Evolutionary computation based three-area automatic generation control

doi:10.1016/j.eswa.2010.02.014

Evolutionary computation based three-area automatic generation control

2010-08-01

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DOI：10.1016/j.eswa.2010.02.014

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In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional-integral-derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are genetic algorithm, various types of particle swarm optimization and bacteria foraging optimization. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations, but still MCASO and BFO yield much more global true optimal results. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters. fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses. (C) 2010 Elsevier Ltd. All rights reserved.

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Evolutionary techniques

SFL

Evolutionary computation based three-area automatic generation control

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