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

Saima Ali; Shahid Qamar; Laiq Khan; Umer Akram

doi:doi:10.11648/j.ijepe.20130204.15

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

Saima Ali, Shahid Qamar, Laiq Khan, Umer Akram

Published in International Journal of Energy and Power Engineering (Volume 2, Issue 4)

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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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

Saima Ali

Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan
Shahid Qamar

Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan
Laiq Khan

Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan
Umer Akram

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download