Particle swarm optimization with crazy particles for nonconvex economic dispatch

Authors:
Krishna Teerth Chaturvedi;Manjaree Pandit;Laxmi Srivastava
Affiliations:
Department of Electrical Engineering, UIT, Rajiv Gandhi University of Technology, Bhopal, India;Department of Electrical Engineering, M.I.T.S., Gwalior 474 005, M.P., India;Department of Electrical Engineering, M.I.T.S., Gwalior 474 005, M.P., India
Venue:
Applied Soft Computing
Year:
2009

Citing 3
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Abstract

The paper presents an effective evolutionary method for economic power dispatch. The idea is to allocate power demand to the on-line power generators in such a manner that the cost of operation is minimized. Conventional methods assume quadratic or piecewise quadratic cost curves of power generators but modern generating units have non-linearities which make this assumption inaccurate. Evolutionary optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) are free from convexity assumptions and succeed in achieving near global solutions due to their excellent parallel search capability. But these methods usually tend to converge prematurely to a local minimum solution, particularly when the search space is irregular. To tackle this problem ''crazy particles'' are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation. The performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior.