvement, increasing the system efficiency by approximately 4.7%. These findings highlight the effectiveness of the proposed approach in enhancing the performance of DC-DC boost converters in fuel cell.
کلیدواژه‌ها
Fuel cell؛ DC-DC converters؛ model predictive control؛ stability؛ controllability؛ firefly optimization algorithm؛ system efficiency
مراجع
  1. Fan, Z. Tu, and S. H. Chan, “Recent development of hydrogen and fuel cell technologies: A review,” Energy Rep., vol. 7, pp. 8421–8446, 2021.
  2. Wang, D. F. R. Diaz, K. S. Chen, Z. Wang, and X. C. Adroher, “Materials, technological status, and fundamentals of pem fuel cells–a review,” Mater. today, vol. 32, pp. 178–203, 2020.
  3. Mekhilef, R. Saidur, and A. Safari, “Comparative study of different fuel cell technologies,” Renewable Sustainable Energy Rev., vol. 16, no. 1, pp. 981–989, 2012.
  4. Rajabi, F. M. Shahir, and R. Sedaghati, “New unidirectional step-up dc-dc converter for fuel-cell vehicle: Design and implementation,” Electr. Power Syst. Res., vol. 212, p. 108653, 2022.
  5. Jarin, S. Akkara, S. S. Mole, A. Manivannan, and A. I. Selvakumar, “Fuel vehicle improvement using high voltage gain in dc-dc boost converter,” Renewable Energy Focus, vol. 43, pp. 228–238, 2022.
  6. Zhou, Q. Zhang, and J. Li, “Topology and control of fuel cell generation converters,” Energ., vol. 16, no. 11, p. 4525, 2023.
  7. Cho and J.-S. Lai, “High-efficiency multiphase dc–dc converter for fuel-cell-powered truck auxiliary power unit,” IEEE Trans. Veh. Technol., vol. 62, no. 6, pp. 2421–2429, 2012.
  8. Kirubakaran, S. Jain, and R. Nema, “The pem fuel cell system with dc/dc boost converter: Design, modeling and simulation,” Int. J. recent trends Eng., vol. 1, no. 3, pp. 157–161, 2009.
  9. N. Esfahani, M. Delshad, and M. B. Tavakoli, “A new family of soft single switched dc-dc converters with lossless passive snubber,” Majlesi J. Electr. Eng., vol. 14, no. 2, pp. 51–59, 2020.
  10. A. Abbas, T. A. Abdul-Jabbar, A. A. Obed, A. Kersten, M. Kuder, and T. Weyh, “A comprehensive review and analytical comparison of non-isolated dc-dc converters for fuel cell applications,” Energ., vol. 16, no. 8, p. 3493, 2023.
  11. Narayanaswamy and S. Mandava, “Non-isolated multiport converter for renewable energy sources: A comprehensive review,” Energ., vol. 16, no. 4, p. 1834, 2023.
  12. Hayat, D. Sibtain, A. F. Murtaza, S. Shahzad, M. S. Jajja, and H. Kilic, “Design and analysis of input capacitor in dc–dc boost converter for photovoltaic-based systems,” Sustainability, vol. 15, no. 7, p. 6321, 2023.
  13. Guiza, D. Ounnas, S. Youcef, and A. Bouden, “Pid based on a single artificial neural network algorithm for dc-dc boost converter,” Indones. J. Electr. Eng. Comput. Sci., vol. 31, no. 1, pp. 160–169, 2023.
  14. Sivakumar, M. J. Sathik, P. Manoj, and G. Sundararajan, “An assessment on performance of dc–dc converters for renewable energy applications,” Renewable Sustainable Energy Rev., vol. 58, pp. 1475–1485, 2016.
  15. Rehman, I. Al-Bahadly, and S. Mukhopadhyay, “Multiinput dc–dc converters in renewable energy applications–an overview,” Renewable Sustainable Energy Rev., vol. 41, pp. 521–539, 2015.
  16. P. Siwakoti, F. Blaabjerg, and P. C. Loh, “Quasi-y-source boost dc–dc converter,” IEEE Trans. Power Electron., vol. 30, no. 12, pp. 6514–6519, 2015.
  17. Kumar and D. Kumar, “A systematic review on firefly algorithm: past, present, and future,” Arch. Comput. Methods Eng., vol. 28, pp. 3269–3291, 2021.
  18. K. Joyo, Y. Raza, K. Kadir, K. Naidu, S. F. Ahmed, and S. Khan, “Firefly optimised pid control for upper extremity rehabilitation robot,” in 2019 IEEE Int. Conf. Smart Instrum. Meas. Appl. (ICSIMA), pp. 1–5, IEEE, 2019.
  19. Jain, S. Sharma, and S. Sharma, “Firefly algorithm,” Nat.-Inspired Algorithms Appl., pp. 157–180, 2021.
  20. Zhang, S. Li, and K. Jermsittiparsert, “Optimal design of a proton exchange membrane fuel cell-based combined cooling, heating, and power system by an enhanced version of farmland fertility optimizer,” Energy Sources Part A, pp. 1–20, 2020.
