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

Sadaf Sardar; Amjid Ullah Khattak; Shahid Qamar

doi:doi:10.11648/j.ijepe.20150401.11

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

Sadaf Sardar, Amjid Ullah Khattak, Shahid Qamar

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 1)

Received: 5 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.

Published in	International Journal of Energy and Power Engineering (Volume 4, Issue 1)
DOI	10.11648/j.ijepe.20150401.11
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), 2014. Published by Science Publishing Group

Keywords

Neural Netwrok, Fuzzy Logic, Distributed Generation, SOFC

References

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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://article.sciencepublishinggroup.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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TY  - JOUR
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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Author Information

Sadaf Sardar

Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan
Amjid Ullah Khattak

Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan
Shahid Qamar

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

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Plain Text BibTeX RIS

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
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  -

Copy | Download