Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls

Nagu Bhookya; RamanaRao P. V; Sydulu Maheshwarapu

doi:doi:10.11648/j.epes.20130204.11

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Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls

Nagu Bhookya, RamanaRao P. V, Sydulu Maheshwarapu

Published in American Journal of Electrical Power and Energy Systems (Volume 2, Issue 4)

Received: 14 May 2013 Published: 10 July 2013

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Abstract

This paper presents an affective neuro – fuzzy controller (NFC) to improve the transient stability of multi-machine system with HVDC link. Fuzzy rules are used as neurons in artificial neural network (ANN) model. Excellent learning capability of ANN and heuristic fuzzy rules and input/output membership functions of fuzzy logic technique are optimally tuned from training examples by back propagation algorithm (BPA). Considerable time required for fuzzy inference system to match rules is saved using NFC. To illustrate the performance of NFC, transient stability study is carried out on a multi machine system and results are compared with conventional controller as well as fuzzy logic controller.

Published in	American Journal of Electrical Power and Energy Systems (Volume 2, Issue 4)
DOI	10.11648/j.epes.20130204.11
Page(s)	98-105
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

Neuro – Fuzzy Controller, Artificial Neural Network, Transient Stability, Back Propagation Algorithm

References

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[3]	C.S. Rao and T. K. Nag Sarkar, "Half Wave Thyristor Controlled Dynamic Brake to Improve Transient Stability," IEEE Trans., vol. PAS-103, no. 5, pp. 1077-1083, May 1984.
[4]	A. Ekstrom and G. Liss, "A refined HVDC control system," IEEE Trans. Power Apparatus and Systems, vol. PAS-89, no. 536, May/June 1970.
[5]	P. Kundur, Power System Stability and Control McGraw- Hill, Inc., 1994
[6]	Garng M. Huang, VikramKrishnaswamy, "HVDC Controls for Power System Stability", IEEE Power Engineering Society, pp 597- 602, 2002.
[7]	V. K. Sood, N. Kandil, R. V. Patel, K. Khorasani, "Comparative Evaluation of Neural-Network-Based and PI Current Controllers for HVDC Transmission", IEEE Transactions on Power Electronics, VOL.9, NO.3, May1994.
[8]	T. Smed, G. Anderson, "A New Approach to AC/DC Power Flow", IEEE Trans. on Power Systems., Vol. 6, No. 3, pp 1238- 1244, Aug. 1991.
[9]	K. R. Padiyar,HVDC Power Transmission Systems New Age International (P) Ltd., 2004.
[10]	Jos Arrillaga and Bruce Smith, "AC- DC Power System Analysis", The Institution of Electrical Engineers, 1998.
[11]	M. Sugeno and K. Murakami, "Fuzzy Parking control of Model Car,"in the 13rd IEEE Conf. on Decision and Control, Las Vegas, 1984.
[12]	R. Tauscheit and E. M. Scharf, "Experiments with the Use of a Rule-Based Self-organizing Controller for Robotics Applications," FuzzySets and System, vol. 26, pp. 195-214, 1988.
[13]	P.M.Anderson and A.A.Fouad, Power System Control and Stability1sted.,Iowa State University Press, 1977.
[14]	Stagg and El- Abiad, Computer Methods in Power System Analysis International Student Edition, McGraw- Hill, Book Company, 1968
[15]	C. T. Lin and C. S. George Lee, "Neural-Network Based Fuzzy LogicControl and Decision System," IEEE Trans. on Computers, vol. 40,no.12, pp. 1320-1336, Dec. 1991.

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APA Style

Nagu Bhookya, RamanaRao P. V, Sydulu Maheshwarapu. (2013). Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. American Journal of Electrical Power and Energy Systems, 2(4), 98-105. https://doi.org/10.11648/j.epes.20130204.11

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ACS Style

Nagu Bhookya; RamanaRao P. V; Sydulu Maheshwarapu. Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. Am. J. Electr. Power Energy Syst. 2013, 2(4), 98-105. doi: 10.11648/j.epes.20130204.11

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AMA Style

Nagu Bhookya, RamanaRao P. V, Sydulu Maheshwarapu. Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. Am J Electr Power Energy Syst. 2013;2(4):98-105. doi: 10.11648/j.epes.20130204.11