  21. -H. Chen, Y.-W. Li, C.-C. Lin, M.-H. Chang, A. Saravanakumar, and L. H. Saw, “Multi-objective optimization design for pressure uniformity in a proton exchange membrane fuel cell stack,” Int. J. Energy Res., vol. 46, no. 13, pp. 18947–18963, 2022.
  22. Fister, I. Fister Jr, X. Yang, and J. Brest, “A comprehensive review of firefly algorithms. swarm and evolutionary computation, 13, 34-46,” 2013.
  23. Ali, M. A. Othman, M. N. Husain, and M. H. Misran, “A review of firefly algorithm,” ARPN J. Eng. Appl. Sci., vol. 9, no. 10, pp. 1732–1736, 2014.
  24. Alaswad, A. Omran, J. R. Sodre, T. Wilberforce, G. Pignatelli, M. Dassisti, A. Baroutaji, and A. G. Olabi, “Technical and commercial challenges of proton-exchange membrane (pem) fuel cells,” Energ., vol. 14, no. 1, p. 144, 2020.
  25. Sun, Y. Jin, L. Pan, J. Shen, and K. Y. Lee, “Efficiency analysis and control of a grid-connected pem fuel cell in distributed generation,” Energy Convers. Manage., vol. 195, pp. 587–596, 2019.
  26. Daud, R. Rosli, E. Majlan, S. Hamid, R. Mohamed, and T. Husaini, “Pem fuel cell system control: A review,” Renewable Energy, vol. 113, pp. 620–638, 2017.
  27. Ma, M. Lin, T.-E. Lin, T. Lan, X. Liao, F. Maréchal, Y. Yang, C. Dong, L. Wang, et al., “Fuel cell-battery hybrid systems for mobility and off-grid applications: A review,” Renewable Sustainable Energy Rev., vol. 135, p. 110119, 2021.
  28. Ferahtia, A. Djeroui, H. Rezk, A. Houari, S. Zeghlache, and M. Machmoum, “Optimal control and implementation of energy management strategy for a dc microgrid,” Energy, vol. 238, p. 121777, 2022.
  29. A. Sinha, M. Z. Ansari, A. K. Shukla, T. Choudhary, et al., “Comprehensive review on integration strategies and numerical modeling of fuel cell hybrid system for power & heat production,” Int. J. Hydrogen Energy, 2023.
  30. Jamuna and S. Saravanan, “Sustainable future of transport and stationary applications using hydrogen fuel cell technology,”
  31. Wu, J. Yao, P. Zhu, F. Yang, X. Meng, S. Kurko, and Z. Zhang, “Study of mw-scale biogas-fed sofc-wgs-tsapemfc hybrid power technology as distributed energy system: Thermodynamic, exergetic and thermo-economic evaluation,” Int. J. Hydrogen Energy, vol. 46, no. 19, pp. 11183–11198, 2021.
  32. Yan, G. Wang, Z. Lu, P. Tan, T. H. Kwan, H. Xu, B. Chen, M. Ni, and Z. Wu, “Techno-economic evaluation and technology roadmap of the mwe-scale sofc-pemfc hybrid fuel cell system for clean power generation,” J. Cleaner Prod., vol. 255, p. 120225, 2020.
  33. K. Bhuyan, P. K. Hota, and B. Panda, “Power quality analysis of a grid-connected solar/wind/hydrogen energy hybrid generation system,” Int. J. Power Electron. Drive Syst., vol. 9, no. 1, p. 377, 2018.
  34. Chitsaz, M. A. Haghghi, and J. Hosseinpour, “Thermodynamic and exergoeconomic analyses of a proton exchange membrane fuel cell (pemfc) system and the feasibility evaluation of integrating with a proton exchange membrane electrolyzer (peme),” Energy Convers. Manage., vol. 186, pp. 487–499, 2019.
  35. T. Lopez, Adaptive robust model predictive control for nonlinear systems. PhD thesis, Massachusetts Institute of Technology, 2019.
  36. Zhang, S. Tian, and X. Lin, “Recent advances and applications of ai-based mathematical modeling in predictive control of hybrid electric vehicle energy management in china,” Electron., vol. 12, no. 2, p. 445, 2023.
  37. Schwenzer, M. Ay, T. Bergs, and D. Abel, “Review on model predictive control: An engineering perspective,” Int. J. Adv. Manuf. Technol., vol. 117, no. 5-6, pp. 1327–1349, 2021.
  38. Cuevas, F. Fausto, and A. González, New Advancements in Swarm Algorithms: Operators and Applications. Springer, 2020.
  39. -S. Yang and X.-S. He, “Why the firefly algorithm works?,” Nat.-Inspired Algorithms Appl. Optim., pp. 245–259, 2018.
  40. -S. Yang and A. Slowik, “Firefly algorithm,” in Swarm intell. algorithms, pp. 163–174, CRC Press, 2020.
  41. K. Kumari, A. Srinivas, S. A. Abhay, and K. Nandikol, “Execution of firefly optimization algorithm in dc-dc landsman converter to control microgrid voltage,” in AIP Conf. Proc., vol. 2455, AIP Publishing, 2022.
  42. Ravindrababu, G. Saraswathi, and K. Sudha, “Design of upfc-pss using firefly algorithm for stability improvement of multi machine system under contingency,” Majlesi J. Electr. Eng., vol. 13, no. 2, pp. 21–39, 2019.
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