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

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Self-Adaptive Fuzzy C-means based Radial Basis Function Network to Solve Economic Load Dispatch Problems

by Surekha P, Dr.S.Sumathi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 4
Year of Publication: 2011
Authors: Surekha P, Dr.S.Sumathi
10.5120/3016-4076

Surekha P, Dr.S.Sumathi . A Self-Adaptive Fuzzy C-means based Radial Basis Function Network to Solve Economic Load Dispatch Problems. International Journal of Computer Applications. 25, 4 ( July 2011), 50-59. DOI=10.5120/3016-4076

@article{ 10.5120/3016-4076,
author = { Surekha P, Dr.S.Sumathi },
title = { A Self-Adaptive Fuzzy C-means based Radial Basis Function Network to Solve Economic Load Dispatch Problems },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 4 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 50-59 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number4/3016-4076/ },
doi = { 10.5120/3016-4076 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:55.513585+05:30
%A Surekha P
%A Dr.S.Sumathi
%T A Self-Adaptive Fuzzy C-means based Radial Basis Function Network to Solve Economic Load Dispatch Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 4
%P 50-59
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent decades, with a large increase in power demand, fuel cost, and limited fuel supply it has become very essential to run the power systems with minimum cost so that the committed units serve the expected load demand. The basic objective of Economic Load Dispatch (ELD) is to distribute the total generation among the generation units in operation, in order to meet the load demand at minimum operating cost while satisfying the system equality and inequality constraints. Nature inspired computing techniques like Artificial Neural Networks (ANN) are preferred for solving ELD problems because they do not impose any restrictions on the shape of the fuel cost curve and are capable of providing good solution quality, and higher precision solutions very close to the global optimum. In this paper, the application of Fuzzy c-means based Radial Basis Function Network (RBFN) to ELD is proposed in order to minimize the error function through a self adaptive process until the error is less than a given tolerance leading to a best solution. The applicability and viability for practical applications has been tested on two different power systems, viz., a IEEE 30 bus 6 unit test system and a 20 unit test system and the experiments were carried out on MATLAB R2008b software. Comparison of the results with the conventional Lambda Iteration method demonstrates the effectiveness of RBFN in solving ELD problems based on fuel cost, power loss, total generated power, algorithmic efficiency, and computational time.

