Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems

Phan Xuan Le; Nguyen Le Thai; Nguyen Le Minh Tri

doi:doi:10.11648/j.epes.20140306.11

| Peer-Reviewed

Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems

Phan Xuan Le, Nguyen Le Thai, Nguyen Le Minh Tri

Published in American Journal of Electrical Power and Energy Systems (Volume 3, Issue 6)

Received: 23 October 2014 Accepted: 4 November 2014 Published: 10 November 2014

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The performance of the Automatic Voltage Regulate (AVR) and the Power System Stability (PSS) methods may be degraded stability of the power system. This paper presents an Adaptive Neural Fuzzy Inference Systems (ANFIS) algorithm for stability of the power system, we use an Adaptive Network based Fuzzy Interference System architecture extended to response with multivariable systems. By using a hybrid learning method, the suggested ANFIS can setting structure diagram input - output based on both human knowledge and stipulated input-output data pairs. Simulation results present the convergence of the algorithm is improved.

Published in	American Journal of Electrical Power and Energy Systems (Volume 3, Issue 6)
DOI	10.11648/j.epes.20140306.11
Page(s)	101-106
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

AVR, PSS, ANFIS

References

[1]	Balwinder Singh Surjan and Ruchira Garg, “Power System Stabilizer Controller Design for SMIB Stability Study,” ISSN: 2249 – 8958, Volume-2, Issue-1, pp. 209–214, 2012.
[2]	P.demello and Charles concordia, “Concept of Synchronous Machine Stability as Affected by Excitation Control,” IEEE transactions on power apparatus and systems, vol. pas-88,no. 4, pp. 316–329, 1969.
[3]	S.E.M. de Oliveira, “Effect of Excitation Systems and of Power Systems Stabilizers on Synchronous Generator Damping and Synchronizing Torque,” IEE Proceedings, Vol 136, Pt.C, No5, pp. 264–270, September 1988.
[4]	R. Sivakumar, C. Sahana, P. A. Savitha, “Design of ANFIS based Estimation and Control for MIMO Systems,” ISSN: 2248-9622, Vol. 2, Issue 3, pp. 2803–2809, May-Jun 2012.
[5]	Jyh-Shing Roger Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3, pp. 665–685, may/june 1993.
[6]	T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE transactions on systems, man, and cybernetics, Vol. SMC-15, No.1, pp. 116–132, January/February, 1985.
[7]	Carlos Ernesto Ugalde Loo, Luigi Vanfretti, Eduardo Liceaga-Castro, Enrique Acha, “Synchronous Generators Modeling and Control Using the Framework of Individual Channel Analysis and Design Part 1,” International Journal of Emerging Electric Power Systems, Volume 8, Iss 5, Art 4, pp. 1–26, 2007.
[8]	A. Abraham, "Adaptation of Fuzzy Inference System Using Neural Learning,” Springer-Verlag Berlin Heidelberg, 2005.
[9]	P.Kundur, “Power System Stability and control,” Vice-President, Power Engineering. Powertech Labs Inc., Surrey, British Columbia, 1993.
[10]	J-s-r.Jang, c-t.sun, e.mizutani, “Neuro-fuzzy anh soft computing,” Prentice Hall Upper Saddle river, NJ 07458, 1997
[11]	IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Std 421.5TM-2005.
[12]	Chee-mun Ong, “Dynamic Simulation of Electric Machinery using Matlab Simulink,” Prentice Hall PTR, 1998.
[13]	Sauer Peter W. and Pai M. A, “Power System Dynamics and Stability,“ Pretice Hall, 1998.
[14]	Sichuan Dongfeng Electric Machinery Works Co., Ltd, “Hydrogenerator Product Instructions, Product type: SF32.3-16/4500,” 2008.

Cite This Article

Plain Text BibTeX RIS

APA Style

Phan Xuan Le, Nguyen Le Thai, Nguyen Le Minh Tri. (2014). Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. American Journal of Electrical Power and Energy Systems, 3(6), 101-106. https://doi.org/10.11648/j.epes.20140306.11

Copy | Download

ACS Style

Phan Xuan Le; Nguyen Le Thai; Nguyen Le Minh Tri. Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. Am. J. Electr. Power Energy Syst. 2014, 3(6), 101-106. doi: 10.11648/j.epes.20140306.11

Copy | Download

AMA Style

Phan Xuan Le, Nguyen Le Thai, Nguyen Le Minh Tri. Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. Am J Electr Power Energy Syst. 2014;3(6):101-106. doi: 10.11648/j.epes.20140306.11

