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Article:Real Time Economic and Emission Dispatch using RBF Network with OLS and MPSO Algorithms

by M.Kondalu, G. Sreekanth reddy, Dr. J. Amarnath
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 7
Year of Publication: 2010
Authors: M.Kondalu, G. Sreekanth reddy, Dr. J. Amarnath
10.5120/1690-2117

M.Kondalu, G. Sreekanth reddy, Dr. J. Amarnath . Article:Real Time Economic and Emission Dispatch using RBF Network with OLS and MPSO Algorithms. International Journal of Computer Applications. 12, 7 ( December 2010), 26-31. DOI=10.5120/1690-2117

@article{ 10.5120/1690-2117,
author = { M.Kondalu, G. Sreekanth reddy, Dr. J. Amarnath },
title = { Article:Real Time Economic and Emission Dispatch using RBF Network with OLS and MPSO Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { December 2010 },
volume = { 12 },
number = { 7 },
month = { December },
year = { 2010 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume12/number7/1690-2117/ },
doi = { 10.5120/1690-2117 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:01:02.799390+05:30
%A M.Kondalu
%A G. Sreekanth reddy
%A Dr. J. Amarnath
%T Article:Real Time Economic and Emission Dispatch using RBF Network with OLS and MPSO Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 12
%N 7
%P 26-31
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a new approach to real time economic and emission dispatch by using orthogonal least-squares (OLS) and modified particle swarm optimization (MPSO) algorithms to construct the radial basis function (RBF) network. The objectives considered are fuel cost and NOx/CO2 emissions. The RBF network is composed of input, hidden, and output layers. The OLS algorithm provides a simple and efficient means for fitting radial basis function networks. The MPSO algorithm is implemented to tune the parameters in the network, including the dilation and translation of RBF centers and the weights between the hidden and output layer. The proposed approach has been tested on the IEEE 30-bus six-generator system. Testing results indicate that the proposed approach can make a quick response and yield accurate Real time economic and emission solutions.

References
  1. R. Ramanathan, “Emission constrained economic dispatch,” IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1994–2000, Nov. 1994.
  2. J. H. Talaq, F. El-Hawary, and M. E. El-Hawary, “Minimum emission power flow,” IEEE Trans. Power Syst., vol. 9, no. 1, pp. 429–435, Feb.1994.
  3. J. Nanda, D. P. Kothari, and K. S. Lingamurthy, “Economic emission load dispatch through goal programming technique,” IEEE Trans. Energy Convers., vol. 3, no. 1, pp. 26–32, Mar. 1988.
  4. R. Yokoyama, S. H. Bae, T. Morita, and H. Sasaki, “Multiobjective optimal generation dispatch based on probability security criteria,” IEEE Trans. Power Syst., vol. 3, no. 1, pp. 317–324, Feb. 1988.
  5. P. C. Chen and C. M. Huang, “Bi-objective power dispatch using goal attainment method and adaptive polynomial networks,” IEEE Trans Energy Convers., vol. 19, no. 4, pp. 741–747, Dec. 2004.
  6. J. H. Park, Y. S. Kim, I. K. Eom, and K. Y. Lee, “Economic load dispatch for piecewise quadratic cost function using Hopfield neural network,” IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1030–1038, Aug.1993.
  7. M. Djukanovic, M. Calcvic, B. Milosevic, and D. J. Sobajic, “Neural-net based real-time economic dispatch for thermal power plants,” IEEE Trans. Energy Convers., vol. 11, no. 4, pp. 755–761, Dec. 1996.
  8. P. S. Kulkarni, A. G. Kothari, and D. P. Kothari, “Combined economic and emission dispatch using improved back propagation neural network,” Elect. Mach. Power Syst., vol. 28, no. 1, pp. 31–44, Jan. 2000.
  9. P. K. Hota and S. K. Dash, “Multiobjective generation dispatch through a neuro-fuzzy technique,” Elect. Power Compon. Syst., vol. 32, no. 11, pp. 1191–1206, Nov. 2004.
  10. P. S. Kulkarni, A. G. Kothari, and D. P. Kothari, “Application of radial basis function neural network for economic dispatch,” J. Inst. Eng. (India): Elect. Eng. Div., vol. 83, pp. 81–86, Sep. 2002.
  11. P. Aravindhababu and K. R. Nayar, “Economic dispatch based on optimal lambda using radial basic function network,” Int. J. Elect. Power Energy Syst., vol. 24, no. 7, pp. 551–556, Sep. 2002.
  12. S. Chen, C. F. N. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks,” IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302–309, Mar. 1991.
  13. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, Nov./Dec. 1995, vol. 4, pp. 1942–1948.
  14. A. I. S. Kumar, K. Dhanushkodi, J. J. Kumar, and C. K. C. Paul, “Particle swarm optimization solution to emission and economic dispatch problem,” in Proc. IEEE Int. Conf. TENCON, Oct. 2003, pp. 435–439.
  15. J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, “A particle swarm optimization for economic dispatch with nonsmooth cost functions,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 34–42, Feb. 2005.
  16. D. N. Jeyakumar, T. Jayabarathi, and T. Raghunathan, “Particle swarm optimization for various types of economic dispatch problems,” Int. J. Elect. Power Energy Syst., vol. 28, no. 1, pp. 36–42, Jan. 2006.
  17. S. Naka, T. Genji, T. Yura, and Y. Fukuyama, “A hybrid particle swarm optimization for distribution state estimation,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 60–68, Feb. 2003.
  18. A. A. A. Esmin, G. Lambert-Torres, and A. C. Zambroni de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 859–866, May 2005.
  19. J. S. Heo, K. Y. Lee, and R. Garduno-Ramirez, “Multiobjective control of power plans using particle swarm optimization technique,” IEEE Trans. Energy Convers., vol. 21, no. 2, pp. 552–561, Jun. 2006.
  20. A. J. Wood and B. F. Wollenberg, Power Generation Operation and Control, 2nd ed. New York: Wiley, 1996.
  21. The Math Work Inc., Optimization Toolbox User’s Guide, Dec. 1992.
  22. C. M. Huang, H. T. Yang, Y. Y. Hong, S. P. Hong, and K. P. Liou, “Power dispatching considering fuel cost and CO Emission,” Monthly J. Taipower’s Eng., vol. 610, pp. 32–48, Jun. 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Modified particle swarm optimization orthogonal least-squares radial basis function Real time economic emission dispatch