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@article{10.11648/j.epes.20130204.11,
  author = {Nagu Bhookya and RamanaRao P. V and Sydulu Maheshwarapu},
  title = {Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {2},
  number = {4},
  pages = {98-105},
  doi = {10.11648/j.epes.20130204.11},
  url = {https://doi.org/10.11648/j.epes.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20130204.11},
  abstract = {This paper presents an affective neuro – fuzzy controller (NFC) to improve the transient stability of multi-machine system with HVDC link. Fuzzy rules are used as neurons in artificial neural network (ANN) model. Excellent learning capability of ANN and heuristic fuzzy rules and input/output membership functions of fuzzy logic technique are optimally tuned from training examples by back propagation algorithm (BPA). Considerable time required for fuzzy inference system to match rules is saved using NFC. To illustrate the performance of NFC, transient stability study is carried out on a multi machine system and results are compared with conventional controller as well as fuzzy logic controller.},
 year = {2013}
}

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TY  - JOUR
T1  - Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls
AU  - Nagu Bhookya
AU  - RamanaRao P. V
AU  - Sydulu Maheshwarapu
Y1  - 2013/07/10
PY  - 2013
N1  - https://doi.org/10.11648/j.epes.20130204.11
DO  - 10.11648/j.epes.20130204.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 98
EP  - 105
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20130204.11
AB  - This paper presents an affective neuro – fuzzy controller (NFC) to improve the transient stability of multi-machine system with HVDC link. Fuzzy rules are used as neurons in artificial neural network (ANN) model. Excellent learning capability of ANN and heuristic fuzzy rules and input/output membership functions of fuzzy logic technique are optimally tuned from training examples by back propagation algorithm (BPA). Considerable time required for fuzzy inference system to match rules is saved using NFC. To illustrate the performance of NFC, transient stability study is carried out on a multi machine system and results are compared with conventional controller as well as fuzzy logic controller.
VL  - 2
IS  - 4
ER  -

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Author Information

Nagu Bhookya

Department of Electrical and Electronics Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
RamanaRao P. V

Department of Electrical and Electronics Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
Sydulu Maheshwarapu

Department of Electrical and Electronics Engineering, National Institute of Technology Warangal, Andhra Pradesh, India

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APA Style

Nagu Bhookya, RamanaRao P. V, Sydulu Maheshwarapu. (2013). Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. American Journal of Electrical Power and Energy Systems, 2(4), 98-105. https://doi.org/10.11648/j.epes.20130204.11

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ACS Style

Nagu Bhookya; RamanaRao P. V; Sydulu Maheshwarapu. Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. Am. J. Electr. Power Energy Syst. 2013, 2(4), 98-105. doi: 10.11648/j.epes.20130204.11

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AMA Style

Nagu Bhookya, RamanaRao P. V, Sydulu Maheshwarapu. Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls. Am J Electr Power Energy Syst. 2013;2(4):98-105. doi: 10.11648/j.epes.20130204.11

Copy | Download

@article{10.11648/j.epes.20130204.11,
  author = {Nagu Bhookya and RamanaRao P. V and Sydulu Maheshwarapu},
  title = {Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {2},
  number = {4},
  pages = {98-105},
  doi = {10.11648/j.epes.20130204.11},
  url = {https://doi.org/10.11648/j.epes.20130204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20130204.11},
  abstract = {This paper presents an affective neuro – fuzzy controller (NFC) to improve the transient stability of multi-machine system with HVDC link. Fuzzy rules are used as neurons in artificial neural network (ANN) model. Excellent learning capability of ANN and heuristic fuzzy rules and input/output membership functions of fuzzy logic technique are optimally tuned from training examples by back propagation algorithm (BPA). Considerable time required for fuzzy inference system to match rules is saved using NFC. To illustrate the performance of NFC, transient stability study is carried out on a multi machine system and results are compared with conventional controller as well as fuzzy logic controller.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Enhancement of Power System Stability Using Self-Organized Neuro–Fuzzy Based HVDC Controls
AU  - Nagu Bhookya
AU  - RamanaRao P. V
AU  - Sydulu Maheshwarapu
Y1  - 2013/07/10
PY  - 2013
N1  - https://doi.org/10.11648/j.epes.20130204.11
DO  - 10.11648/j.epes.20130204.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 98
EP  - 105
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20130204.11
AB  - This paper presents an affective neuro – fuzzy controller (NFC) to improve the transient stability of multi-machine system with HVDC link. Fuzzy rules are used as neurons in artificial neural network (ANN) model. Excellent learning capability of ANN and heuristic fuzzy rules and input/output membership functions of fuzzy logic technique are optimally tuned from training examples by back propagation algorithm (BPA). Considerable time required for fuzzy inference system to match rules is saved using NFC. To illustrate the performance of NFC, transient stability study is carried out on a multi machine system and results are compared with conventional controller as well as fuzzy logic controller.
VL  - 2
IS  - 4
ER  -

Copy | Download