CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

GASA Tuned Optimal Fuzzy Regulator for AGC of an Interconnected Power System

by Ibraheem, Naimul Hasan, Omveer Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 8
Year of Publication: 2011
Authors: Ibraheem, Naimul Hasan, Omveer Singh
10.5120/2451-2633

Ibraheem, Naimul Hasan, Omveer Singh . GASA Tuned Optimal Fuzzy Regulator for AGC of an Interconnected Power System. International Journal of Computer Applications. 20, 8 ( April 2011), 43-48. DOI=10.5120/2451-2633

@article{ 10.5120/2451-2633,
author = { Ibraheem, Naimul Hasan, Omveer Singh },
title = { GASA Tuned Optimal Fuzzy Regulator for AGC of an Interconnected Power System },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 8 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number8/2451-2633/ },
doi = { 10.5120/2451-2633 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:18.794061+05:30
%A Ibraheem
%A Naimul Hasan
%A Omveer Singh
%T GASA Tuned Optimal Fuzzy Regulator for AGC of an Interconnected Power System
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 8
%P 43-48
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An optimal fuzzy AGC regulator design based on GASA tuning technique for interconnected power system is proposed in this paper. The investigations with the GASA tuned optimal fuzzy regulator is carried out on a two area interconnected power system consisting of power plants with different characteristics. Power system area-1 is having plant with reheat thermal turbine whereas area-2 has plant with hydro turbine. The dynamic response plots are obtained for 1% load disturbance in hydro area. The dynamic response plots achieved are compared with Conventional integral regulator and Fuzzy logic regulator. Simulation results demonstrate that GASA tuned optimal fuzzy AGC regulator is appreciably better than the other regulators.

References
  1. Elgerd, O. I. 1982. Electric energy system theory: an introduction.Tata Mc-Graw Hill, 1982.
  2. Nanda, J. and Kaul, B. L. 1978. Automatic generation control of an interconnected power system. IEE Proc. Generation, Transmission and Distribution, vol. 125, no 5, pp. p 384-390, May 1978.
  3. Ibraheem and Kumar, P. 2003. Dynamic performance enhancement of hydro power systems with asynchronous tie-lines. J. Electric Power Components and Systems, vol. 31, no. 7, pp. 605–626, 2003.
  4. Moon, Young-Hyum and Ryu, Heon-Su. 2001. Fuzzy logic based extended integral control for load frequency control. IEEE Power Engineering Society Winter Meeting, vol. 3, pp. 1289-1293, Feb. 2001.
  5. Chang, C.S. and Fu, W. 1997. Area load frequency control using fuzzy gain scheduling of PI controllers. Electric Power System Research, vol. 42, pp. 145-152, 1997.
  6. Rerkeedapong, D. and Felachi, A. 2002. Fuzzy rule based load frequency control. IEEE Journal of Power Engineering Society Winter Meeting, vol. 3, no. , pp. 1154-1159, July 2002.
  7. Aditya, 2003. Design of load frequency controllers using genetic algorithm for two area interconnected hydro power system. Electric Power Components and Systems, 31:1, 81-94, 2003.
  8. Golpira, H., et al. 2011. Application of GA optimization for automatic generation control design in an interconnected power system. Energy Conversion and Management 52 (2011) 2247–2255.
  9. Patel, R. N., 2007. Application of artificial intelligence for tuning the parameters of an AGC. International Journal of Mathematical, Physical and Engineering Sciences 1;1 2007.
  10. Bensenouci, Ahmed and Ghany. 2007. Step-wise optimum adaptive variable-structure automatic generation control design using simulated annealing”, IEEE Conference, 2007.
  11. Ibraheem, et al. 2005. Recent philosophies of automatic generation control strategies in power systems. IEEE Transaction Power System, vol. 11, no. 3, pp. 346-357, Feb. 2005.
  12. Das, D. B. and Patvardhan, C. 2003. Useful multi-objective hybrid evolutionary approach to optimal power flow. IEE Proceedings-Generation, Transmission and Distribution, Vol. 150, No.3, 275-282, May 2003.
  13. Li, Pingkang and Du, Xiuxix. 2009. Multi-area AGC system performance improvement using GA based fuzzy logic control. International Conference on Electrical Engineering, 2009.
  14. Juang, Chia-Feng and Lu, Chun-Feng. 2005. Power System Load Frequency Control by Genetic Fuzzy Gain Scheduling Regulator. Journal of the Chinese Institute of Engineers, Vol. 28, No. 6, pp. 1013-1018, 2005.
  15. Ghoshal, S. P., 2004. Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system. Electric Power Systems Research Journal, 70, 115–127, 2004.
  16. Ghoshal, S. P. and Roy, N. K. 2004. A novel approach for optimization of proportional integral derivative gains in automatic generation control. Australasian Universities Power Engineering Conference (AUPEC 2004). 26-29 September 2004, Brisbane, Australia.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic generation control (AGC) Fuzzy logic regulator (FLR) Area control error (ACE) Genetic Algorithm-Simulated Annealing (GASA) interconnected power system