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An Improved Particle Swarm Optimization for Economic Dispatch with Non-Smooth Cost Functions

G.Sreenivasan*, Ch. SaiBabu**, N. Narasimhulu***

* Intell Engineering College, Anantapur, A.P., India.

**,*** JNT University, Anantapur, A.P, India.

**** SKD Engineering College, Gooty, A.P., India.

Periodicity:January - March'2009
DOI : https://doi.org/10.26634/jee.2.3.254

Abstract

Particle swarm optimization has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic dispatch (ED) with non smooth cost functions. To show improved particle swarm optimization (IPSO) in efficiency and effectiveness, the proposed IPSO is applied to test the economic dispatch problems, with non-smooth cost functions considering valve-point effects and multi-fuel problems. The results of the IPSO are compared with the results of conventional numerical methods, evolutionary programming approaches and genetic algorithm. Finally the convergence characteristics of improved particle swarm optimization (IPSO) are compared with modified particle swarm optimization results (MPSO).

Keywords

Constrained optimization, economic dispatch (ED), non-smooth optimization, particle swarm optimization (PSO), improved particle swarm optimization (IPSO) and modified particle swarm optimization (MPSO).

How to Cite this Article?

G. Sreenivasan, S. Sivanagaraju, Saibabu and N. Narasimhulu (2009). An Improved Particle Swarm Optimization For Economic Dispatch With Non-Smooth Cost Functions. i-manager’s Journal on Electrical Engineering, 2(3), Jan-Mar 2009, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 18-25. https://doi.org/10.26634/jee.2.3.254

References

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[3] H. T. Yang, P. C. Yang, and C. L. Huang, “Evolutionary programming based economic dispatch for units with nonsmooth fuel cost functions,”IEEE Trans. Power Syst., Vol. 11, No. 1, pp. 112-118, Feb. 1996.

[4] D. C.Walters and G. B. Sheble, “Genetic algorithm solution of economic dispatch with the valve point loading,” IEEE Trans. Power Systems, Vol.8, pp. 1325-1332, Aug. 1993.

[5] K. Y. Lee, A. Sode-Yome, J. H. Park, “Adaptive Hopfield neural network for economic load dispatch,” IEEE Trans. Power Syst., Vol. 13,pp. 519-526, May 1998.

[6] Nidul Sinha, R. Chakrabarti, P. K. Chattopadhyay,” Evolutionary Programming Techniques For Economic Load Dispatch” IEEE Trans. on evolutionary computation, Vol.7, No. 1, Feb 2003.

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