Copy | Download

@article{10.11648/j.epes.20140306.11,
  author = {Phan Xuan Le and Nguyen Le Thai and Nguyen Le Minh Tri},
  title = {Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {3},
  number = {6},
  pages = {101-106},
  doi = {10.11648/j.epes.20140306.11},
  url = {https://doi.org/10.11648/j.epes.20140306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20140306.11},
  abstract = {The performance of the Automatic Voltage Regulate (AVR) and the Power System Stability (PSS) methods may be degraded stability of the power system. This paper presents an Adaptive Neural Fuzzy Inference Systems (ANFIS) algorithm for stability of the power system, we use an Adaptive Network based Fuzzy Interference System architecture extended to response with multivariable systems. By using a hybrid learning method, the suggested ANFIS can setting structure diagram input - output based on both human knowledge and stipulated input-output data pairs. Simulation results present the convergence of the algorithm is improved.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems
AU  - Phan Xuan Le
AU  - Nguyen Le Thai
AU  - Nguyen Le Minh Tri
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.epes.20140306.11
DO  - 10.11648/j.epes.20140306.11
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  - 101
EP  - 106
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20140306.11
AB  - The performance of the Automatic Voltage Regulate (AVR) and the Power System Stability (PSS) methods may be degraded stability of the power system. This paper presents an Adaptive Neural Fuzzy Inference Systems (ANFIS) algorithm for stability of the power system, we use an Adaptive Network based Fuzzy Interference System architecture extended to response with multivariable systems. By using a hybrid learning method, the suggested ANFIS can setting structure diagram input - output based on both human knowledge and stipulated input-output data pairs. Simulation results present the convergence of the algorithm is improved.
VL  - 3
IS  - 6
ER  -

Copy | Download

Author Information

Phan Xuan Le

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China; Faculty of Electric and Electronic Engineering, Tuy Hoa Industrial College, Tuy Hoa City, Phu Yen Province, Vietnam
Nguyen Le Thai

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China; Faculty of Electric and Electronic Engineering, Tuy Hoa Industrial College, Tuy Hoa City, Phu Yen Province, Vietnam
Nguyen Le Minh Tri

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China; Faculty of Electrical Engineering, Kien Giang Technology and Economics College, Kien Giang Province, Vietnam

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Phan Xuan Le, Nguyen Le Thai, Nguyen Le Minh Tri. (2014). Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. American Journal of Electrical Power and Energy Systems, 3(6), 101-106. https://doi.org/10.11648/j.epes.20140306.11

Copy | Download

ACS Style

Phan Xuan Le; Nguyen Le Thai; Nguyen Le Minh Tri. Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. Am. J. Electr. Power Energy Syst. 2014, 3(6), 101-106. doi: 10.11648/j.epes.20140306.11

Copy | Download

AMA Style

Phan Xuan Le, Nguyen Le Thai, Nguyen Le Minh Tri. Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems. Am J Electr Power Energy Syst. 2014;3(6):101-106. doi: 10.11648/j.epes.20140306.11

Copy | Download

@article{10.11648/j.epes.20140306.11,
  author = {Phan Xuan Le and Nguyen Le Thai and Nguyen Le Minh Tri},
  title = {Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {3},
  number = {6},
  pages = {101-106},
  doi = {10.11648/j.epes.20140306.11},
  url = {https://doi.org/10.11648/j.epes.20140306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20140306.11},
  abstract = {The performance of the Automatic Voltage Regulate (AVR) and the Power System Stability (PSS) methods may be degraded stability of the power system. This paper presents an Adaptive Neural Fuzzy Inference Systems (ANFIS) algorithm for stability of the power system, we use an Adaptive Network based Fuzzy Interference System architecture extended to response with multivariable systems. By using a hybrid learning method, the suggested ANFIS can setting structure diagram input - output based on both human knowledge and stipulated input-output data pairs. Simulation results present the convergence of the algorithm is improved.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Research Methods for Power System Stability Using Adaptive Neural Fuzzy Inference Systems
AU  - Phan Xuan Le
AU  - Nguyen Le Thai
AU  - Nguyen Le Minh Tri
Y1  - 2014/11/10
PY  - 2014
N1  - https://doi.org/10.11648/j.epes.20140306.11
DO  - 10.11648/j.epes.20140306.11
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  - 101
EP  - 106
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20140306.11
AB  - The performance of the Automatic Voltage Regulate (AVR) and the Power System Stability (PSS) methods may be degraded stability of the power system. This paper presents an Adaptive Neural Fuzzy Inference Systems (ANFIS) algorithm for stability of the power system, we use an Adaptive Network based Fuzzy Interference System architecture extended to response with multivariable systems. By using a hybrid learning method, the suggested ANFIS can setting structure diagram input - output based on both human knowledge and stipulated input-output data pairs. Simulation results present the convergence of the algorithm is improved.
VL  - 3
IS  - 6
ER  -

Copy | Download