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Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System

Received: 21 July 2013     Published: 30 August 2013
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Abstract

An online auxiliary control was designed for Static Var Compensator (SVC) to improve the poorly damped oscillations in multi-machine power system subjected to small and large disturbances. This paper presents auxiliary control based on Adaptive NeuroFuzzy (ANF) control using triangular membership function. Such a model free based control does not require any prior information about the system and is robust to system changes quickly. To minimize the cost function and to tune the parameters of the antecedent and consequent part of the proposed control, a Gradient Descent (GD) learning algorithm is used. The time domain simulation results were carried out for two machine test system for four different cases. In order to exploit the performance and robustness of ANF control, the results were compared with conventional PI and no control. Simulation results and performance indices reveal that the proposed control outperforms during various fault conditions and hence improves the transient stability to a great extend.

Published in International Journal of Energy and Power Engineering (Volume 2, Issue 4)
DOI 10.11648/j.ijepe.20130204.15
Page(s) 172-183
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), 2013. Published by Science Publishing Group

Keywords

Static Var Compensator, Muti-Machine Power System, Adaptive Neurofuzzy, Triangular Membership Function, Gradient Descent Learning Algorithm

References
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Cite This Article
  • APA Style

    Saima Ali, Shahid Qamar, Laiq Khan, Umer Akram. (2013). Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System. International Journal of Energy and Power Engineering, 2(4), 172-183. https://doi.org/10.11648/j.ijepe.20130204.15

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    ACS Style

    Saima Ali; Shahid Qamar; Laiq Khan; Umer Akram. Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System. Int. J. Energy Power Eng. 2013, 2(4), 172-183. doi: 10.11648/j.ijepe.20130204.15

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    AMA Style

    Saima Ali, Shahid Qamar, Laiq Khan, Umer Akram. Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System. Int J Energy Power Eng. 2013;2(4):172-183. doi: 10.11648/j.ijepe.20130204.15

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  • @article{10.11648/j.ijepe.20130204.15,
      author = {Saima Ali and Shahid Qamar and Laiq Khan and Umer Akram},
      title = {Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System},
      journal = {International Journal of Energy and Power Engineering},
      volume = {2},
      number = {4},
      pages = {172-183},
      doi = {10.11648/j.ijepe.20130204.15},
      url = {https://doi.org/10.11648/j.ijepe.20130204.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20130204.15},
      abstract = {An online auxiliary control was designed for Static Var Compensator (SVC) to improve the poorly damped oscillations in multi-machine power system subjected to small and large disturbances. This paper presents auxiliary control based on Adaptive NeuroFuzzy (ANF) control using triangular membership function. Such a model free based control does not require any prior information about the system and is robust to system changes quickly. To minimize the cost function and to tune the parameters of the antecedent and consequent part of the proposed control, a Gradient Descent (GD) learning algorithm is used. The time domain simulation results were carried out for two machine test system for four different cases. In order to exploit the performance and robustness of ANF control, the results were compared with conventional PI and no control. Simulation results and performance indices reveal that the proposed control outperforms during various fault conditions and hence improves the transient stability to a great extend.},
     year = {2013}
    }
    

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  • TY  - JOUR
    T1  - Online Adaptive Neurofuzzy Based SVC Control Strategy for Damping Low Frequency Oscillations in Multi-Machine Power System
    AU  - Saima Ali
    AU  - Shahid Qamar
    AU  - Laiq Khan
    AU  - Umer Akram
    Y1  - 2013/08/30
    PY  - 2013
    N1  - https://doi.org/10.11648/j.ijepe.20130204.15
    DO  - 10.11648/j.ijepe.20130204.15
    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  - 172
    EP  - 183
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20130204.15
    AB  - An online auxiliary control was designed for Static Var Compensator (SVC) to improve the poorly damped oscillations in multi-machine power system subjected to small and large disturbances. This paper presents auxiliary control based on Adaptive NeuroFuzzy (ANF) control using triangular membership function. Such a model free based control does not require any prior information about the system and is robust to system changes quickly. To minimize the cost function and to tune the parameters of the antecedent and consequent part of the proposed control, a Gradient Descent (GD) learning algorithm is used. The time domain simulation results were carried out for two machine test system for four different cases. In order to exploit the performance and robustness of ANF control, the results were compared with conventional PI and no control. Simulation results and performance indices reveal that the proposed control outperforms during various fault conditions and hence improves the transient stability to a great extend.
    VL  - 2
    IS  - 4
    ER  - 

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Author Information
  • Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan

  • Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan

  • Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan

  • Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan

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