International Journal of Energy and Power Engineering

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Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System

Received: 05 December 2014    Accepted: 22 December 2014    Published: 29 December 2014
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Abstract

The solid oxide fuel cell (SOFC) is widely acknowledged for clean distributed power generation use, but critical process problems frequently occur when the stand-alone fuel cell is directly linked with the electricity grid. To guarantee the optimal operation of the SOFC in a power system, it is essential, that its generation ramp rate and load following is fast enough to sustain power quality. In order to address these problems, a suitable and highly efficient control system will be required to control and track power load demands for complex SOFC power systems under grid connection. Therefore, novel nonlinear hybrid adaptive Fuzzy Neural Network (AFNN) is developed for control of grid connected SOFC. During peak power demand schedules from electric utility grid and large load perturbations, maintaining optimal power quality and load-following is a big challenge. Both the rapid power load following and safe SOFC operation requirement is taken into account in the design of the closed-loop control system. Simulation results showed that the proposed hybrid AFNN enhance the optimal power quality and load-following than conventional PI controller.

DOI 10.11648/j.ijepe.20150401.11
Published in International Journal of Energy and Power Engineering (Volume 4, Issue 1, February 2015)
Page(s) 1-10
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Neural Netwrok, Fuzzy Logic, Distributed Generation, SOFC

