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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
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