References
  1. Ching-Tzong Su, Chien-Tung Lin. "New Approach with a Hopfield Modeling Framework to Economic Dispatch." IEEE Transactions on Power Systems vol. 15, no. 2 (May 2000): 541 - 545.
  2. Aniruddha Bhattacharya, P.K. Chattopadhyay. "Solving complex economic load dispatch problems using biogeography-based optimization." Expert Systems with Applications vol.37 (2010): 3605-3615.
  3. Aravindhababu P., Nayar K.R. "Economic dispatch based on optimal lambda using radial basis function network." Journal on Electrical Power and Energy systems vol. 24 (August 2001): 551-556.
  4. C.L., Wadhwa. Electrical Power Systems. New Delhi: New Age International (p) Limited Publishers, 2000.
  5. Chao-Ming Huang, Fu-Lu Wang. "An RBF Network With OLS and EPSO Algorithms for Real-Time Power Dispatch." IEEE Trans. Power Systems vol. 22, no. 1 (February 2007): 96-104.
  6. Chih-Cheng Hung, Youngsup Kim, Coleman, T.L. "A comparative study of radial basis function neural networks and wavelet neural networks in classification of remotely sensed data." IEEE Proc. of 5th Biannual World Automation Congress. IEEE, 2002. 455 - 461.
  7. Djukanovic.M., Calovic.M.,Milosevic.B.,Sobajic.DJ. "Neural-net based real time economic dispatch for therma power plants." IEEE Trans. Energy Conversion vol. 11, no. 44 (1996): 755-762.
  8. Glover, Z. X. Liang and J. D. "A zoom feature for a dynamic programming solution to economic dispatch including transmission losses." IEEE Trans. on Power Systems vol. 7, no. 2 (May 1992): 544-550.
  9. H. T. Yang, P. C. Yang and C. L. Huang. "Evolutionary Programming Based Economic Dispatch For Units With Non-smooth Fuel Cost Functions." IEEE Transactions on Power Systems vol. 11, no. 1 (1996): 112-118.
  10. J. B. Park, K. S. Lee, J. R. Shin and K. Y. Lee. "A particle swarm optimization for economic dispatch with non smooth cost functions." IEEE Trans. on Power Systems vol. 8, no. 3 (August 1993): 1325-1332.
  11. J. Yuryevich, K. P. Wong. "Evolutionary Programming Based Optimal Power Flow Algorithm." IEEE Transaction on power systems vol. 14, no. 4 (November 1999): 1245 - 1250 .
  12. Ke Meng, Zhao Yang Dong, Dian Hui Wang, Kit Po Wong. "A Self-Adaptive RBF Neural Network Classifier for Transformer Fault Analysis." IEEE Trans. Power Systems vol. 25, no. 3 (August 2010): 1350-1360.
  13. Lakshmi Devi A., Vamsi Krishna O. "Combined economic and emission dispatch using Evolutionary algorithms-a case study." ARPN Journal of Engineering and Applied Sciences 3, no. 6 (December 2008): 28-35.
  14. Lowery, P. G. "Generation unit commitment by dynamic programming." IEEE Trans. Power App.Syst., vol. PAS-102 (1983): 1218–1225.
  15. Mary, N. Thenmozhi and D. "Economic emission load dispatch using hybrid Genetic Algorithm." Chiang Mai, Thailand. 2004. 476-479.
  16. Matuda.S., Akimoto.Y.,. "The representatio of large numbers in neural networks and its applications to economic load dispatching of electric power." ICNN. 1989. 587-592.
  17. Powell.M.J.D. "Restart Procedures for the conjugate gradient method." Mathematical Programming vol. 12 (1977): 241-254.
  18. Rayapudi, S. Rao. "An Intelligent Water Drop Algorithm for Solving Economic Load Dispatch Problem." International Journal of Electrical and Electronics Engineering vol. 5, no. 1 (2011): 43-49.
  19. Roa-Sepulveda C.A., Herrera M., Pavez-Lazo B., Knight U.G., Coonick A.H. "Economic dispatch using fuzzy decision trees." Electric Power Systems Research vol.66, no. 2 (August 2003): 115-122.
  20. Rollet, R., G. B. Benie, W. Li, and S. Wang. "Image classification algorithm based on the RBF neural network and K-means." International Journal of Remote Sensing vol. 19, no. 15 (1998): 3003-3009.
  21. Sailaja Kumari M., Sydulu M. "A Fast Computational Genetic Algorithm for Economic Load Dispatch." International Journal of Recent Trends in Engineering vol. 1, no. 1 (May 2009): 349-356.
  22. Singh. G., Srivastava.S.C.,Kalra.P.K.,Vinod Kumar.D.M. "Fast approach to artificial neural network training and its application to economic load dispatch." Electrical Machines and Power Systems, 1995: 13-24.
  23. Sivanandam S. N., Sumathi S., Deepa S.N. Introduction to Neural networks using MATLAB 6.0. New Delhi: Tata McGraw-Hill Publishing Company Limited, 2006.
  24. T. Jayabarathi, G. Sadasivam and V. Ramachandran. "Evolutionary programming based economic dispatch of generators with prohibited operating zones." Electric Power Systems Research vol. 52, no. 3 (1999): 261-266.
  25. W.-M. Lin, F.-S. Cheng, and M.-T. Tsay. "An improved tabu search for economic dispatch with multiple minima." IEEE Trans. Power Syst vol. 17 (February 2002 ): 108 - 112.
  26. Wilson, J. A. Muckstadt and R. C. "An application of mixed-integer programming duality to scheduling thermal generating systems." IEEE Trans. Power App. Syst vol. PAS-87, no. 12 (1968): 1968-1978.
  27. Wong, K. P. Wong and Y. W. "Genetic and genetic/simulated-annealing approaches to economic dispatch." Proc. Inst. Elect. Eng. Gen. Trans. Distrib. vol. 141 (September 1994): 507-513.
  28. Wood J., Wollenberg B. F. Power generation operation and control. John Wiley & Sons, 1984.
  29. Y. Labbi, D. Ben Attous. "A hybrid GA–PS method to solve the economic load dispatch problem" Journal of Theoretical and Applied Information Technology vol.15, no. 1 (2010): 61-68.
  30. Yoshimura, A. I. Cohen and M. "A branch-and-bound algorithm for unit commitment." IEEE Trans. Power App. Syst. vol. PAS-102, no. 2 (1983): 444–451.
Index Terms

Computer Science
Information Sciences

Keywords

Economic load dispatch Fuzzy c-means clustering Radial Basis Function Network algorithmic efficiency computational time APAE

Powered by PhDFocusTM