References
[1] Y.T. Qi, B. Huang, K.T. Chuang, “Dynamic modeling of solid oxide fuel cell: the effect of diffusion and inherent impedance,” J. Power Sources, vol. 15, pp. 32-47, 2005.
[2] J. Larminie and A. Dicks, “Fuel Cell Systems Explained,”, 2nd ed. West Sussex, U.K: Wiley, 2003.
[3] V. Knyazkin, L. Soder, and C. Canizares, “Control challenges of fuel celldriven distributed generation,” presented at the IEEE Bologna Power-Tech Conf., Italy, June. 23–26, 2003.
[4] N. Akkinapragada and B. H. Chowdhury, “SOFC-based fuel cells for load following stationary applications,” in Power Symp, 38thNorth Amer., Carbondale, IL, 2006,pp. 553–560.
[5] Y. Zhu and K. Tomsovic, “Development of models for analyzing the load following performance of microturbines and fuel cells,” Electr. Power Syst. Res., vol. 62, pp. 1–11, 2002.
[6] R. Kandepu, L. Imsland, B. A. Foss, C. Stiller, B. Thorud, and O. Bolland, “Modeling and control of a SOFC-GT-based autonomous power system,” Energy, vol. 32, pp. 406–417, 2007.
[7] F. Mueller, F. Jabbari, R. Gaynor, and J. Brouwer, “Novel solid oxide fuel cell system controller for rapid load following,” J. Power Sources, vol. 172, pp. 308–323, 2007.
[8] J. T. Pukrushpan, A. G. Stefanopoulou, and H. Peng, Control of Fuel Cell Power Systems: Principles, Modeling, Analysis and Feedback Design. New York: Springer-Verlag, 2004.
[9] X.Wang, B. Huang, and T. Chen, “Data-driven predictive control for solid oxide fuel cells,” J. Process Control, vol. 17, pp. 103–114, 2007.
[10] J. Padulles, G. W. Ault, and J. R. McDonald, “An integrated SOFC plant dynamic model for power systems simulation,” J. Power Sources, vol. 86, pp. 495–500, 2000.
[11] A. M. Murshed, B. Huang, and K. Nandakumar, “Control relevant modeling of planer s olid oxide fuel cell system,” J. Power Sources, vol. 163, no. 2, pp. 830–845, 2007.
[12] R. Lasseter, Dynamic Models for Micro-turbines and Fuel Cells, IEEE-PES Summer meeting," vol.2, pp. 761-766, July 2001.
[13] K. Ro, and S. Rahman, Two-Loop Controller for Maximizing Performance of a Grid-Connected Photovoltaic-Fuel cell Hybrid Power Plant, IEEE Trans. Energy Conversion, vol. 13, Issue 3, pp. 276-281, September 1998.
[14] Don B. Nelson, M. Hashem Nehrir, and Victor Gerez, Economic Evaluation of Grid Connected Fuel-Cell Systems IEEE Transactions on Energy Conversion,"vol. 20, no. 2, June 2005.
[15] W. Du, H.F.Wang, Effect of grid-connected solid oxide fuel cell power generation on power systems small-signal stability," IET Renewable Power Generation, 31st January 2009.
[16] F. Jurado, “Predictive control of solid oxide fuel cells using fuzzy Hammerstein models,” J. Power Sources, vol. 158, pp. 245–253, 2006.
[17] Sanandaji, B. M., Vincent, T. L., Colclasure, A. M., &Kee, R. J., “Modeling and control of tubular solid-oxide fuel cell systems: II. Nonlinear model reduction and model predictive control,” Journal of Power Sources, vol. 196, no. 11, pp. 208-217, 2011.
[18] M. Singh and A. Chandra, “Real-time implementation of ANFIS control for renewable interfacing inverter in 3P4W distribution network,” IEEE Trans. Ind. Electron., vol. 60, no. 1, pp. 121–128, Jan. 2013.
[19] García, P., Garcia, C. A., Fernández, L. M., Llorens, F., &Jurado, F.,“ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries,” IEEE Trans. Industrial Informatics, vol. 10 no. 2, pp. 1107-1117, 2014.
[20] T. A. Johansen, R. Shorten, and R. Murray-Smith, “On the interpretation and identification of dynamic Takagi–Sugeno fuzzy models,” IEEE Trans. Fuzzy Syst., vol. 8, no. 3, pp. 297–313, Jun. 2000.
[21] T. A. Johansen and R. Babuska, “Multi objective identification of Takagi– Sugeno fuzzy models,” IEEE Trans. Fuzzy Syst., vol. 11, no. 6, pp. 847– 860, Dec. 2003.
[22] G. Feng, “A survey on analysis and design of model-based fuzzy control systems,” IEEE Trans. Fuzzy Syst., vol. 14, no. 5, pp. 676–697, Oct. 2006.
[23] T. J. Zhang, G. Feng, and J. H. Lu, “Fuzzy constrained min-max model predictive control using piecewise Lyapunov functions,” IEEE Trans.Fuzzy Syst., vol. 15, no. 4, pp. 686–698, Aug. 2007.
[24] T. J. Zhang, G. Feng, J. H. Lu, and W. G. Xiang, “Robust constrained fuzzy affine model predictive control with application to a fluidized bed combustion plant,” IEEE Trans. Control Syst. Technol., vol. 16, no. 5, pp. 1047–1056, Sep. 2008.
[25] T. J. Zhang, G. Feng, and X.-J.Zeng, “Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control,” Automatica, vol. 45, no. 4, Apr. 2009.
[26] J. L. Bernal-Agustín and R. Dufo-López, “Simulation and optimization of stand-alone hybrid renewable energy systems,” Renew. Sust. Energ.Rev., vol. 13, no. 8, pp. 2111–2118, Oct. 2009.
[27] M. A. Akcayol, “Application of adaptive neuro-fuzzy controller for SRM,” Adv. Eng. Softw., vol. 35, no. 3-4, pp. 129-137, Mar. 2004.
[28] Handbook. Fuel Cell, (2004).EG & G Technical services. Inc., USDOE, 7-18.
[29] Wu, X. J., Zhu, X. J., Cao, G. Y., &Tu, H. Y, Dynamic modeling of SOFC based on a T–S fuzzy model. Simulation Modeling Practice and Theory, vol. 16, no. 5, pp. 494-504, 2008.
[30] Zadeh, L. A, Fuzzy sets. Information and control, vol. 8, no. 3, pp. 338-353, 1965.
[31] Takagi, T., &Sugeno, M. (1983, July). Derivation of fuzzy control rules from human operator’s control actions. In Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis (vol. 6, pp. 55-60).
[32] Ying, H, Fuzzy control and modeling: analytical foundations and applications. Wiley-IEEE Press, 2000.
[33] Sayed T., Tavakolie A. and Razavi A.,“Comparison of adaptive network based fuzzy inference systems and B-splineneuro-fuzzy mode choice models,” Journal of computing in civil engineering, vol. 17, no. 2, pp. 123–130, 2003.
[34] Khaldi, M.R., A.K. Sarkar, K.Y. Lee and Y.M. Park, 1993. The Modal Performance Measure for Parameter Optimization of Power System Stabilizers. IEEE Transactions on Energy Conversion, 8(4): 660-666.
Author Information
  • Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan

  • Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan

  • Department of Electronic Engineering, International Islamic University, Islamabad, Pakistan

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  • APA Style

    Sadaf Sardar, Amjid Ullah Khattak, Shahid Qamar. (2014). Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System. International Journal of Energy and Power Engineering, 4(1), 1-10. https://doi.org/10.11648/j.ijepe.20150401.11

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    ACS Style

    Sadaf Sardar; Amjid Ullah Khattak; Shahid Qamar. Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System. Int. J. Energy Power Eng. 2014, 4(1), 1-10. doi: 10.11648/j.ijepe.20150401.11

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    AMA Style

    Sadaf Sardar, Amjid Ullah Khattak, Shahid Qamar. Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System. Int J Energy Power Eng. 2014;4(1):1-10. doi: 10.11648/j.ijepe.20150401.11

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  • @article{10.11648/j.ijepe.20150401.11,
      author = {Sadaf Sardar and Amjid Ullah Khattak and Shahid Qamar},
      title = {Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {1},
      pages = {1-10},
      doi = {10.11648/j.ijepe.20150401.11},
      url = {https://doi.org/10.11648/j.ijepe.20150401.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijepe.20150401.11},
      abstract = {The solid oxide fuel cell (SOFC) is widely acknowledged for clean distributed power generation use, but critical process problems frequently occur when the stand-alone fuel cell is directly linked with the electricity grid. To guarantee the optimal operation of the SOFC in a power system, it is essential, that its generation ramp rate and load following is fast enough to sustain power quality. In order to address these problems, a suitable and highly efficient control system will be required to control and track power load demands for complex SOFC power systems under grid connection. Therefore, novel nonlinear hybrid adaptive Fuzzy Neural Network (AFNN) is developed for control of grid connected SOFC. During peak power demand schedules from electric utility grid and large load perturbations, maintaining optimal power quality and load-following is a big challenge. Both the rapid power load following and safe SOFC operation requirement is taken into account in the design of the closed-loop control system. Simulation results showed that the proposed hybrid AFNN enhance the optimal power quality and load-following than conventional PI controller.},
     year = {2014}
    }
    

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    T1  - Hybrid Adaptive Fuzzy Neural Network Control for Grid-Connected SOFC System
    AU  - Sadaf Sardar
    AU  - Amjid Ullah Khattak
    AU  - Shahid Qamar
    Y1  - 2014/12/29
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijepe.20150401.11
    DO  - 10.11648/j.ijepe.20150401.11
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 1
    EP  - 10
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20150401.11
    AB  - The solid oxide fuel cell (SOFC) is widely acknowledged for clean distributed power generation use, but critical process problems frequently occur when the stand-alone fuel cell is directly linked with the electricity grid. To guarantee the optimal operation of the SOFC in a power system, it is essential, that its generation ramp rate and load following is fast enough to sustain power quality. In order to address these problems, a suitable and highly efficient control system will be required to control and track power load demands for complex SOFC power systems under grid connection. Therefore, novel nonlinear hybrid adaptive Fuzzy Neural Network (AFNN) is developed for control of grid connected SOFC. During peak power demand schedules from electric utility grid and large load perturbations, maintaining optimal power quality and load-following is a big challenge. Both the rapid power load following and safe SOFC operation requirement is taken into account in the design of the closed-loop control system. Simulation results showed that the proposed hybrid AFNN enhance the optimal power quality and load-following than conventional PI controller.
    VL  - 4
    IS  - 1
    ER  - 

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